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Data lock will equal a real date.
ie Say 9-1-2017 or whatever.
Then that date will be used as the "last date of Follow-up or Death" and the KM curves and all statistics will use that date to calculate the time factor (ie amount of time from a patients entry into the trial to the particular parameter measured such as progression or death or whatever is defined as an 'event'. Or if the patient has not experienced any event they will have a time factor = to the lock date minus their date of trial entry and randomization.
For patients that have not evented, they will be censored and not used for making the KM curve for any time that is greater than the difference of the lock date minus their particular data entry date or randomization date.
Please see this review and comparison of my PFS model results with the NEJM STUPP data. One can see how revolutionary this treatment will be as the STUPP protocol is arguably still the standard of Care (+/- Optune).
https://www.dropbox.com/s/ywsv4jyv7mkucxs/DCVacL%20compared%20to%20STUPP.JPG?dl=0
Also, note the Stupp gragh gives the number of patients at risk at various time intervals under the gragh so one knows how many patients data are left for the longer time periods out. IE those numbers below equal the total number of patients on the various trial arms minus those who have evented or who were censored.
For you to better understand this I include below the data I used to create the current model comparisons. So, in this case, the Date of entry is the dates given by Bosch at ASCO 2017 for the patient enrollment ramp. The treatment(1) or Placebo (2) was generated by a random number generator in excel in a 2 to 1 ratio for experimental and placebo respectively. Then the Control (0) group was manually given the time in days and the events directly based on the IMUC Control group data pro rata to compensate for the larger NWBO trial.
Then once the number of events were derived in the NWBO control group by this method, the number of events remaining was simply a subtraction of the number of PFS events that NWBO has given us on 2 separate occasions: Once in Dec 2013 66 PFS events for the whole group and another disclosed in Feb. 2017 or before when 248 events were disclosed.
Then the exact same process was used on the experimental patient group in that the events and time were placed in to be equal to the published IMUC control group KM PFS curves ... but here is the cool part ... the number of events was limited to 148 which was derived as noted above and by Dec 2013 only 66 events were placed prior to that date. Therefore there were significantly fewer events in the experimental DCVaxL arm than would have been expected had the experiment arm survived progression free at the same rate as was published by the IMUC control group curves.
Then it was a simple matter of inputting the time factor, the event/censor factor and the group placebo or experimental into the KM statistical software.
And walla the curves appear with the statistical comparison tests.
Derived Data for all 331 patients in the DCVax-L TRIAL
PT # * ENTRY * RX=1 Placebo=0 * DAYS * 1=event 0= censor * DATE EVENT or Censor * MONTHS * Plus 83d Mo
1 * 6/2/2008 * 1 * 53 * 1 * 7/25/2008 * 1.8 * 4.6
2 * 6/13/2008 * 0 * 53 * 1 * 8/5/2008 * 1.8 * 4.6
3 * 6/27/2008 * 1 * 53 * 1 * 8/19/2008 * 1.8 * 4.6
4 * 7/23/2008 * 0 * 53 * 1 * 9/14/2008 * 1.8 * 4.6
5 * 7/10/2008 * 1 * 85 * 1 * 10/3/2008 * 2.8 * 5.6
6 * 8/5/2008 * 0 * 85 * 1 * 10/29/2008 * 2.8 * 5.6
7 * 9/12/2008 * 1 * 85 * 1 * 12/6/2008 * 2.8 * 5.6
8 * 9/26/2008 * 1 * 95 * 1 * 12/30/2008 * 3.2 * 6
9 * 10/9/2008 * 1 * 95 * 1 * 1/12/2009 * 3.2 * 6
10 * 11/4/2008 * 0 * 85 * 1 * 1/28/2009 * 2.8 * 5.6
11 * 11/17/2008 * 0 * 95 * 1 * 2/20/2009 * 3.2 * 6
12 * 12/1/2008 * 0 * 95 * 1 * 3/6/2009 * 3.2 * 6
13 * 12/12/2008 * 0 * 105 * 1 * 3/27/2009 * 3.5 * 6.3
14 * 12/26/2008 * 0 * 105 * 1 * 4/10/2009 * 3.5 * 6.3
15 * 1/8/2009 * 0 * 112 * 1 * 4/30/2009 * 3.7 * 6.5
16 * 2/3/2009 * 1 * 105 * 1 * 5/19/2009 * 3.5 * 6.3
17 * 2/16/2009 * 1 * 105 * 1 * 6/1/2009 * 3.5 * 6.3
18 * 3/2/2009 * 0 * 112 * 1 * 6/22/2009 * 3.7 * 6.5
19 * 3/13/2009 * 0 * 112 * 1 * 7/3/2009 * 3.7 * 6.5
20 * 3/27/2009 * 0 * 112 * 1 * 7/17/2009 * 3.7 * 6.5
21 * 4/9/2009 * 1 * 112 * 1 * 7/30/2009 * 3.7 * 6.5
22 * 10/3/2011 * 0 * 120 * 1 * 1/31/2012 * 4 * 6.8
23 * 10/21/2011 * 0 * 120 * 1 * 2/18/2012 * 4 * 6.8
24 * 11/2/2011 * 0 * 120 * 1 * 3/1/2012 * 4 * 6.8
25 * 11/14/2011 * 1 * 112 * 1 * 3/5/2012 * 3.7 * 6.5
26 * 11/24/2011 * 0 * 120 * 1 * 3/23/2012 * 4 * 6.8
27 * 12/5/2011 * 0 * 120 * 1 * 4/3/2012 * 4 * 6.8
28 * 12/15/2011 * 1 * 112 * 1 * 4/5/2012 * 3.7 * 6.5
29 * 12/26/2011 * 1 * 112 * 1 * 4/16/2012 * 3.7 * 6.5
30 * 1/6/2012 * 0 * 120 * 1 * 5/5/2012 * 4 * 6.8
31 * 2/7/2012 * 0 * 122 * 1 * 6/8/2012 * 4.1 * 6.9
32 * 2/17/2012 * 1 * 120 * 1 * 6/16/2012 * 4 * 6.8
33 * 2/29/2012 * 0 * 122 * 1 * 6/30/2012 * 4.1 * 6.9
34 * 3/11/2012 * 1 * 120 * 1 * 7/9/2012 * 4 * 6.8
35 * 3/21/2012 * 1 * 120 * 1 * 7/19/2012 * 4 * 6.8
36 * 4/1/2012 * 0 * 123 * 1 * 8/2/2012 * 4.1 * 6.9
37 * 5/4/2012 * 0 * 123 * 1 * 9/4/2012 * 4.1 * 6.9
38 * 5/14/2012 * 1 * 120 * 1 * 9/11/2012 * 4 * 6.8
39 * 5/25/2012 * 1 * 120 * 1 * 9/22/2012 * 4 * 6.8
40 * 6/5/2012 * 0 * 126 * 1 * 10/9/2012 * 4.2 * 7
41 * 6/16/2012 * 1 * 120 * 1 * 10/14/2012 * 4 * 6.8
42 * 6/27/2012 * 0 * 126 * 1 * 10/31/2012 * 4.2 * 7
43 * 8/20/2012 * 1 * 122 * 1 * 12/20/2012 * 4.1 * 6.9
44 * 7/29/2012 * 0 * 151 * 1 * 12/27/2012 * 5 * 7.8
45 * 8/9/2012 * 0 * 151 * 1 * 1/7/2013 * 5 * 7.8
46 * 9/10/2012 * 1 * 122 * 1 * 1/10/2013 * 4.1 * 6.9
47 * 9/21/2012 * 1 * 123 * 1 * 1/22/2013 * 4.1 * 6.9
48 * 8/30/2012 * 0 * 158 * 1 * 2/4/2013 * 5.3 * 8.1
49 * 11/3/2012 * 1 * 123 * 1 * 3/6/2013 * 4.1 * 6.9
50 * 11/14/2012 * 1 * 126 * 1 * 3/20/2013 * 4.2 * 7
51 * 10/23/2012 * 0 * 158 * 1 * 3/30/2013 * 5.3 * 8.1
52 * 12/16/2012 * 1 * 126 * 1 * 4/21/2013 * 4.2 * 7
53 * 11/25/2012 * 0 * 158 * 1 * 5/2/2013 * 5.3 * 8.1
54 * 12/6/2012 * 0 * 172 * 1 * 5/27/2013 * 5.7 * 8.5
55 * 1/18/2013 * 1 * 151 * 1 * 6/18/2013 * 5 * 7.8
56 * 1/29/2013 * 1 * 151 * 1 * 6/29/2013 * 5 * 7.8
57 * 2/8/2013 * 1 * 158 * 1 * 7/16/2013 * 5.3 * 8.1
58 * 2/19/2013 * 0 * 172 * 1 * 8/10/2013 * 5.7 * 8.5
59 * 3/2/2013 * 0 * 172 * 1 * 8/21/2013 * 5.7 * 8.5
60 * 4/3/2013 * 1 * 158 * 1 * 9/8/2013 * 5.3 * 8.1
61 * 4/25/2013 * 1 * 158 * 1 * 9/30/2013 * 5.3 * 8.1
62 * 4/14/2013 * 0 * 175 * 1 * 10/6/2013 * 5.8 * 8.6
63 * 5/6/2013 * 0 * 175 * 1 * 10/28/2013 * 5.8 * 8.6
64 * 5/15/2013 * 0 * 182 * 1 * 11/13/2013 * 6.1 * 8.9
65 * 5/30/2013 * 1 * 172 * 1 * 11/18/2013 * 5.7 * 8.5
66 * 5/26/2013 * 0 * 182 * 1 * 11/24/2013 * 6.1 * 8.9
67 * 6/13/2013 * 1 * 172 * 1 * 12/2/2013 * 5.7 * 8.5
68 * 6/17/2013 * 1 * 172 * 1 * 12/6/2013 * 5.7 * 8.5
69 * 6/22/2013 * 1 * 175 * 1 * 12/14/2013 * 5.8 * 8.6
70 * 7/5/2013 * 1 * 175 * 1 * 12/27/2013 * 5.8 * 8.6
71 * 7/14/2013 * 1 * 182 * 1 * 1/12/2014 * 6.1 * 8.9
72 * 7/19/2013 * 1 * 182 * 1 * 1/17/2014 * 6.1 * 8.9
73 * 7/10/2013 * 0 * 200 * 1 * 1/26/2014 * 6.7 * 9.5
74 * 7/23/2013 * 0 * 200 * 1 * 2/8/2014 * 6.7 * 9.5
75 * 8/6/2013 * 1 * 200 * 1 * 2/22/2014 * 6.7 * 9.5
76 * 8/15/2013 * 1 * 200 * 1 * 3/3/2014 * 6.7 * 9.5
77 * 8/10/2013 * 0 * 206 * 1 * 3/4/2014 * 6.9 * 9.7
78 * 8/19/2013 * 1 * 206 * 1 * 3/13/2014 * 6.9 * 9.7
79 * 9/2/2013 * 1 * 206 * 1 * 3/27/2014 * 6.9 * 9.7
80 * 9/7/2013 * 0 * 206 * 1 * 4/1/2014 * 6.9 * 9.7
81 * 9/11/2013 * 1 * 210 * 1 * 4/9/2014 * 7 * 9.8
82 * 9/16/2013 * 1 * 210 * 1 * 4/14/2014 * 7 * 9.8
83 * 9/20/2013 * 0 * 210 * 1 * 4/18/2014 * 7 * 9.8
84 * 10/13/2013 * 0 * 210 * 1 * 5/11/2014 * 7 * 9.8
85 * 10/4/2013 * 1 * 224 * 1 * 5/16/2014 * 7.5 * 10.3
86 * 10/8/2013 * 1 * 224 * 1 * 5/20/2014 * 7.5 * 10.3
87 * 10/17/2013 * 0 * 224 * 1 * 5/29/2014 * 7.5 * 10.3
88 * 10/22/2013 * 0 * 224 * 1 * 6/3/2014 * 7.5 * 10.3
89 * 10/26/2013 * 1 * 260 * 1 * 7/13/2014 * 8.7 * 11.5
90 * 11/9/2013 * 1 * 260 * 1 * 7/27/2014 * 8.7 * 11.5
91 * 11/13/2013 * 1 * 278 * 1 * 8/18/2014 * 9.3 * 12.1
92 * 11/18/2013 * 1 * 278 * 1 * 8/23/2014 * 9.3 * 12.1
93 * 12/15/2013 * 0 * 260 * 1 * 9/1/2014 * 8.7 * 11.5
94 * 12/2/2013 * 1 * 290 * 1 * 9/18/2014 * 9.7 * 12.5
95 * 4/21/2014 * 1 * 151 * 1 * 9/19/2014 * 5 * 7.8
96 * 12/6/2013 * 1 * 290 * 1 * 9/22/2014 * 9.7 * 12.5
97 * 12/11/2013 * 1 * 300 * 1 * 10/7/2014 * 10 * 12.8
98 * 1/20/2014 * 0 * 260 * 1 * 10/7/2014 * 8.7 * 11.5
99 * 5/4/2014 * 1 * 158 * 1 * 10/9/2014 * 5.3 * 8.1
100 * 5/13/2014 * 1 * 158 * 1 * 10/18/2014 * 5.3 * 8.1
101 * 8/31/2014 * 1 * 53 * 1 * 10/23/2014 * 1.8 * 4.6
102 * 9/3/2014 * 1 * 53 * 1 * 10/26/2014 * 1.8 * 4.6
103 * 1/25/2014 * 0 * 278 * 1 * 10/30/2014 * 9.3 * 12.1
104 * 12/29/2013 * 1 * 312 * 1 * 11/6/2014 * 10.4 * 13.2
105 * 5/18/2014 * 1 * 172 * 1 * 11/6/2014 * 5.7 * 8.5
106 * 1/2/2014 * 1 * 315 * 1 * 11/13/2014 * 10.5 * 13.3
107 * 6/1/2014 * 1 * 172 * 1 * 11/20/2014 * 5.7 * 8.5
108 * 1/7/2014 * 1 * 318 * 1 * 11/21/2014 * 10.6 * 13.4
109 * 6/6/2014 * 1 * 175 * 1 * 11/28/2014 * 5.8 * 8.6
110 * 3/2/2014 * 0 * 278 * 1 * 12/5/2014 * 9.3 * 12.1
111 * 9/12/2014 * 1 * 85 * 1 * 12/6/2014 * 2.8 * 5.6
112 * 6/18/2014 * 1 * 182 * 1 * 12/17/2014 * 6.1 * 8.9
113 * 1/29/2014 * 1 * 322 * 1 * 12/17/2014 * 10.7 * 13.5
114 * 2/3/2014 * 1 * 321 * 1 * 12/21/2014 * 10.7 * 13.5
115 * 2/7/2014 * 1 * 323 * 1 * 12/27/2014 * 10.8 * 13.6
116 * 9/24/2014 * 1 * 95 * 1 * 12/28/2014 * 3.2 * 6
117 * 3/16/2014 * 0 * 290 * 1 * 12/31/2014 * 9.7 * 12.5
118 * 3/25/2014 * 0 * 290 * 1 * 1/9/2015 * 9.7 * 12.5
119 * 2/21/2014 * 1 * 324 * 1 * 1/11/2015 * 10.8 * 13.6
120 * 2/25/2014 * 1 * 323 * 1 * 1/14/2015 * 10.8 * 13.6
121 * 10/2/2014 * 1 * 105 * 1 * 1/15/2015 * 3.5 * 6.3
122 * 10/5/2014 * 1 * 105 * 1 * 1/18/2015 * 3.5 * 6.3
123 * 3/7/2014 * 1 * 320 * 1 * 1/21/2015 * 10.7 * 13.5
124 * 10/11/2014 * 1 * 112 * 1 * 1/31/2015 * 3.7 * 6.5
125 * 4/12/2014 * 0 * 300 * 1 * 2/6/2015 * 10 * 12.8
126 * 3/29/2014 * 1 * 325 * 1 * 2/17/2015 * 10.8 * 13.6
127 * 4/3/2014 * 1 * 325 * 1 * 2/22/2015 * 10.8 * 13.6
128 * 4/30/2014 * 0 * 300 * 1 * 2/24/2015 * 10 * 12.8
129 * 11/4/2014 * 1 * 112 * 1 * 2/24/2015 * 3.7 * 6.5
130 * 4/7/2014 * 1 * 325 * 1 * 2/26/2015 * 10.8 * 13.6
131 * 11/7/2014 * 1 * 120 * 1 * 3/7/2015 * 4 * 6.8
132 * 11/13/2014 * 1 * 120 * 1 * 3/13/2015 * 4 * 6.8
133 * 5/9/2014 * 0 * 310 * 1 * 3/15/2015 * 10.3 * 13.1
134 * 11/25/2014 * 1 * 120 * 1 * 3/25/2015 * 4 * 6.8
135 * 10/26/2014 * 0 * 151 * 1 * 3/26/2015 * 5 * 7.8
136 * 10/29/2014 * 0 * 151 * 1 * 3/29/2015 * 5 * 7.8
137 * 5/22/2014 * 0 * 312 * 1 * 3/30/2015 * 10.4 * 13.2
138 * 12/3/2014 * 1 * 120 * 1 * 4/2/2015 * 4 * 6.8
139 * 12/6/2014 * 1 * 122 * 1 * 4/7/2015 * 4.1 * 6.9
140 * 11/1/2014 * 0 * 158 * 1 * 4/8/2015 * 5.3 * 8.1
141 * 12/9/2014 * 1 * 122 * 1 * 4/10/2015 * 4.1 * 6.9
142 * 11/10/2014 * 0 * 158 * 1 * 4/17/2015 * 5.3 * 8.1
143 * 12/18/2014 * 1 * 123 * 1 * 4/20/2015 * 4.1 * 6.9
144 * 6/9/2014 * 0 * 315 * 1 * 4/20/2015 * 10.5 * 13.3
145 * 11/19/2014 * 0 * 158 * 1 * 4/26/2015 * 5.3 * 8.1
146 * 6/12/2014 * 0 * 318 * 1 * 4/26/2015 * 10.6 * 13.4
147 * 6/15/2014 * 0 * 319 * 1 * 4/30/2015 * 10.6 * 13.4
148 * 12/27/2014 * 1 * 126 * 1 * 5/2/2015 * 4.2 * 7
149 * 11/22/2014 * 0 * 172 * 1 * 5/13/2015 * 5.7 * 8.5
150 * 12/1/2014 * 0 * 172 * 1 * 5/22/2015 * 5.7 * 8.5
151 * 7/6/2014 * 0 * 320 * 1 * 5/22/2015 * 10.7 * 13.5
152 * 12/30/2014 * 1 * 151 * 1 * 5/30/2015 * 5 * 7.8
153 * 7/15/2014 * 0 * 322 * 1 * 6/2/2015 * 10.7 * 13.5
154 * 1/5/2015 * 1 * 151 * 1 * 6/5/2015 * 5 * 7.8
155 * 12/15/2014 * 0 * 172 * 1 * 6/5/2015 * 5.7 * 8.5
156 * 7/24/2014 * 0 * 321 * 1 * 6/10/2015 * 10.7 * 13.5
157 * 7/27/2014 * 0 * 323 * 1 * 6/15/2015 * 10.8 * 13.6
158 * 12/24/2014 * 0 * 175 * 1 * 6/17/2015 * 5.8 * 8.6
159 * 1/14/2015 * 1 * 158 * 1 * 6/21/2015 * 5.3 * 8.1
160 * 1/2/2015 * 0 * 175 * 1 * 6/26/2015 * 5.8 * 8.6
161 * 6/21/2014 * 1 * 380 * 1 * 7/6/2015 * 12.7 * 15.5
162 * 1/8/2015 * 0 * 182 * 1 * 7/9/2015 * 6.1 * 8.9
163 * 6/27/2014 * 1 * 380 * 1 * 7/12/2015 * 12.7 * 15.5
164 * 1/26/2015 * 1 * 172 * 1 * 7/17/2015 * 5.7 * 8.5
165 * 8/28/2014 * 0 * 324 * 1 * 7/18/2015 * 10.8 * 13.6
166 * 1/29/2015 * 1 * 172 * 1 * 7/20/2015 * 5.7 * 8.5
167 * 2/1/2015 * 1 * 172 * 1 * 7/23/2015 * 5.7 * 8.5
168 * 9/6/2014 * 0 * 324 * 1 * 7/27/2015 * 10.8 * 13.6
169 * 7/3/2014 * 1 * 390 * 1 * 7/28/2015 * 13 * 15.8
170 * 9/18/2014 * 0 * 320 * 1 * 8/4/2015 * 10.7 * 13.5
171 * 9/15/2014 * 0 * 323 * 1 * 8/4/2015 * 10.8 * 13.6
172 * 2/13/2015 * 1 * 175 * 1 * 8/7/2015 * 5.8 * 8.6
173 * 9/27/2014 * 0 * 320 * 1 * 8/13/2015 * 10.7 * 13.5
174 * 9/30/2014 * 0 * 325 * 1 * 8/21/2015 * 10.8 * 13.6
175 * 2/25/2015 * 1 * 182 * 1 * 8/26/2015 * 6.1 * 8.9
176 * 10/17/2014 * 0 * 325 * 1 * 9/7/2015 * 10.8 * 13.6
177 * 10/20/2014 * 0 * 325 * 1 * 9/10/2015 * 10.8 * 13.6
178 * 7/9/2014 * 1 * 430 * 1 * 9/12/2015 * 14.3 * 17.1
179 * 10/23/2014 * 0 * 325 * 1 * 9/13/2015 * 10.8 * 13.6
180 * 7/12/2014 * 1 * 430 * 1 * 9/15/2015 * 14.3 * 17.1
181 * 3/3/2015 * 1 * 200 * 1 * 9/19/2015 * 6.7 * 9.5
182 * 3/6/2015 * 1 * 200 * 1 * 9/22/2015 * 6.7 * 9.5
183 * 7/21/2014 * 1 * 435 * 1 * 9/29/2015 * 14.5 * 17.3
184 * 3/15/2015 * 1 * 206 * 1 * 10/7/2015 * 6.9 * 9.7
185 * 8/1/2014 * 1 * 440 * 1 * 10/15/2015 * 14.7 * 17.5
186 * 3/21/2015 * 1 * 210 * 1 * 10/17/2015 * 7 * 9.8
187 * 3/24/2015 * 1 * 224 * 1 * 11/3/2015 * 7.5 * 10.3
188 * 3/27/2015 * 1 * 224 * 1 * 11/6/2015 * 7.5 * 10.3
189 * 8/4/2014 * 1 * 470 * 1 * 11/17/2015 * 15.7 * 18.5
190 * 8/7/2014 * 1 * 470 * 1 * 11/20/2015 * 15.7 * 18.5
191 * 8/16/2014 * 1 * 480 * 1 * 12/9/2015 * 16 * 18.8
192 * 4/8/2015 * 1 * 260 * 1 * 12/24/2015 * 8.7 * 11.5
193 * 7/28/2015 * 1 * 151 * 1 * 12/26/2015 * 5 * 7.8
194 * 8/3/2015 * 1 * 158 * 1 * 1/8/2016 * 5.3 * 8.1
195 * 8/9/2015 * 1 * 158 * 1 * 1/14/2016 * 5.3 * 8.1
196 * 4/17/2015 * 1 * 278 * 1 * 1/20/2016 * 9.3 * 12.1
197 * 8/19/2014 * 1 * 520 * 1 * 1/21/2016 * 17.3 * 20.1
198 * 1/20/2015 * 0 * 380 * 1 * 2/4/2016 * 12.7 * 15.5
199 * 4/23/2015 * 1 * 290 * 1 * 2/7/2016 * 9.7 * 12.5
200 * 1/23/2015 * 0 * 380 * 1 * 2/7/2016 * 12.7 * 15.5
201 * 4/26/2015 * 1 * 290 * 1 * 2/10/2016 * 9.7 * 12.5
202 * 2/7/2015 * 0 * 380 * 1 * 2/22/2016 * 12.7 * 15.5
203 * 5/2/2015 * 1 * 300 * 1 * 2/26/2016 * 10 * 12.8
204 * 2/10/2015 * 0 * 390 * 1 * 3/6/2016 * 13 * 15.8
205 * 2/16/2015 * 0 * 390 * 1 * 3/12/2016 * 13 * 15.8
206 * 5/8/2015 * 1 * 312 * 1 * 3/15/2016 * 10.4 * 13.2
207 * 5/11/2015 * 1 * 315 * 1 * 3/21/2016 * 10.5 * 13.3
208 * 5/14/2015 * 1 * 318 * 1 * 3/27/2016 * 10.6 * 13.4
209 * 5/26/2015 * 1 * 320 * 1 * 4/10/2016 * 10.7 * 13.5
210 * 6/7/2015 * 1 * 321 * 1 * 4/23/2016 * 10.7 * 13.5
211 * 2/19/2015 * 0 * 430 * 1 * 4/24/2016 * 14.3 * 17.1
212 * 2/28/2015 * 0 * 430 * 1 * 5/3/2016 * 14.3 * 17.1
213 * 6/16/2015 * 1 * 324 * 1 * 5/5/2016 * 10.8 * 13.6
214 * 6/22/2015 * 1 * 324 * 1 * 5/11/2016 * 10.8 * 13.6
215 * 6/28/2015 * 1 * 320 * 1 * 5/13/2016 * 10.7 * 13.5
216 * 3/9/2015 * 0 * 435 * 1 * 5/17/2016 * 14.5 * 17.3
217 * 7/13/2015 * 1 * 325 * 1 * 6/2/2016 * 10.8 * 13.6
218 * 7/16/2015 * 1 * 325 * 1 * 6/5/2016 * 10.8 * 13.6
219 * 3/30/2015 * 0 * 435 * 1 * 6/7/2016 * 14.5 * 17.3
220 * 7/19/2015 * 1 * 325 * 1 * 6/8/2016 * 10.8 * 13.6
221 * 4/2/2015 * 0 * 440 * 1 * 6/15/2016 * 14.7 * 17.5
222 * 4/14/2015 * 0 * 440 * 1 * 6/27/2016 * 14.7 * 17.5
223 * 4/20/2015 * 0 * 470 * 1 * 8/2/2016 * 15.7 * 18.5
224 * 8/12/2015 * 1 * 380 * 1 * 8/26/2016 * 12.7 * 15.5
225 * 5/17/2015 * 0 * 470 * 1 * 8/29/2016 * 15.7 * 18.5
226 * 8/25/2014 * 1 * 740 * 1 * 9/3/2016 * 24.7 * 27.5
227 * 5/23/2015 * 0 * 480 * 1 * 9/14/2016 * 16 * 18.8
228 * 8/24/2015 * 1 * 390 * 1 * 9/17/2016 * 13 * 15.8
229 * 5/29/2015 * 0 * 480 * 1 * 9/20/2016 * 16 * 18.8
230 * 8/27/2015 * 1 * 390 * 1 * 9/20/2016 * 13 * 15.8
231 * 6/1/2015 * 0 * 520 * 1 * 11/2/2016 * 17.3 * 20.1
232 * 8/30/2015 * 1 * 430 * 1 * 11/2/2016 * 14.3 * 17.1
233 * 9/5/2015 * 1 * 435 * 1 * 11/13/2016 * 14.5 * 17.3
234 * 9/11/2015 * 1 * 440 * 1 * 11/24/2016 * 14.7 * 17.5
235 * 9/14/2015 * 1 * 440 * 1 * 11/27/2016 * 14.7 * 17.5
236 * 9/17/2015 * 1 * 470 * 1 * 12/30/2016 * 15.7 * 18.5
237 * 9/29/2015 * 1 * 480 * 1 * 1/21/2017 * 16 * 18.8
238 * 8/13/2014 * 0 * 902.2048193 * 1 * 2/1/2017 * 30.1 * 32.9
239 * 10/5/2015 * 0 * 485 * 0 * 2/1/2017 * 16.2 * 19
240 * 9/23/2015 * 0 * 497 * 0 * 2/1/2017 * 16.6 * 19.4
241 * 9/20/2015 * 0 * 500 * 0 * 2/1/2017 * 16.7 * 19.5
242 * 8/21/2015 * 0 * 530 * 0 * 2/1/2017 * 17.7 * 20.5
243 * 8/18/2015 * 0 * 533 * 0 * 2/1/2017 * 17.8 * 20.6
244 * 8/6/2015 * 0 * 545 * 0 * 2/1/2017 * 18.2 * 21
245 * 7/22/2015 * 0 * 560 * 0 * 2/1/2017 * 18.7 * 21.5
246 * 7/10/2015 * 0 * 572 * 0 * 2/1/2017 * 19.1 * 21.9
247 * 7/7/2015 * 0 * 575 * 0 * 2/1/2017 * 19.2 * 22
248 * 7/4/2015 * 0 * 578 * 0 * 2/1/2017 * 19.3 * 22.1
249 * 6/19/2015 * 0 * 593 * 0 * 2/1/2017 * 19.8 * 22.6
250 * 6/4/2013 * 1 * 1337.95122 * 1 * 2/1/2017 * 44.6 * 47.4
251 * 6/3/2014 * 1 * 973.0481928 * 1 * 2/1/2017 * 32.4 * 35.2
252 * 11/16/2014 * 1 * 807.746988 * 1 * 2/1/2017 * 26.9 * 29.7
253 * 2/22/2015 * 1 * 710 * 1 * 2/1/2017 * 23.7 * 26.5
254 * 4/11/2015 * 1 * 662 * 1 * 2/1/2017 * 22.1 * 24.9
255 * 7/25/2015 * 1 * 557 * 1 * 2/1/2017 * 18.6 * 21.4
256 * 10/2/2015 * 1 * 488 * 1 * 2/1/2017 * 16.3 * 19.1
257 * 8/18/2008 * 1 * 3089 * 0 * 2/1/2017 * 103 * 105.8
258 * 9/1/2008 * 1 * 3075 * 0 * 2/1/2017 * 102.5 * 105.3
259 * 10/22/2008 * 1 * 3024 * 0 * 2/1/2017 * 100.8 * 103.6
260 * 1/21/2009 * 1 * 2933 * 0 * 2/1/2017 * 97.8 * 100.6
261 * 4/15/2009 * 1 * 2849 * 0 * 2/1/2017 * 95 * 97.8
262 * 10/11/2011 * 1 * 1939.2 * 0 * 2/1/2017 * 64.6 * 67.4
263 * 1/17/2012 * 1 * 1842 * 0 * 2/1/2017 * 61.4 * 64.2
264 * 1/27/2012 * 1 * 1831.2 * 0 * 2/1/2017 * 61 * 63.8
265 * 4/12/2012 * 1 * 1755.6 * 0 * 2/1/2017 * 58.5 * 61.3
266 * 4/23/2012 * 1 * 1744.8 * 0 * 2/1/2017 * 58.2 * 61
267 * 7/7/2012 * 1 * 1669.2 * 0 * 2/1/2017 * 55.6 * 58.4
268 * 7/18/2012 * 1 * 1658.4 * 0 * 2/1/2017 * 55.3 * 58.1
269 * 10/2/2012 * 1 * 1582.8 * 0 * 2/1/2017 * 52.8 * 55.6
270 * 10/13/2012 * 1 * 1572 * 0 * 2/1/2017 * 52.4 * 55.2
271 * 12/27/2012 * 1 * 1496.4 * 0 * 2/1/2017 * 49.9 * 52.7
272 * 1/7/2013 * 1 * 1485.6 * 0 * 2/1/2017 * 49.5 * 52.3
273 * 3/13/2013 * 1 * 1420.8 * 0 * 2/1/2017 * 47.4 * 50.2
274 * 3/24/2013 * 1 * 1410 * 0 * 2/1/2017 * 47 * 49.8
275 * 6/8/2013 * 1 * 1333.426829 * 0 * 2/1/2017 * 44.4 * 47.2
276 * 6/26/2013 * 1 * 1315.329268 * 0 * 2/1/2017 * 43.8 * 46.6
277 * 7/1/2013 * 1 * 1310.804878 * 0 * 2/1/2017 * 43.7 * 46.5
278 * 7/28/2013 * 1 * 1283.658537 * 0 * 2/1/2017 * 42.8 * 45.6
279 * 8/1/2013 * 1 * 1279.134146 * 0 * 2/1/2017 * 42.6 * 45.4
280 * 8/24/2013 * 1 * 1256.512195 * 0 * 2/1/2017 * 41.9 * 44.7
281 * 8/29/2013 * 1 * 1251.987805 * 0 * 2/1/2017 * 41.7 * 44.5
282 * 9/25/2013 * 1 * 1224.841463 * 0 * 2/1/2017 * 40.8 * 43.6
283 * 9/29/2013 * 1 * 1220.317073 * 0 * 2/1/2017 * 40.7 * 43.5
284 * 10/31/2013 * 1 * 1188.646341 * 0 * 2/1/2017 * 39.6 * 42.4
285 * 11/4/2013 * 1 * 1184.121951 * 0 * 2/1/2017 * 39.5 * 42.3
286 * 11/22/2013 * 1 * 1166.02439 * 0 * 2/1/2017 * 38.9 * 41.7
287 * 11/27/2013 * 1 * 1161.5 * 0 * 2/1/2017 * 38.7 * 41.5
288 * 12/20/2013 * 1 * 1138.878049 * 0 * 2/1/2017 * 38 * 40.8
289 * 12/24/2013 * 1 * 1134.353659 * 0 * 2/1/2017 * 37.8 * 40.6
290 * 1/11/2014 * 1 * 1116.256098 * 0 * 2/1/2017 * 37.2 * 40
291 * 1/16/2014 * 1 * 1111.731707 * 0 * 2/1/2017 * 37.1 * 39.9
292 * 2/12/2014 * 1 * 1084.585366 * 0 * 2/1/2017 * 36.2 * 39
293 * 2/16/2014 * 1 * 1080.060976 * 0 * 2/1/2017 * 36 * 38.8
294 * 3/11/2014 * 1 * 1057.439024 * 0 * 2/1/2017 * 35.2 * 38
295 * 3/20/2014 * 1 * 1048.390244 * 0 * 2/1/2017 * 34.9 * 37.7
296 * 4/16/2014 * 1 * 1021.243902 * 0 * 2/1/2017 * 34 * 36.8
297 * 4/25/2014 * 1 * 1012.195122 * 0 * 2/1/2017 * 33.7 * 36.5
298 * 5/27/2014 * 1 * 980.5243902 * 0 * 2/1/2017 * 32.7 * 35.5
299 * 6/24/2014 * 1 * 952.3855422 * 0 * 2/1/2017 * 31.7 * 34.5
300 * 6/30/2014 * 1 * 946.4819277 * 0 * 2/1/2017 * 31.5 * 34.3
301 * 7/18/2014 * 1 * 928.7710843 * 0 * 2/1/2017 * 31 * 33.8
302 * 7/30/2014 * 1 * 916.9638554 * 0 * 2/1/2017 * 30.6 * 33.4
303 * 8/10/2014 * 1 * 905.1566265 * 0 * 2/1/2017 * 30.2 * 33
304 * 8/22/2014 * 1 * 893.3493976 * 0 * 2/1/2017 * 29.8 * 32.6
305 * 9/9/2014 * 1 * 875.6385542 * 0 * 2/1/2017 * 29.2 * 32
306 * 9/21/2014 * 1 * 863.8313253 * 0 * 2/1/2017 * 28.8 * 31.6
307 * 10/8/2014 * 1 * 846.1204819 * 0 * 2/1/2017 * 28.2 * 31
308 * 10/14/2014 * 1 * 840.2168675 * 0 * 2/1/2017 * 28 * 30.8
309 * 11/28/2014 * 1 * 795.939759 * 0 * 2/1/2017 * 26.5 * 29.3
310 * 12/12/2014 * 1 * 781.1807229 * 0 * 2/1/2017 * 26 * 28.8
311 * 12/21/2014 * 1 * 772.3253012 * 0 * 2/1/2017 * 25.7 * 28.5
312 * 1/11/2015 * 1 * 751.6626506 * 0 * 2/1/2017 * 25.1 * 27.9
313 * 1/17/2015 * 1 * 745.7590361 * 0 * 2/1/2017 * 24.9 * 27.7
314 * 2/4/2015 * 1 * 728 * 0 * 2/1/2017 * 24.3 * 27.1
315 * 3/12/2015 * 1 * 692 * 0 * 2/1/2017 * 23.1 * 25.9
316 * 3/18/2015 * 1 * 686 * 0 * 2/1/2017 * 22.9 * 25.7
317 * 4/5/2015 * 1 * 668 * 0 * 2/1/2017 * 22.3 * 25.1
318 * 4/29/2015 * 1 * 644 * 0 * 2/1/2017 * 21.5 * 24.3
319 * 5/5/2015 * 1 * 638 * 0 * 2/1/2017 * 21.3 * 24.1
320 * 5/20/2015 * 1 * 623 * 0 * 2/1/2017 * 20.8 * 23.6
321 * 6/4/2015 * 1 * 608 * 0 * 2/1/2017 * 20.3 * 23.1
322 * 6/25/2015 * 1 * 587 * 0 * 2/1/2017 * 19.6 * 22.4
323 * 7/1/2015 * 1 * 581 * 0 * 2/1/2017 * 19.4 * 22.2
324 * 7/31/2015 * 1 * 551 * 0 * 2/1/2017 * 18.4 * 21.2
325 * 8/15/2015 * 1 * 536 * 0 * 2/1/2017 * 17.9 * 20.7
326 * 9/2/2015 * 1 * 518 * 0 * 2/1/2017 * 17.3 * 20.1
327 * 9/8/2015 * 1 * 512 * 0 * 2/1/2017 * 17.1 * 19.9
328 * 9/26/2015 * 1 * 494 * 0 * 2/1/2017 * 16.5 * 19.3
329 * 6/10/2015 * 0 * 610 * 1 * 2/9/2017 * 20.3 * 23.1
330 * 6/13/2015 * 0 * 740 * 1 * 6/22/2017 * 24.7 * 27.5
331 * 10/15/2015 * 1 * 740 * 1 * 10/24/2017 * 24.7 * 27.5
DCVax-L GBM trial Result PFS Model based on IMUC Control DATA
Reposted now with updated working links.
Discloser: I am long this stock; I have no inside information; I am a clinical researcher.
Hypothesis 1: The Control Patients in the DCVax-L GBM trial should relapse at a rate similar to what was shown in the IMUC trial. The IMUC trial is the most similar to the DCVax-L GBM trial in terms of eligibility entry criteria. The progression free survival (PFS) was reported recently at the 2014 American Society of Clinical Oncology (ASCO) meeting.
https://www.dropbox.com/s/a90hj1507dd2ekr/bosch%20nwbo%203.PNG?dl=0
Hypothesis 2: Given that we know the number of PFS events in the total DCVax-L trial at this time (first interim analysis) is 66 December 2013;
https://www.dropbox.com/s/qy0zlxwehjf5f9l/nwbo%2066%20events.JPG?dl=0
And NWBO reported 248 PFS events on before 2/1/2017
https://www.dropbox.com/s/w60sbgacpzi28ck/bosch%20nwbo%201.PNG?dl=0
Then one can model using JMP(SAS) software, model the control arm PFS for the DCVaX-L trial to be the same as the corrected IMUC trial results and thus obtain an estimate of the probable number of events required in the control arm of the DCVax-L. When one does this with a Kaplan Meier plot, one obtains a result of 100 events in the control arm with the total number of patients in the control arm of 111. Then one can deduce that the number of events in the experimental arm for the DCVax-L trial would be 331- 111 or 148 events.
Hypothesis 3: Using the derived 148 PFS events in the experimental arm, one can model the experimental arm to be similar to the IMUC control arm trial results but, in this case only allow for just 148 events out of 220 total patients in the DCVax-L experimental arm.
When one does this with a Kaplan Meier plot, the data and curves are shown below. When one applies log-rank and Wilcoxon significance testing to the two curves one obtains a significant result of p<.0001
https://www.dropbox.com/s/o6270dlwrcx3wr8/PFS%20NWBO%20COMPARE%20jpg.JPG?dl=0
My best-educated hypothesis is that clearly, the DCVax-L trial is going to return a positive result on the primary end point
This result will be a practice changing result. Especially when one sees the plateau in the treatment curve. The nice thing about this PFS review also is that there is no crossover effect between the curves such as could occur with my prior overall survival analysis OS.
129619 Alphapuppy Ihub post
Just to put this into perspective the Stupp GMB trial PFS only increased 2 months by the addition of temazolamide (TMZ) (5 to 6.9 months). Yet this became almost immediately, the new gold standard of care in this disease and TMZ almost immediately became a blockbuster multi-billion dollar drug.
DCVaX-L increases in the operable patients even more than this and with none of the chemotherapy side effects and will quickly be the new standard of care in this disease.
Tadasana
Nope, I have not sent any of the model data to NWBO. I figured that they would have more info than me!!
But your point is well taken, as being lawyers, they might have more info, but absolutely no understanding of how to get good data and information from what they have.
I would assume that they would monitor this board, but after review of that recent Lawsuit where they didn't even know they were having a judgment hearing, it does make me wonder if they do monitor this or anything else besides the getting more money issue.
ALSO, I just found out that as of 9-1-2017 Dropbox was dropping their public folder so I have been informed that all my prior links will no longer work.
Please contact me if any of you would want new links to the visual data located in my previous Posts.
IF there is that desire I can re-post the posts with updated links.
AP
Jammy
Here is the model with a lock date of 8-15-2015.
Still PFS is significant. I expect however that OS would not be significant at this time.
Note only 313 cases at this time as some participants were randomized after this lock date.
https://www.dropbox.com/s/ml1mpe0q0wt9ue3/dcfax%20PFS%20model%20lock%20date%20aug%202015%20-%20Copy.JPG?dl=0
AP
China
excerpt from your post
"Re: antihama Post# 132371
It's unbelievable really. There are just too many different pieces of evidence that point to either total failure, major complications,"
Wondering therefore what you mean by major complication?
AP
China
Median is just one point on the curves.
Look at the entire curves to see the differences.
AP
Abeta
AMEN to that.
Having spent over 30 years seeing people devastated by this EMPEROR of all CANCERS it is hard to express how good and beautiful a plateau in a PFS GBM survival curve is.
AP
Thanks Senti
You are as smart as ever.
I do have curves both ways. With the added 83 days and without. I initially ran the curves without the added 83 days but when I reviewed one of my prior posts I thought IMUC randomization occurred after RT TMZ and DCVaX-L Trial entry date was earlier.
Well here is the curves WITHOUT the added 83 days.
Here it is without the added 83 days.
https://dl.dropboxusercontent.com/u/11047753/PFS%20NWBO%20COMPARE%20jpg.JPG
Here are the curves WITH the 83 days added.
https://dl.dropboxusercontent.com/u/11047753/PFS%20NWBO%20COMPARE%20plus%2083.JPG
AP
Sure Yes the tail and curve plateau would be better. Not sure if the p value would improve much as they don't usually report anything different than p<.001 which is what it is at currently.
AP
Also Tas
For my data the date 2-1-2017 was analagous to the "data Lock" because that was the date that NWBO disclosed that there at least 248 events in the whole population.
AP
Tas
Data lock will equal a real date.
ie Say 9-1-2017 or whatever.
Then that date will be used as the "last date of Follow-up or Death" and the KM curves and all statistics will use that date to calculate the time factor (ie amount of time from a patients entry into the trial to the particular parameter measured such as progression or death or whatever is defined as an 'event'. Or if the patient has not experienced any event they will have a time factor = to the lock date minus their date of trial entry and randomization.
For patients that have not evented, they will be censored and not used for making the KM curve for any time that is greater than the difference of the lock date minus their particular data entry date or randomization date.
Please see this review and comparison of my PFS model results with the NEJM STUPP data. One can see how revolutionary this treatment will be as the STUPP protocol is arguably still the standard of Care (+/- Optune).
https://dl.dropboxusercontent.com/u/11047753/DCVacL%20compared%20to%20STUPP.JPG
Also, note the Stupp gragh gives the number of patients at risk at various time intervals under the gragh so one knows how many patients data are left for the longer time periods out. IE those numbers below equal the total number of patients on the various trial arms minus those who have evented or who were censored.
For you to better understand this I include below the data I used to create the current model comparisons. So, in this case, the Date of entry is the dates given by Bosch at ASCO 2017 for the patient enrollment ramp. The treatment(1) or Placebo (2) was generated by a random number generator in excel in a 2 to 1 ratio for experimental and placebo respectively. Then the Control (0) group was manually given the time in days and the events directly based on the IMUC Control group data pro rata to compensate for the larger NWBO trial.
Then once the number of events were derived in the NWBO control group by this method, the number of events remaining was simply a subtraction of the number of PFS events that NWBO has given us on 2 separate occasions: Once in Dec 2013 66 PFS events for the whole group and another disclosed in Feb. 2017 or before when 248 events were disclosed.
Then the exact same process was used on the experimental patient group in that the events and time were placed in to be equal to the published IMUC control group KM PFS curves ... but here is the cool part ... the number of events was limited to 148 which was derived as noted above and by Dec 2013 only 66 events were placed prior to that date. Therefore there were significantly fewer events in the experimental DCVaxL arm than would have been expected had the experiment arm survived progression free at the same rate as was published by the IMUC control group curves.
Then it was a simple matter of inputting the time factor, the event/censor factor and the group placebo or experimental into the KM statistical software.
And walla the curves appear with the statistical comparison tests.
Derived Data for all 331 patients in the DCVax-L TRIAL
PT # * ENTRY * RX=1 Placebo=0 * DAYS * 1=event 0= censor * DATE EVENT or Censor * MONTHS * Plus 83d Mo
1 * 6/2/2008 * 1 * 53 * 1 * 7/25/2008 * 1.8 * 4.6
2 * 6/13/2008 * 0 * 53 * 1 * 8/5/2008 * 1.8 * 4.6
3 * 6/27/2008 * 1 * 53 * 1 * 8/19/2008 * 1.8 * 4.6
4 * 7/23/2008 * 0 * 53 * 1 * 9/14/2008 * 1.8 * 4.6
5 * 7/10/2008 * 1 * 85 * 1 * 10/3/2008 * 2.8 * 5.6
6 * 8/5/2008 * 0 * 85 * 1 * 10/29/2008 * 2.8 * 5.6
7 * 9/12/2008 * 1 * 85 * 1 * 12/6/2008 * 2.8 * 5.6
8 * 9/26/2008 * 1 * 95 * 1 * 12/30/2008 * 3.2 * 6
9 * 10/9/2008 * 1 * 95 * 1 * 1/12/2009 * 3.2 * 6
10 * 11/4/2008 * 0 * 85 * 1 * 1/28/2009 * 2.8 * 5.6
11 * 11/17/2008 * 0 * 95 * 1 * 2/20/2009 * 3.2 * 6
12 * 12/1/2008 * 0 * 95 * 1 * 3/6/2009 * 3.2 * 6
13 * 12/12/2008 * 0 * 105 * 1 * 3/27/2009 * 3.5 * 6.3
14 * 12/26/2008 * 0 * 105 * 1 * 4/10/2009 * 3.5 * 6.3
15 * 1/8/2009 * 0 * 112 * 1 * 4/30/2009 * 3.7 * 6.5
16 * 2/3/2009 * 1 * 105 * 1 * 5/19/2009 * 3.5 * 6.3
17 * 2/16/2009 * 1 * 105 * 1 * 6/1/2009 * 3.5 * 6.3
18 * 3/2/2009 * 0 * 112 * 1 * 6/22/2009 * 3.7 * 6.5
19 * 3/13/2009 * 0 * 112 * 1 * 7/3/2009 * 3.7 * 6.5
20 * 3/27/2009 * 0 * 112 * 1 * 7/17/2009 * 3.7 * 6.5
21 * 4/9/2009 * 1 * 112 * 1 * 7/30/2009 * 3.7 * 6.5
22 * 10/3/2011 * 0 * 120 * 1 * 1/31/2012 * 4 * 6.8
23 * 10/21/2011 * 0 * 120 * 1 * 2/18/2012 * 4 * 6.8
24 * 11/2/2011 * 0 * 120 * 1 * 3/1/2012 * 4 * 6.8
25 * 11/14/2011 * 1 * 112 * 1 * 3/5/2012 * 3.7 * 6.5
26 * 11/24/2011 * 0 * 120 * 1 * 3/23/2012 * 4 * 6.8
27 * 12/5/2011 * 0 * 120 * 1 * 4/3/2012 * 4 * 6.8
28 * 12/15/2011 * 1 * 112 * 1 * 4/5/2012 * 3.7 * 6.5
29 * 12/26/2011 * 1 * 112 * 1 * 4/16/2012 * 3.7 * 6.5
30 * 1/6/2012 * 0 * 120 * 1 * 5/5/2012 * 4 * 6.8
31 * 2/7/2012 * 0 * 122 * 1 * 6/8/2012 * 4.1 * 6.9
32 * 2/17/2012 * 1 * 120 * 1 * 6/16/2012 * 4 * 6.8
33 * 2/29/2012 * 0 * 122 * 1 * 6/30/2012 * 4.1 * 6.9
34 * 3/11/2012 * 1 * 120 * 1 * 7/9/2012 * 4 * 6.8
35 * 3/21/2012 * 1 * 120 * 1 * 7/19/2012 * 4 * 6.8
36 * 4/1/2012 * 0 * 123 * 1 * 8/2/2012 * 4.1 * 6.9
37 * 5/4/2012 * 0 * 123 * 1 * 9/4/2012 * 4.1 * 6.9
38 * 5/14/2012 * 1 * 120 * 1 * 9/11/2012 * 4 * 6.8
39 * 5/25/2012 * 1 * 120 * 1 * 9/22/2012 * 4 * 6.8
40 * 6/5/2012 * 0 * 126 * 1 * 10/9/2012 * 4.2 * 7
41 * 6/16/2012 * 1 * 120 * 1 * 10/14/2012 * 4 * 6.8
42 * 6/27/2012 * 0 * 126 * 1 * 10/31/2012 * 4.2 * 7
43 * 8/20/2012 * 1 * 122 * 1 * 12/20/2012 * 4.1 * 6.9
44 * 7/29/2012 * 0 * 151 * 1 * 12/27/2012 * 5 * 7.8
45 * 8/9/2012 * 0 * 151 * 1 * 1/7/2013 * 5 * 7.8
46 * 9/10/2012 * 1 * 122 * 1 * 1/10/2013 * 4.1 * 6.9
47 * 9/21/2012 * 1 * 123 * 1 * 1/22/2013 * 4.1 * 6.9
48 * 8/30/2012 * 0 * 158 * 1 * 2/4/2013 * 5.3 * 8.1
49 * 11/3/2012 * 1 * 123 * 1 * 3/6/2013 * 4.1 * 6.9
50 * 11/14/2012 * 1 * 126 * 1 * 3/20/2013 * 4.2 * 7
51 * 10/23/2012 * 0 * 158 * 1 * 3/30/2013 * 5.3 * 8.1
52 * 12/16/2012 * 1 * 126 * 1 * 4/21/2013 * 4.2 * 7
53 * 11/25/2012 * 0 * 158 * 1 * 5/2/2013 * 5.3 * 8.1
54 * 12/6/2012 * 0 * 172 * 1 * 5/27/2013 * 5.7 * 8.5
55 * 1/18/2013 * 1 * 151 * 1 * 6/18/2013 * 5 * 7.8
56 * 1/29/2013 * 1 * 151 * 1 * 6/29/2013 * 5 * 7.8
57 * 2/8/2013 * 1 * 158 * 1 * 7/16/2013 * 5.3 * 8.1
58 * 2/19/2013 * 0 * 172 * 1 * 8/10/2013 * 5.7 * 8.5
59 * 3/2/2013 * 0 * 172 * 1 * 8/21/2013 * 5.7 * 8.5
60 * 4/3/2013 * 1 * 158 * 1 * 9/8/2013 * 5.3 * 8.1
61 * 4/25/2013 * 1 * 158 * 1 * 9/30/2013 * 5.3 * 8.1
62 * 4/14/2013 * 0 * 175 * 1 * 10/6/2013 * 5.8 * 8.6
63 * 5/6/2013 * 0 * 175 * 1 * 10/28/2013 * 5.8 * 8.6
64 * 5/15/2013 * 0 * 182 * 1 * 11/13/2013 * 6.1 * 8.9
65 * 5/30/2013 * 1 * 172 * 1 * 11/18/2013 * 5.7 * 8.5
66 * 5/26/2013 * 0 * 182 * 1 * 11/24/2013 * 6.1 * 8.9
67 * 6/13/2013 * 1 * 172 * 1 * 12/2/2013 * 5.7 * 8.5
68 * 6/17/2013 * 1 * 172 * 1 * 12/6/2013 * 5.7 * 8.5
69 * 6/22/2013 * 1 * 175 * 1 * 12/14/2013 * 5.8 * 8.6
70 * 7/5/2013 * 1 * 175 * 1 * 12/27/2013 * 5.8 * 8.6
71 * 7/14/2013 * 1 * 182 * 1 * 1/12/2014 * 6.1 * 8.9
72 * 7/19/2013 * 1 * 182 * 1 * 1/17/2014 * 6.1 * 8.9
73 * 7/10/2013 * 0 * 200 * 1 * 1/26/2014 * 6.7 * 9.5
74 * 7/23/2013 * 0 * 200 * 1 * 2/8/2014 * 6.7 * 9.5
75 * 8/6/2013 * 1 * 200 * 1 * 2/22/2014 * 6.7 * 9.5
76 * 8/15/2013 * 1 * 200 * 1 * 3/3/2014 * 6.7 * 9.5
77 * 8/10/2013 * 0 * 206 * 1 * 3/4/2014 * 6.9 * 9.7
78 * 8/19/2013 * 1 * 206 * 1 * 3/13/2014 * 6.9 * 9.7
79 * 9/2/2013 * 1 * 206 * 1 * 3/27/2014 * 6.9 * 9.7
80 * 9/7/2013 * 0 * 206 * 1 * 4/1/2014 * 6.9 * 9.7
81 * 9/11/2013 * 1 * 210 * 1 * 4/9/2014 * 7 * 9.8
82 * 9/16/2013 * 1 * 210 * 1 * 4/14/2014 * 7 * 9.8
83 * 9/20/2013 * 0 * 210 * 1 * 4/18/2014 * 7 * 9.8
84 * 10/13/2013 * 0 * 210 * 1 * 5/11/2014 * 7 * 9.8
85 * 10/4/2013 * 1 * 224 * 1 * 5/16/2014 * 7.5 * 10.3
86 * 10/8/2013 * 1 * 224 * 1 * 5/20/2014 * 7.5 * 10.3
87 * 10/17/2013 * 0 * 224 * 1 * 5/29/2014 * 7.5 * 10.3
88 * 10/22/2013 * 0 * 224 * 1 * 6/3/2014 * 7.5 * 10.3
89 * 10/26/2013 * 1 * 260 * 1 * 7/13/2014 * 8.7 * 11.5
90 * 11/9/2013 * 1 * 260 * 1 * 7/27/2014 * 8.7 * 11.5
91 * 11/13/2013 * 1 * 278 * 1 * 8/18/2014 * 9.3 * 12.1
92 * 11/18/2013 * 1 * 278 * 1 * 8/23/2014 * 9.3 * 12.1
93 * 12/15/2013 * 0 * 260 * 1 * 9/1/2014 * 8.7 * 11.5
94 * 12/2/2013 * 1 * 290 * 1 * 9/18/2014 * 9.7 * 12.5
95 * 4/21/2014 * 1 * 151 * 1 * 9/19/2014 * 5 * 7.8
96 * 12/6/2013 * 1 * 290 * 1 * 9/22/2014 * 9.7 * 12.5
97 * 12/11/2013 * 1 * 300 * 1 * 10/7/2014 * 10 * 12.8
98 * 1/20/2014 * 0 * 260 * 1 * 10/7/2014 * 8.7 * 11.5
99 * 5/4/2014 * 1 * 158 * 1 * 10/9/2014 * 5.3 * 8.1
100 * 5/13/2014 * 1 * 158 * 1 * 10/18/2014 * 5.3 * 8.1
101 * 8/31/2014 * 1 * 53 * 1 * 10/23/2014 * 1.8 * 4.6
102 * 9/3/2014 * 1 * 53 * 1 * 10/26/2014 * 1.8 * 4.6
103 * 1/25/2014 * 0 * 278 * 1 * 10/30/2014 * 9.3 * 12.1
104 * 12/29/2013 * 1 * 312 * 1 * 11/6/2014 * 10.4 * 13.2
105 * 5/18/2014 * 1 * 172 * 1 * 11/6/2014 * 5.7 * 8.5
106 * 1/2/2014 * 1 * 315 * 1 * 11/13/2014 * 10.5 * 13.3
107 * 6/1/2014 * 1 * 172 * 1 * 11/20/2014 * 5.7 * 8.5
108 * 1/7/2014 * 1 * 318 * 1 * 11/21/2014 * 10.6 * 13.4
109 * 6/6/2014 * 1 * 175 * 1 * 11/28/2014 * 5.8 * 8.6
110 * 3/2/2014 * 0 * 278 * 1 * 12/5/2014 * 9.3 * 12.1
111 * 9/12/2014 * 1 * 85 * 1 * 12/6/2014 * 2.8 * 5.6
112 * 6/18/2014 * 1 * 182 * 1 * 12/17/2014 * 6.1 * 8.9
113 * 1/29/2014 * 1 * 322 * 1 * 12/17/2014 * 10.7 * 13.5
114 * 2/3/2014 * 1 * 321 * 1 * 12/21/2014 * 10.7 * 13.5
115 * 2/7/2014 * 1 * 323 * 1 * 12/27/2014 * 10.8 * 13.6
116 * 9/24/2014 * 1 * 95 * 1 * 12/28/2014 * 3.2 * 6
117 * 3/16/2014 * 0 * 290 * 1 * 12/31/2014 * 9.7 * 12.5
118 * 3/25/2014 * 0 * 290 * 1 * 1/9/2015 * 9.7 * 12.5
119 * 2/21/2014 * 1 * 324 * 1 * 1/11/2015 * 10.8 * 13.6
120 * 2/25/2014 * 1 * 323 * 1 * 1/14/2015 * 10.8 * 13.6
121 * 10/2/2014 * 1 * 105 * 1 * 1/15/2015 * 3.5 * 6.3
122 * 10/5/2014 * 1 * 105 * 1 * 1/18/2015 * 3.5 * 6.3
123 * 3/7/2014 * 1 * 320 * 1 * 1/21/2015 * 10.7 * 13.5
124 * 10/11/2014 * 1 * 112 * 1 * 1/31/2015 * 3.7 * 6.5
125 * 4/12/2014 * 0 * 300 * 1 * 2/6/2015 * 10 * 12.8
126 * 3/29/2014 * 1 * 325 * 1 * 2/17/2015 * 10.8 * 13.6
127 * 4/3/2014 * 1 * 325 * 1 * 2/22/2015 * 10.8 * 13.6
128 * 4/30/2014 * 0 * 300 * 1 * 2/24/2015 * 10 * 12.8
129 * 11/4/2014 * 1 * 112 * 1 * 2/24/2015 * 3.7 * 6.5
130 * 4/7/2014 * 1 * 325 * 1 * 2/26/2015 * 10.8 * 13.6
131 * 11/7/2014 * 1 * 120 * 1 * 3/7/2015 * 4 * 6.8
132 * 11/13/2014 * 1 * 120 * 1 * 3/13/2015 * 4 * 6.8
133 * 5/9/2014 * 0 * 310 * 1 * 3/15/2015 * 10.3 * 13.1
134 * 11/25/2014 * 1 * 120 * 1 * 3/25/2015 * 4 * 6.8
135 * 10/26/2014 * 0 * 151 * 1 * 3/26/2015 * 5 * 7.8
136 * 10/29/2014 * 0 * 151 * 1 * 3/29/2015 * 5 * 7.8
137 * 5/22/2014 * 0 * 312 * 1 * 3/30/2015 * 10.4 * 13.2
138 * 12/3/2014 * 1 * 120 * 1 * 4/2/2015 * 4 * 6.8
139 * 12/6/2014 * 1 * 122 * 1 * 4/7/2015 * 4.1 * 6.9
140 * 11/1/2014 * 0 * 158 * 1 * 4/8/2015 * 5.3 * 8.1
141 * 12/9/2014 * 1 * 122 * 1 * 4/10/2015 * 4.1 * 6.9
142 * 11/10/2014 * 0 * 158 * 1 * 4/17/2015 * 5.3 * 8.1
143 * 12/18/2014 * 1 * 123 * 1 * 4/20/2015 * 4.1 * 6.9
144 * 6/9/2014 * 0 * 315 * 1 * 4/20/2015 * 10.5 * 13.3
145 * 11/19/2014 * 0 * 158 * 1 * 4/26/2015 * 5.3 * 8.1
146 * 6/12/2014 * 0 * 318 * 1 * 4/26/2015 * 10.6 * 13.4
147 * 6/15/2014 * 0 * 319 * 1 * 4/30/2015 * 10.6 * 13.4
148 * 12/27/2014 * 1 * 126 * 1 * 5/2/2015 * 4.2 * 7
149 * 11/22/2014 * 0 * 172 * 1 * 5/13/2015 * 5.7 * 8.5
150 * 12/1/2014 * 0 * 172 * 1 * 5/22/2015 * 5.7 * 8.5
151 * 7/6/2014 * 0 * 320 * 1 * 5/22/2015 * 10.7 * 13.5
152 * 12/30/2014 * 1 * 151 * 1 * 5/30/2015 * 5 * 7.8
153 * 7/15/2014 * 0 * 322 * 1 * 6/2/2015 * 10.7 * 13.5
154 * 1/5/2015 * 1 * 151 * 1 * 6/5/2015 * 5 * 7.8
155 * 12/15/2014 * 0 * 172 * 1 * 6/5/2015 * 5.7 * 8.5
156 * 7/24/2014 * 0 * 321 * 1 * 6/10/2015 * 10.7 * 13.5
157 * 7/27/2014 * 0 * 323 * 1 * 6/15/2015 * 10.8 * 13.6
158 * 12/24/2014 * 0 * 175 * 1 * 6/17/2015 * 5.8 * 8.6
159 * 1/14/2015 * 1 * 158 * 1 * 6/21/2015 * 5.3 * 8.1
160 * 1/2/2015 * 0 * 175 * 1 * 6/26/2015 * 5.8 * 8.6
161 * 6/21/2014 * 1 * 380 * 1 * 7/6/2015 * 12.7 * 15.5
162 * 1/8/2015 * 0 * 182 * 1 * 7/9/2015 * 6.1 * 8.9
163 * 6/27/2014 * 1 * 380 * 1 * 7/12/2015 * 12.7 * 15.5
164 * 1/26/2015 * 1 * 172 * 1 * 7/17/2015 * 5.7 * 8.5
165 * 8/28/2014 * 0 * 324 * 1 * 7/18/2015 * 10.8 * 13.6
166 * 1/29/2015 * 1 * 172 * 1 * 7/20/2015 * 5.7 * 8.5
167 * 2/1/2015 * 1 * 172 * 1 * 7/23/2015 * 5.7 * 8.5
168 * 9/6/2014 * 0 * 324 * 1 * 7/27/2015 * 10.8 * 13.6
169 * 7/3/2014 * 1 * 390 * 1 * 7/28/2015 * 13 * 15.8
170 * 9/18/2014 * 0 * 320 * 1 * 8/4/2015 * 10.7 * 13.5
171 * 9/15/2014 * 0 * 323 * 1 * 8/4/2015 * 10.8 * 13.6
172 * 2/13/2015 * 1 * 175 * 1 * 8/7/2015 * 5.8 * 8.6
173 * 9/27/2014 * 0 * 320 * 1 * 8/13/2015 * 10.7 * 13.5
174 * 9/30/2014 * 0 * 325 * 1 * 8/21/2015 * 10.8 * 13.6
175 * 2/25/2015 * 1 * 182 * 1 * 8/26/2015 * 6.1 * 8.9
176 * 10/17/2014 * 0 * 325 * 1 * 9/7/2015 * 10.8 * 13.6
177 * 10/20/2014 * 0 * 325 * 1 * 9/10/2015 * 10.8 * 13.6
178 * 7/9/2014 * 1 * 430 * 1 * 9/12/2015 * 14.3 * 17.1
179 * 10/23/2014 * 0 * 325 * 1 * 9/13/2015 * 10.8 * 13.6
180 * 7/12/2014 * 1 * 430 * 1 * 9/15/2015 * 14.3 * 17.1
181 * 3/3/2015 * 1 * 200 * 1 * 9/19/2015 * 6.7 * 9.5
182 * 3/6/2015 * 1 * 200 * 1 * 9/22/2015 * 6.7 * 9.5
183 * 7/21/2014 * 1 * 435 * 1 * 9/29/2015 * 14.5 * 17.3
184 * 3/15/2015 * 1 * 206 * 1 * 10/7/2015 * 6.9 * 9.7
185 * 8/1/2014 * 1 * 440 * 1 * 10/15/2015 * 14.7 * 17.5
186 * 3/21/2015 * 1 * 210 * 1 * 10/17/2015 * 7 * 9.8
187 * 3/24/2015 * 1 * 224 * 1 * 11/3/2015 * 7.5 * 10.3
188 * 3/27/2015 * 1 * 224 * 1 * 11/6/2015 * 7.5 * 10.3
189 * 8/4/2014 * 1 * 470 * 1 * 11/17/2015 * 15.7 * 18.5
190 * 8/7/2014 * 1 * 470 * 1 * 11/20/2015 * 15.7 * 18.5
191 * 8/16/2014 * 1 * 480 * 1 * 12/9/2015 * 16 * 18.8
192 * 4/8/2015 * 1 * 260 * 1 * 12/24/2015 * 8.7 * 11.5
193 * 7/28/2015 * 1 * 151 * 1 * 12/26/2015 * 5 * 7.8
194 * 8/3/2015 * 1 * 158 * 1 * 1/8/2016 * 5.3 * 8.1
195 * 8/9/2015 * 1 * 158 * 1 * 1/14/2016 * 5.3 * 8.1
196 * 4/17/2015 * 1 * 278 * 1 * 1/20/2016 * 9.3 * 12.1
197 * 8/19/2014 * 1 * 520 * 1 * 1/21/2016 * 17.3 * 20.1
198 * 1/20/2015 * 0 * 380 * 1 * 2/4/2016 * 12.7 * 15.5
199 * 4/23/2015 * 1 * 290 * 1 * 2/7/2016 * 9.7 * 12.5
200 * 1/23/2015 * 0 * 380 * 1 * 2/7/2016 * 12.7 * 15.5
201 * 4/26/2015 * 1 * 290 * 1 * 2/10/2016 * 9.7 * 12.5
202 * 2/7/2015 * 0 * 380 * 1 * 2/22/2016 * 12.7 * 15.5
203 * 5/2/2015 * 1 * 300 * 1 * 2/26/2016 * 10 * 12.8
204 * 2/10/2015 * 0 * 390 * 1 * 3/6/2016 * 13 * 15.8
205 * 2/16/2015 * 0 * 390 * 1 * 3/12/2016 * 13 * 15.8
206 * 5/8/2015 * 1 * 312 * 1 * 3/15/2016 * 10.4 * 13.2
207 * 5/11/2015 * 1 * 315 * 1 * 3/21/2016 * 10.5 * 13.3
208 * 5/14/2015 * 1 * 318 * 1 * 3/27/2016 * 10.6 * 13.4
209 * 5/26/2015 * 1 * 320 * 1 * 4/10/2016 * 10.7 * 13.5
210 * 6/7/2015 * 1 * 321 * 1 * 4/23/2016 * 10.7 * 13.5
211 * 2/19/2015 * 0 * 430 * 1 * 4/24/2016 * 14.3 * 17.1
212 * 2/28/2015 * 0 * 430 * 1 * 5/3/2016 * 14.3 * 17.1
213 * 6/16/2015 * 1 * 324 * 1 * 5/5/2016 * 10.8 * 13.6
214 * 6/22/2015 * 1 * 324 * 1 * 5/11/2016 * 10.8 * 13.6
215 * 6/28/2015 * 1 * 320 * 1 * 5/13/2016 * 10.7 * 13.5
216 * 3/9/2015 * 0 * 435 * 1 * 5/17/2016 * 14.5 * 17.3
217 * 7/13/2015 * 1 * 325 * 1 * 6/2/2016 * 10.8 * 13.6
218 * 7/16/2015 * 1 * 325 * 1 * 6/5/2016 * 10.8 * 13.6
219 * 3/30/2015 * 0 * 435 * 1 * 6/7/2016 * 14.5 * 17.3
220 * 7/19/2015 * 1 * 325 * 1 * 6/8/2016 * 10.8 * 13.6
221 * 4/2/2015 * 0 * 440 * 1 * 6/15/2016 * 14.7 * 17.5
222 * 4/14/2015 * 0 * 440 * 1 * 6/27/2016 * 14.7 * 17.5
223 * 4/20/2015 * 0 * 470 * 1 * 8/2/2016 * 15.7 * 18.5
224 * 8/12/2015 * 1 * 380 * 1 * 8/26/2016 * 12.7 * 15.5
225 * 5/17/2015 * 0 * 470 * 1 * 8/29/2016 * 15.7 * 18.5
226 * 8/25/2014 * 1 * 740 * 1 * 9/3/2016 * 24.7 * 27.5
227 * 5/23/2015 * 0 * 480 * 1 * 9/14/2016 * 16 * 18.8
228 * 8/24/2015 * 1 * 390 * 1 * 9/17/2016 * 13 * 15.8
229 * 5/29/2015 * 0 * 480 * 1 * 9/20/2016 * 16 * 18.8
230 * 8/27/2015 * 1 * 390 * 1 * 9/20/2016 * 13 * 15.8
231 * 6/1/2015 * 0 * 520 * 1 * 11/2/2016 * 17.3 * 20.1
232 * 8/30/2015 * 1 * 430 * 1 * 11/2/2016 * 14.3 * 17.1
233 * 9/5/2015 * 1 * 435 * 1 * 11/13/2016 * 14.5 * 17.3
234 * 9/11/2015 * 1 * 440 * 1 * 11/24/2016 * 14.7 * 17.5
235 * 9/14/2015 * 1 * 440 * 1 * 11/27/2016 * 14.7 * 17.5
236 * 9/17/2015 * 1 * 470 * 1 * 12/30/2016 * 15.7 * 18.5
237 * 9/29/2015 * 1 * 480 * 1 * 1/21/2017 * 16 * 18.8
238 * 8/13/2014 * 0 * 902.2048193 * 1 * 2/1/2017 * 30.1 * 32.9
239 * 10/5/2015 * 0 * 485 * 0 * 2/1/2017 * 16.2 * 19
240 * 9/23/2015 * 0 * 497 * 0 * 2/1/2017 * 16.6 * 19.4
241 * 9/20/2015 * 0 * 500 * 0 * 2/1/2017 * 16.7 * 19.5
242 * 8/21/2015 * 0 * 530 * 0 * 2/1/2017 * 17.7 * 20.5
243 * 8/18/2015 * 0 * 533 * 0 * 2/1/2017 * 17.8 * 20.6
244 * 8/6/2015 * 0 * 545 * 0 * 2/1/2017 * 18.2 * 21
245 * 7/22/2015 * 0 * 560 * 0 * 2/1/2017 * 18.7 * 21.5
246 * 7/10/2015 * 0 * 572 * 0 * 2/1/2017 * 19.1 * 21.9
247 * 7/7/2015 * 0 * 575 * 0 * 2/1/2017 * 19.2 * 22
248 * 7/4/2015 * 0 * 578 * 0 * 2/1/2017 * 19.3 * 22.1
249 * 6/19/2015 * 0 * 593 * 0 * 2/1/2017 * 19.8 * 22.6
250 * 6/4/2013 * 1 * 1337.95122 * 1 * 2/1/2017 * 44.6 * 47.4
251 * 6/3/2014 * 1 * 973.0481928 * 1 * 2/1/2017 * 32.4 * 35.2
252 * 11/16/2014 * 1 * 807.746988 * 1 * 2/1/2017 * 26.9 * 29.7
253 * 2/22/2015 * 1 * 710 * 1 * 2/1/2017 * 23.7 * 26.5
254 * 4/11/2015 * 1 * 662 * 1 * 2/1/2017 * 22.1 * 24.9
255 * 7/25/2015 * 1 * 557 * 1 * 2/1/2017 * 18.6 * 21.4
256 * 10/2/2015 * 1 * 488 * 1 * 2/1/2017 * 16.3 * 19.1
257 * 8/18/2008 * 1 * 3089 * 0 * 2/1/2017 * 103 * 105.8
258 * 9/1/2008 * 1 * 3075 * 0 * 2/1/2017 * 102.5 * 105.3
259 * 10/22/2008 * 1 * 3024 * 0 * 2/1/2017 * 100.8 * 103.6
260 * 1/21/2009 * 1 * 2933 * 0 * 2/1/2017 * 97.8 * 100.6
261 * 4/15/2009 * 1 * 2849 * 0 * 2/1/2017 * 95 * 97.8
262 * 10/11/2011 * 1 * 1939.2 * 0 * 2/1/2017 * 64.6 * 67.4
263 * 1/17/2012 * 1 * 1842 * 0 * 2/1/2017 * 61.4 * 64.2
264 * 1/27/2012 * 1 * 1831.2 * 0 * 2/1/2017 * 61 * 63.8
265 * 4/12/2012 * 1 * 1755.6 * 0 * 2/1/2017 * 58.5 * 61.3
266 * 4/23/2012 * 1 * 1744.8 * 0 * 2/1/2017 * 58.2 * 61
267 * 7/7/2012 * 1 * 1669.2 * 0 * 2/1/2017 * 55.6 * 58.4
268 * 7/18/2012 * 1 * 1658.4 * 0 * 2/1/2017 * 55.3 * 58.1
269 * 10/2/2012 * 1 * 1582.8 * 0 * 2/1/2017 * 52.8 * 55.6
270 * 10/13/2012 * 1 * 1572 * 0 * 2/1/2017 * 52.4 * 55.2
271 * 12/27/2012 * 1 * 1496.4 * 0 * 2/1/2017 * 49.9 * 52.7
272 * 1/7/2013 * 1 * 1485.6 * 0 * 2/1/2017 * 49.5 * 52.3
273 * 3/13/2013 * 1 * 1420.8 * 0 * 2/1/2017 * 47.4 * 50.2
274 * 3/24/2013 * 1 * 1410 * 0 * 2/1/2017 * 47 * 49.8
275 * 6/8/2013 * 1 * 1333.426829 * 0 * 2/1/2017 * 44.4 * 47.2
276 * 6/26/2013 * 1 * 1315.329268 * 0 * 2/1/2017 * 43.8 * 46.6
277 * 7/1/2013 * 1 * 1310.804878 * 0 * 2/1/2017 * 43.7 * 46.5
278 * 7/28/2013 * 1 * 1283.658537 * 0 * 2/1/2017 * 42.8 * 45.6
279 * 8/1/2013 * 1 * 1279.134146 * 0 * 2/1/2017 * 42.6 * 45.4
280 * 8/24/2013 * 1 * 1256.512195 * 0 * 2/1/2017 * 41.9 * 44.7
281 * 8/29/2013 * 1 * 1251.987805 * 0 * 2/1/2017 * 41.7 * 44.5
282 * 9/25/2013 * 1 * 1224.841463 * 0 * 2/1/2017 * 40.8 * 43.6
283 * 9/29/2013 * 1 * 1220.317073 * 0 * 2/1/2017 * 40.7 * 43.5
284 * 10/31/2013 * 1 * 1188.646341 * 0 * 2/1/2017 * 39.6 * 42.4
285 * 11/4/2013 * 1 * 1184.121951 * 0 * 2/1/2017 * 39.5 * 42.3
286 * 11/22/2013 * 1 * 1166.02439 * 0 * 2/1/2017 * 38.9 * 41.7
287 * 11/27/2013 * 1 * 1161.5 * 0 * 2/1/2017 * 38.7 * 41.5
288 * 12/20/2013 * 1 * 1138.878049 * 0 * 2/1/2017 * 38 * 40.8
289 * 12/24/2013 * 1 * 1134.353659 * 0 * 2/1/2017 * 37.8 * 40.6
290 * 1/11/2014 * 1 * 1116.256098 * 0 * 2/1/2017 * 37.2 * 40
291 * 1/16/2014 * 1 * 1111.731707 * 0 * 2/1/2017 * 37.1 * 39.9
292 * 2/12/2014 * 1 * 1084.585366 * 0 * 2/1/2017 * 36.2 * 39
293 * 2/16/2014 * 1 * 1080.060976 * 0 * 2/1/2017 * 36 * 38.8
294 * 3/11/2014 * 1 * 1057.439024 * 0 * 2/1/2017 * 35.2 * 38
295 * 3/20/2014 * 1 * 1048.390244 * 0 * 2/1/2017 * 34.9 * 37.7
296 * 4/16/2014 * 1 * 1021.243902 * 0 * 2/1/2017 * 34 * 36.8
297 * 4/25/2014 * 1 * 1012.195122 * 0 * 2/1/2017 * 33.7 * 36.5
298 * 5/27/2014 * 1 * 980.5243902 * 0 * 2/1/2017 * 32.7 * 35.5
299 * 6/24/2014 * 1 * 952.3855422 * 0 * 2/1/2017 * 31.7 * 34.5
300 * 6/30/2014 * 1 * 946.4819277 * 0 * 2/1/2017 * 31.5 * 34.3
301 * 7/18/2014 * 1 * 928.7710843 * 0 * 2/1/2017 * 31 * 33.8
302 * 7/30/2014 * 1 * 916.9638554 * 0 * 2/1/2017 * 30.6 * 33.4
303 * 8/10/2014 * 1 * 905.1566265 * 0 * 2/1/2017 * 30.2 * 33
304 * 8/22/2014 * 1 * 893.3493976 * 0 * 2/1/2017 * 29.8 * 32.6
305 * 9/9/2014 * 1 * 875.6385542 * 0 * 2/1/2017 * 29.2 * 32
306 * 9/21/2014 * 1 * 863.8313253 * 0 * 2/1/2017 * 28.8 * 31.6
307 * 10/8/2014 * 1 * 846.1204819 * 0 * 2/1/2017 * 28.2 * 31
308 * 10/14/2014 * 1 * 840.2168675 * 0 * 2/1/2017 * 28 * 30.8
309 * 11/28/2014 * 1 * 795.939759 * 0 * 2/1/2017 * 26.5 * 29.3
310 * 12/12/2014 * 1 * 781.1807229 * 0 * 2/1/2017 * 26 * 28.8
311 * 12/21/2014 * 1 * 772.3253012 * 0 * 2/1/2017 * 25.7 * 28.5
312 * 1/11/2015 * 1 * 751.6626506 * 0 * 2/1/2017 * 25.1 * 27.9
313 * 1/17/2015 * 1 * 745.7590361 * 0 * 2/1/2017 * 24.9 * 27.7
314 * 2/4/2015 * 1 * 728 * 0 * 2/1/2017 * 24.3 * 27.1
315 * 3/12/2015 * 1 * 692 * 0 * 2/1/2017 * 23.1 * 25.9
316 * 3/18/2015 * 1 * 686 * 0 * 2/1/2017 * 22.9 * 25.7
317 * 4/5/2015 * 1 * 668 * 0 * 2/1/2017 * 22.3 * 25.1
318 * 4/29/2015 * 1 * 644 * 0 * 2/1/2017 * 21.5 * 24.3
319 * 5/5/2015 * 1 * 638 * 0 * 2/1/2017 * 21.3 * 24.1
320 * 5/20/2015 * 1 * 623 * 0 * 2/1/2017 * 20.8 * 23.6
321 * 6/4/2015 * 1 * 608 * 0 * 2/1/2017 * 20.3 * 23.1
322 * 6/25/2015 * 1 * 587 * 0 * 2/1/2017 * 19.6 * 22.4
323 * 7/1/2015 * 1 * 581 * 0 * 2/1/2017 * 19.4 * 22.2
324 * 7/31/2015 * 1 * 551 * 0 * 2/1/2017 * 18.4 * 21.2
325 * 8/15/2015 * 1 * 536 * 0 * 2/1/2017 * 17.9 * 20.7
326 * 9/2/2015 * 1 * 518 * 0 * 2/1/2017 * 17.3 * 20.1
327 * 9/8/2015 * 1 * 512 * 0 * 2/1/2017 * 17.1 * 19.9
328 * 9/26/2015 * 1 * 494 * 0 * 2/1/2017 * 16.5 * 19.3
329 * 6/10/2015 * 0 * 610 * 1 * 2/9/2017 * 20.3 * 23.1
330 * 6/13/2015 * 0 * 740 * 1 * 6/22/2017 * 24.7 * 27.5
331 * 10/15/2015 * 1 * 740 * 1 * 10/24/2017 * 24.7 * 27.5
Yes correct, no crossover effect with PFS curves . . . much cleaner than OS. curves.
AP
I want it noted that this data was given here before anything disclosed at Bosch's meeting today.
https://dl.dropboxusercontent.com/u/11047753/PFS%20NWBO%20COMPARE%20plus%2083.JPG
Even Big Pharma cannot fight curves like this.
This model is the PFS primary endpoint curves.
https://dl.dropboxusercontent.com/u/11047753/PFS%20NWBO%20COMPARE%20plus%2083.JPG
The actual data cannot mathematically be substantially different from this based upon already publically disclosed information.
To those who care
I am giving you a look at the future based upon publically disclosed data.
https://dl.dropboxusercontent.com/u/11047753/PFS%20NWBO%20COMPARE%20plus%2083.JPG
DCVax-L GBM trial Result PFS Model based on IMUC Control DATA
Discloser: I am long this stock; I have no inside information; I am a clinical researcher.
Hypothesis 1: The Control Patients in the DCVax-L GBM trial should relapse at a rate similar to what was shown in the IMUC trial. The IMUC trial is the most similar to the DCVax-L GBM trial in terms of eligibility entry criteria. The progression free survival (PFS) was reported recently at the 2014 American Society of Clinical Oncology (ASCO) meeting.
https://dl.dropboxusercontent.com/u/11047753/bosch%20nwbo%203.PNG
In order to compare the IMUC Trial to the current DCVax-L trial control patients, however, one must add 83 days to the IMUC results because the IMUC data time 0 for PFS was upon first administration of immunotherapy (ie after surgery and RT) whereas the DCVax-L Trial (and every other major trial in this disease) calculated the start day 0 for the PFS curves at the date of surgery.
Hypothesis 2: Given that we know the number of PFS events in the total DCVax-L trial at this time (first interim analysis) is 66 December 2013;
https://dl.dropboxusercontent.com/u/11047753/nwbo%2066%20events.JPG
And NWBO reported 248 PFS events on before 2/1/2017
https://dl.dropboxusercontent.com/u/11047753/bosch%20nwbo%201.PNG
Then one can model using JMP(SAS) software, model the control arm PFS for the DCVaX-L trial to be the same as the corrected IMUC trial results and thus obtain an estimate of the probable number of events required in the control arm of the DCVax-L. When one does this with a Kaplan Meier plot, one obtains a result of 100 events in the control arm with the total number of patients in the control arm of 111. Then one can deduce that the number of events in the experimental arm for the DCVax-L trial would be 331- 111 or 148 events.
Hypothesis 3: Using the derived 148 PFS events in the experimental arm, one can model the experimental arm to be similar to the IMUC control arm trial results but, in this case only allow for just 148 events out of 220 total patients in the DCVax-L experimental arm.
When one does this with a Kaplan Meier plot, the data and curves are shown below. When one applies log-rank and Wilcoxon significance testing to the two curves one obtains a significant result of p<.0001
https://dl.dropboxusercontent.com/u/11047753/PFS%20NWBO%20COMPARE%20plus%2083.JPG
My best-educated hypothesis is that clearly, the DCVax-L trial is going to return a positive result on the primary end point
This result will be a practice changing result. Especially when one sees the plateau in the treatment curve. The nice thing about this PFS review also is that there is no crossover effect between the curves such as could occur with my prior overall survival analysis OS.
129619 Alphapuppy Ihub post
Just to put this into perspective the Stupp GMB trial PFS only increased 2 months by the addition of temazolamide (TMZ) (5 to 6.9 months). Yet this became almost immediately, the new gold standard of care in this disease and TMZ almost immediately became a blockbuster multi-billion dollar drug.
DCVaX-L increases in the operable patients even more than this and with none of the chemotherapy side effects and will quickly be the new standard of care in this disease.
Ex
When doing models such as this in is important to visualize the forest and not get swamped with the individual trees.
My model has data for all 331 patients. Statistic will never tell you how an individual will do, but only will tell you how a population of people will do.
My model tells the story that the treated experimental arm Population in the DCVax-L trial likely will provide at least a 4 month improvement in median overall survival with a statistically significant p value.
Like you, I also like the work of the Lepracaun and look at his work as another corroborating data set.
Alpha
aboob
I thought the IMUC data set was the best because this trial had published results one could use the IMUC survival curve to determine the events in the control arm and the entry requirements for the IMUC were very similar to the NWBO trial.
The Stupp Trial would not be good because Stupp had 18% of patients that had biopsy only and did not have a Gross total or near total resection.
Also the fact that IMUC is valued at 2.5 million now has no relevance regarding the quality of their dataset.
The analysis of mine and others further reinforces my belief that we are looking at a positive trial for DCVax-L.
As an investor I think that this is a great opportunity to purchase shares of a company before the good news is released with associated potential share appreciation.
Alpha
Ex
Doc is correct.
This model uses the IMUC ITT survival curve as its basis. The only data points that we know for sure is the enrolment ramp and we know that exactly courtesy of NWBO and Dr. Bosch. Also we know that there were 100 patients alive when Dr. Bosch have his ASCO review.
This is the IMUC curve that I used to base my model.
https://dl.dropboxusercontent.com/u/11047753/IMUC%20ITT%20GRAPH.JPG
With the enrolment ramp, each Patient was assigned an enrolment date.
A spreadsheet was developed with each row representing a patient randomized on the trial. There were 331 data rows. Patients where then assigned to treatment (experimental) vs Placebo arms in a ratio of 2 to 1 as per the protocol requires.
The event data from the IMUC control curve was expanded by ratios to represent the NWBO control MODEL. (since there were more patients on the NWBO trial than were on the IMUC trial). When one does this 87 events are required for the NWBO curve to be equal to the IMUC control curve. This is done manually by looking and measuring from the curve. The IMUC time data is in Days and that was converted to months for the NWBO model.
IMPORTANT: Please note that in the IMUC control curve there where no data points past 950 Days or 31.7 months. Therefore with this model nothing past 31.7 months would be accurate. In order to complete the
KM curve, one could not have any blanks so in my initial review for these not evented cases, I just subtracted the date of enrollment to the date of Bosch's 100 still alive talk. Since this NWBO trial has been going on for over a decade that gave some large follow-up numbers for some patients. But these did not affect this models outcome.
Below I adjusted this and the following link provides the updated curves for the NWBO survival model based upon IMUC dataset. Essentially nothing changes except there are no data points past 35 months because with this model we have no idea what is happening in that part of the curve.
Link for the new curve.
https://dl.dropboxusercontent.com/u/11047753/Comparison%20Curve.JPG
The significant p value of .029 is still applicable.
Also with this model the Mean survival data is not a meaningful measure and should not be used in anyway since we do not know the amount of follow-up time for those cases that did not event yet.
Basically these curves show a reasonable model for the potential outcome of the trial up to 30 months time. After that this model is not able to predict the survival.
Basically, these curves shows that there are less events in the experimental arm than what would be predicted if the experimental had a survival outcome the same as the IMUC control curve.
Hope this helps you understand this model better.
Alpha
Ex
Thanks for your review. I welcome both pos and neg comments as I look at as a form of peer review.
I will be formulating a response and include all my methods and data. The problem is the spreadsheets are massive and cannot be posted so I will use some links.
The tail of this model would have a higher uncertainty since I used the IMUC ITT (intent to Treat) survival curves as the initial basis of this model.
I have previously posted a review of the tail of the NWBO trial using a different method.
Please review this post. 121596
Still the model shows statistically significant survival improvement for the experimental arm
Alpha
Thanks Flip
I appreciate the input. I agree that this model is conservative and the likely actual results will exceed these presented. I was really worried when I hit the calculate button on the statistics program that my model was not going to be statistically significant but even with the potential flaws, still the curves returned a significant P value in favor of the treatment arm.
Alpha
A14
4 months is tremendous for GBM (the emperor of all cancers).
Look if memory serves for the Optune trial the improvement was < 4months and it was approved and now is used in the clinic.
I don't think DCVaX-L will be a total cure but perhaps with combo therapies we will shift the survival curves more to the right.
Alpha
Kabunusi
Basically it has to do with the amount of follow up a patient has that has not evented (ie has not died). The alive patients are censored after the time exceeds their particular follow-up time. So their survival will contribute to the survival curve for only as long as there follow-up time.
One can calculate all this by hand but now computers do it in a flash.
Alpha
Kabunushi
Correct the model does not account for, nor could it account for cross-over affects. There are limitations but this is the best model one can make given the limited number of data points available. Since both the control and the experimental groups where modeled after the IMUC control curve dataset, I believe that likely the real trial outcome would be better than this for the whole NWBO group.
Alpha
Root
Yes it is a positive model;
I would welcome comments from the likes of flipper, abeta, senti, and that leprechaun guy.
Here is a link for a word format which might be easier to read.
https://dl.dropboxusercontent.com/u/11047753/NWBO%20DCVax%20MODEL%20REVIEW.docx
Alpha
AI4
Basically the model shows a 4 month statistically significant overall survival improvement with the DCVax-L Treatment.
Additionally it shows that the treated patients have a long survival tail.
So in a nutshell the treatment appears to work.
Alpha
JT It is XLSTAT and add on plugin to Excel This took me 4 nights to completed as the program is not all that user friendly.
Alpha
NWBO DCVax-L Survival Analysis Models show significant survival benefit
Finally I have finished the Survival Model for the DCVax-L Trial.
Model Parameters:
1. First developed a spreadsheet using the entry ramp published at ASCO 2017 by Bousch MD and NWBO.
2.Assigned in a 2 to 1 ratio the experimental and Placebo groups.
3. Used the IMUC Published ITT survival curves (published at ASCO 2014) to extrapolate to the NWBO dataset. The primary assumption for this model is that the IMUC Control Survival will be essentially equal to the NWBO Control Survival Curve. This is a reasonable assumption as the entry requirements for the IMUC trial and the NWBO trial are very similar.
4 The model for the NWBO control patients data to be equivalent to the IMUC Control Data, requires 87 death events to for the NWBO control group. It was disclosed at ASCO 2017 that there were still 100 Patients alive in the entire NWBO dataset. Therefore, there would be 331-100 or 231 total death events for the entire NWBO dataset. Utilizing the derived 87 death events from the model, one then can ascertain that there would be 144 death events in the DCVax-L treatment group (ie 231-87=144).
5. Armed with this derived number of events (144) in the experimental arm, one can model the experimental arm by modeling the NWBO data using the IMUC Control arm date but limiting the number of death events to only 144.
6. One then can then compare the overall survival KM survival curves for the DCVax-L experimental group (N=221) and the Placebo group (N=110).
When one does this the results show a statistically significant (p=.029) improvement in OS favoring the treatment group with a Median OS of 19 vs 15 months and a 75 Quartile survival not yet obtained by the experimental group.
To visualize the Survival Curve click on this link.
https://dl.dropboxusercontent.com/u/11047753/NWBO%20Survival%20Curve.JPG
Summary statistics (Events):
Stratum Total observed Total failed Total censored Time steps
1 DCVAX-L 221 141 80 164
2 PLACEBO 110 87 23 79
Test of equality of the survival distribution functions (DF = 1):
COMPARISION OF GROUPS
Statistic Observed value Critical value p-value alpha
Log-rank 5.474 3.841 0.019 0.050
Wilcoxon 4.784 3.841 0.029 0.050
Tarone-Ware 5.268 3.841 0.022 0.050
NWBO CONTROL Patients DATA:
Results for 2: PLACEBO
Summary statistics (2):
Total observed Total failed Total censored
110 87 23
Mean survival time (2):
Mean survival NWBO Control time (IMUC Control Mos Fup or death expanded<107.255333333333) Standard deviation Lower bound (95%) Upper bound (95%)
33.998 3.633 26.878 41.119
Quantiles estimation (2):
Quantile Estimate Lower bound (95%) Upper bound (95%)
75% 32.000 19.667
50% 15.167 13.333 17.833
25% 10.600 10.333 12.000
Kaplan-Meier table (2): NWBO CONTROL PATIENTS
IMUC Control Mos Fup or death expanded At risk Failed Censored Proportion failed Survival rate Survival distribution function
6.666667 110 1 0 0.009 0.991 0.991
6.8 109 1 0 0.009 0.991 0.982
7.333333 108 2 0 0.019 0.981 0.964
7.833333 106 1 0 0.009 0.991 0.955
8.333333 105 3 0 0.029 0.971 0.927
8.5 102 2 0 0.020 0.980 0.909
8.533333 100 1 0 0.010 0.990 0.900
8.666667 99 2 0 0.020 0.980 0.882
9.4 97 1 0 0.010 0.990 0.873
10.13333 96 1 0 0.010 0.990 0.864
10.26667 95 3 0 0.032 0.968 0.836
10.3 92 1 0 0.011 0.989 0.827
10.33333 91 1 0 0.011 0.989 0.818
10.4 90 4 0 0.044 0.956 0.782
10.5 86 1 0 0.012 0.988 0.773
10.56667 85 1 0 0.012 0.988 0.764
10.6 84 2 0 0.024 0.976 0.745
10.7 82 1 0 0.012 0.988 0.736
10.8 81 2 0 0.025 0.975 0.718
11 79 1 0 0.013 0.987 0.709
11.33333 78 2 0 0.026 0.974 0.691
11.5 76 1 0 0.013 0.987 0.682
11.66667 75 2 0 0.027 0.973 0.664
12 73 1 0 0.014 0.986 0.655
12.33333 72 1 0 0.014 0.986 0.645
12.85 71 1 0 0.014 0.986 0.636
13.16667 70 2 0 0.029 0.971 0.618
13.25 68 1 0 0.015 0.985 0.609
13.33333 67 2 0 0.030 0.970 0.591
13.38333 65 1 0 0.015 0.985 0.582
13.43333 64 1 0 0.016 0.984 0.573
13.46667 63 1 0 0.016 0.984 0.564
13.5 62 2 0 0.032 0.968 0.545
13.75 60 1 0 0.017 0.983 0.536
14 59 2 0 0.034 0.966 0.518
15 57 2 0 0.035 0.965 0.500
15.16667 55 1 0 0.018 0.982 0.491
15.33333 54 2 0 0.037 0.963 0.473
15.5 52 1 0 0.019 0.981 0.464
15.66667 51 1 0 0.020 0.980 0.455
16.33333 50 1 0 0.020 0.980 0.445
17 49 2 0 0.041 0.959 0.427
17.41667 47 1 0 0.021 0.979 0.418
17.83333 46 2 0 0.043 0.957 0.400
18.33333 44 1 0 0.023 0.977 0.391
18.83333 43 1 0 0.023 0.977 0.382
19.08333 42 1 0 0.024 0.976 0.373
19.33333 41 2 0 0.049 0.951 0.355
19.5 39 1 0 0.026 0.974 0.345
19.66667 38 2 0 0.053 0.947 0.327
21.3 36 0 1
23.33333 35 1 0 0.029 0.971 0.318
23.36667 34 1 0 0.029 0.971 0.309
23.4 33 2 0 0.061 0.939 0.290
23.5 31 1 0 0.032 0.968 0.281
27.66667 30 1 0 0.033 0.967 0.271
32 29 7 0 0.241 0.759 0.206
34 22 0 1
105.1463 21 0 1
105.2573 20 0 1
105.3683 19 0 1
105.4793 18 0 1
105.5903 17 0 1
105.7013 16 0 1
105.8123 15 0 1
105.9233 14 0 1
106.0343 13 0 1
106.1453 12 0 1
106.2563 11 0 1
106.3673 10 0 1
106.4783 9 0 1
106.5893 8 0 1
106.7003 7 0 1
106.8113 6 0 1
106.9223 5 0 1
107.0333 4 0 1
107.1443 3 0 1
107.2553 2 0 1
107.3943 1 0 1
NWBO EXPERIMENTAL DCVaX-L Treatment ARM DATA:
Results for 1:DCVAC-L TREATMENT Experimental Group
Summary statistics (1):
Total observed Total failed Total censored
221 141 80
Mean survival time (1):
Mean survival time (IMUC Control Mos Fup or death expanded<35.6146666666648) Standard deviation Lower bound (95%) Upper bound (95%)
22.236 0.744 20.777 23.695
Quantiles estimation (1):
QuantileEstimate Lower bound (95%) Upper bound (95%)
75%
50% 18.967 15.667 23.400
25% 11.500 10.600 13.250
IMUC Control Mos Fup or death expanded At risk Failed Censored Proportion failed Survival rate Survival distribution function Standard error of the survival function Lower bound (95%) Upper bound (95%)
6.666667 221 1 0 0.005 0.995 0.995 0.005 0.987 1.000
6.8 220 1 0 0.005 0.995 0.991 0.006 0.978 1.000
7 219 1 0 0.005 0.995 0.986 0.008 0.971 1.000
7.333333 218 2 0 0.009 0.991 0.977 0.010 0.958 0.997
7.6 216 1 0 0.005 0.995 0.973 0.011 0.951 0.994
7.833333 215 1 0 0.005 0.995 0.968 0.012 0.945 0.991
8.333333 214 4 0 0.019 0.981 0.950 0.015 0.922 0.979
8.4 210 1 0 0.005 0.995 0.946 0.015 0.916 0.976
8.466667 209 1 0 0.005 0.995 0.941 0.016 0.910 0.972
8.5 208 3 0 0.014 0.986 0.928 0.017 0.893 0.962
8.533333 205 1 0 0.005 0.995 0.923 0.018 0.888 0.958
8.666667 204 3 0 0.015 0.985 0.910 0.019 0.872 0.947
9.4 201 1 0 0.005 0.995 0.905 0.020 0.866 0.944
9.733333 200 1 0 0.005 0.995 0.900 0.020 0.861 0.940
10.13333 199 1 0 0.005 0.995 0.896 0.021 0.856 0.936
10.26667 198 5 0 0.025 0.975 0.873 0.022 0.829 0.917
10.3 193 2 0 0.010 0.990 0.864 0.023 0.819 0.909
10.33333 191 2 0 0.010 0.990 0.855 0.024 0.809 0.902
10.4 189 5 0 0.026 0.974 0.833 0.025 0.783 0.882
10.46667 184 2 0 0.011 0.989 0.824 0.026 0.773 0.874
10.5 182 1 0 0.005 0.995 0.819 0.026 0.768 0.870
10.56667 181 1 0 0.006 0.994 0.814 0.026 0.763 0.866
10.6 180 3 0 0.017 0.983 0.801 0.027 0.748 0.854
10.66667 177 1 0 0.006 0.994 0.796 0.027 0.743 0.849
10.7 176 1 0 0.006 0.994 0.792 0.027 0.738 0.845
10.8 175 3 0 0.017 0.983 0.778 0.028 0.724 0.833
11 172 1 0 0.006 0.994 0.774 0.028 0.719 0.829
11.16667 171 2 0 0.012 0.988 0.765 0.029 0.709 0.821
11.33333 169 2 0 0.012 0.988 0.756 0.029 0.699 0.812
11.43333 167 1 0 0.006 0.994 0.751 0.029 0.694 0.808
11.5 166 1 0 0.006 0.994 0.747 0.029 0.689 0.804
11.66667 165 3 0 0.018 0.982 0.733 0.030 0.675 0.791
12 162 1 0 0.006 0.994 0.729 0.030 0.670 0.787
12.16667 161 1 0 0.006 0.994 0.724 0.030 0.665 0.783
12.3 160 1 0 0.006 0.994 0.719 0.030 0.660 0.779
12.33333 159 1 0 0.006 0.994 0.715 0.030 0.655 0.774
12.85 158 1 0 0.006 0.994 0.710 0.031 0.651 0.770
13 157 2 0 0.013 0.987 0.701 0.031 0.641 0.762
13.16667 155 2 0 0.013 0.987 0.692 0.031 0.631 0.753
13.25 153 1 0 0.007 0.993 0.688 0.031 0.627 0.749
13.33333 152 3 0 0.020 0.980 0.674 0.032 0.612 0.736
13.38333 149 1 0 0.007 0.993 0.670 0.032 0.608 0.732
13.4 148 1 0 0.007 0.993 0.665 0.032 0.603 0.727
13.43333 147 2 0 0.014 0.986 0.656 0.032 0.593 0.719
13.46667 145 2 0 0.014 0.986 0.647 0.032 0.584 0.710
13.5 143 3 0 0.021 0.979 0.633 0.032 0.570 0.697
13.63333 140 1 0 0.007 0.993 0.629 0.032 0.565 0.693
13.75 139 1 0 0.007 0.993 0.624 0.033 0.561 0.688
14 138 3 0 0.022 0.978 0.611 0.033 0.547 0.675
15 135 3 0 0.022 0.978 0.597 0.033 0.533 0.662
15.16667 132 1 0 0.008 0.992 0.593 0.033 0.528 0.658
15.23333 131 1 0 0.008 0.992 0.588 0.033 0.523 0.653
15.26667 130 1 0 0.008 0.992 0.584 0.033 0.519 0.649
15.33333 129 2 0 0.016 0.984 0.575 0.033 0.509 0.640
15.43333 127 1 0 0.008 0.992 0.570 0.033 0.505 0.635
15.5 126 1 0 0.008 0.992 0.566 0.033 0.500 0.631
15.66667 125 1 0 0.008 0.992 0.561 0.033 0.496 0.627
16 124 1 0 0.008 0.992 0.557 0.033 0.491 0.622
16.33333 123 1 0 0.008 0.992 0.552 0.033 0.486 0.618
17 122 3 0 0.025 0.975 0.538 0.034 0.473 0.604
17.41667 119 1 0 0.008 0.992 0.534 0.034 0.468 0.600
17.6 118 1 0 0.008 0.992 0.529 0.034 0.464 0.595
17.76667 117 1 0 0.009 0.991 0.525 0.034 0.459 0.591
17.83333 116 2 0 0.017 0.983 0.516 0.034 0.450 0.582
18.06667 114 1 0 0.009 0.991 0.511 0.034 0.445 0.577
18.33333 113 1 0 0.009 0.991 0.507 0.034 0.441 0.573
18.83333 112 1 0 0.009 0.991 0.502 0.034 0.436 0.568
18.96667 111 1 0 0.009 0.991 0.498 0.034 0.432 0.564
19.08333 110 1 0 0.009 0.991 0.493 0.034 0.427 0.559
19.33333 109 3 0 0.028 0.972 0.480 0.034 0.414 0.546
19.5 106 1 0 0.009 0.991 0.475 0.034 0.409 0.541
19.56667 105 1 0 0.010 0.990 0.471 0.034 0.405 0.536
19.6 104 1 0 0.010 0.990 0.466 0.034 0.400 0.532
19.66667 103 2 0 0.019 0.981 0.457 0.034 0.391 0.523
20.83333 101 0 1
21.6 100 1 0 0.010 0.990 0.452 0.033 0.387 0.518
23.33333 99 1 0 0.010 0.990 0.448 0.033 0.382 0.513
23.36667 98 1 0 0.010 0.990 0.443 0.033 0.378 0.509
23.4 97 3 0 0.031 0.969 0.430 0.033 0.364 0.495
23.46667 94 1 0 0.011 0.989 0.425 0.033 0.360 0.490
23.5 93 1 0 0.011 0.989 0.420 0.033 0.355 0.486
27.06767 92 0 1
27.17867 91 0 1
27.28967 90 0 1
27.40067 89 0 1
27.51167 88 0 1
27.62267 87 0 1
27.66667 86 1 0 0.012 0.988 0.416 0.033 0.350 0.481
27.73367 85 0 1
27.84467 84 0 1
27.95567 83 0 1
28.06667 82 0 1
28.17767 81 0 1
28.28867 80 0 1
28.39967 79 0 1
28.51067 78 0 1
28.62167 77 0 1
28.73267 76 0 1
28.84367 75 0 1
28.95467 74 0 1
29.06567 73 0 1
29.17667 72 0 1
29.28767 71 0 1
29.33333 70 1 0 0.014 0.986 0.410 0.033 0.344 0.475
29.39867 69 0 1
29.50967 68 0 1
29.62067 67 0 1
29.73167 66 0 1
29.84267 65 0 1
29.95367 64 0 1
30.06467 63 0 1
30.17567 62 0 1
30.28667 61 0 1
30.39767 60 0 1
30.50867 59 0 1
30.61967 58 0 1
30.66667 57 1 0 0.018 0.982 0.402 0.033 0.337 0.468
30.73067 56 0 1
30.84167 55 0 1
30.95267 54 0 1
31.06367 53 0 1
31.17467 52 0 1
31.28567 51 0 1
31.39667 50 0 1
31.50767 49 0 1
31.61867 48 0 1
31.72967 47 0 1
31.84067 46 0 1
31.95167 45 0 1
32 44 10 0 0.227 0.773 0.311 0.036 0.240 0.382
32.06267 34 0 1
32.17367 33 0 1
32.28467 32 0 1
32.39567 31 0 1
32.50667 30 0 1
32.61767 29 0 1
32.72867 28 0 1
32.83967 27 0 1
32.95067 26 0 1
33.06167 25 0 1
33.17267 24 0 1
33.28367 23 0 1
33.39467 22 0 1
33.50567 21 0 1
33.61667 20 0 1
33.72767 19 0 1
33.83867 18 0 1
33.94967 17 0 1
34.06067 16 0 1
34.17167 15 0 1
34.28267 14 0 1
34.39367 13 0 1
34.50467 12 0 1
34.61567 11 0 1
34.72667 10 0 1
34.83767 9 0 1
34.94867 8 0 1
35.05967 7 0 1
35.17067 6 0 1
35.28167 5 0 1
35.39267 4 0 1
35.50367 3 0 1
35.61467 2 0 1
35.72567 1 0 1
Discloser
I am long NWBO stock; I am a clinical Researcher
China don't be stupid. We're talking about glioblastoma multiforme ;the Emperor of all cancers. You got to start thinking outside of the box with this one baby or else it's over. Granted, it would be nice to have more of the Nwbo results. At least Freeze the tumor so that you have all of your options.
I would be virtually 100% confident that Mayo Clinic Arrogant did not freeze any tumor. Aside from standard of care treatment, or in house protocols, nothing else is on the radar at MCA.
Their surgeons probably don't even know about or are aware of NWBO.
However, the only glimmer of hope might be, that one of the scientific advisory panel members is a neurosurgeon at MCA in Jacksonville Florida. He would probably be your best Avenue at getting that information to MCA physicians in Az.
China
Hahaha.
You obviously have never tried to call a doctor at Mayo Clinic Arrogant. Much much better to leave an email about NWBO like what was done.
Especially for a high profile case like this they MCA won't even let you talk to the cook Who is making the hospital food.
You might have better luck however in discussing with MCA peoples tattoos as noted in the below story from naturalnews.com
Mayo Clinic Hospital Surgeon Takes Photo of Patient's Penis During Surgical Procedure
(NaturalNews) A surgeon at the Arizona Mayo Clinic Hospital in Phoenix has admitted to using his mobile phone to take a picture of a patient's penis during surgery.
Dr. Adam Hansen said that he took the picture of Sean Dubowik's penis while inserting a catheter during a gall bladder operation. Dubowik, a strip club owner, has the words "Hot Rod" tattooed on his penis. According to Dubowik, he got the tattoo as part of a $1,000 bet.
"I feel violated, betrayed and disgusted," Dubowik said, upon learning of the photo. "The longer I sit here the angrier I get."
Hansen, a trainee surgeon, apparently showed the photo to some of his colleagues after the operation. One of these people then anonymously called the Arizona Republic newspaper to report the incident. After the Republic reported the story, the Mayo Clinic announced that Hansen was "no longer practicing medicine" at the hospital.
The hospital did not specify whether Hansen had resigned or been fired. Hospital officials say they are still trying to identify the person who called the press.
The federal government has decided not to prosecute Hansen for violating the 2003 Health Insurance Portability and Accountability Act (HIPAA), which is meant to protect patient privacy. Violations of HIPAA are only misdemeanors, and no one in Arizona has ever been prosecuted under the act.
"HIPAA is not even the point," said Chic Older, executive director of the Arizona Medical Association. "Many ethical boundaries were crossed" by Hansen's actions.
Older said that Hansen's actions deserved disciplinary consequences, but acknowledged that at least the doctor's actions did not lead to physical harm. "He just made a stupid error in judgment," Older said, for which he has paid a "high price."
According to Older, the Arizona Medical Board might still decide to bring charges of unprofessional conduct against Hansen.
Thanks thanks flipper I will try to run the analysis. Thanks for the info I will try to run these analysis. I probably won't get at it for a day or two because of my work schedule.
Abeta
Yes that looks pretty good to me.
I have really enjoyed looking at your data. You are not a dope.
Sometimes this is really tough to analyze. I would like to be able to post my entire spreadsheet but I haven't figured out how to do that on this board like you are able to.
Basically I've developed a spreadsheet with 331 Rows. Each row represents a patient. I then put in the dates of their entry and formulate the analysis that way.
One can then model different scenarios. Unfortunately We don't have a lot of precise data points so we end up somewhat with educated guesses. I utilized the worst case scenario, because we did have defined dates of entry and we knew the number of failures as of July 15, 2017. So I figured, that was our best "known" dataset.
We could run this with additional data points that you and flipper and others have just to see what would happen. But the uncertainty goes up when we don't have defined points. And that worries me.
Fortunately however my review, the worst case scenario, wasn't all that bad. So I expect the actual curves, when published will be better than what my worst case scenario showed.
Alpha
JTorence
They said that 100 patients were still alive as of Bosch's talk and he said they were getting 2 events (deaths) per month and therefore by Mid July would have 2 additional events. I just used that since that would be the most up to date data known.
Alpha
Discloser: I am long this stock; I have no inside information; I am a clinical researcher.
Assumption 1: Recently the company disclosed the trial entry data and indicated that as of 7-15-2017 there were 98 patients out of the 331 randomized, still alive. Utilizing this new data, I endeavored to model a worst-case DCVax-L GBM survival curve for all patients. Since 90% of the patients in this trial have received the experimental treatment DCVax-L, this trial is really a randomization of upfront early use DCVax-L vs Delayed used of DCVAX-L given after the first progression. For the purpose of creating this curve, one assumes that only the 98 most recently randomized in the trial are still alive. Hence this will provide us with the least amount of survival time as all their other patients were randomized at an earlier date. Furthermore, in these remaining patients, we know for a fact the amount of time for follow-up as we have been given there entry randomization date and we have been told that there will be 98 patients still alive by 7-15-2017.
Assumption 2: If you, therefore, assume that all remaining patients randomized early survived by an amount equal to the quoted standard of care (SOC) median survival of 15-17 months, one can generate a Kaplan-Meyer Survival curve for the entire 331 patients which will be the worse case scenario especially for the tail of the survival curve.
When one does this the 36 month tail survival for all patients is equal to nearly 30% . This tail long-term survival is essentially equivalent to what has been reported and published by the Optune dataset.
Please remember that this analysis is intended to return the worse case survival curve because we already know that many of the earlier randomized patients have lived longer than SOC.
THEREFORE this analysis would suggest that the total group of patients in the DCVax-L Trial will have overall survival curves much better than the one shown that has a tail equivalent to what has been published with the OPTUNE dataset.
I wish I could model this more precisely but the limited data points makes that impossible.
It does look like the survival will be quite good in this trial as a whole.
Summary statistics:
Total observed Total failed Total censored
331 233 98
Kaplan-Meier table:
Months At risk Failed Censored Proportion failed
15 331 78 0 0.236
16 253 77 0 0.304
17 176 78 0 0.443
20.26666667 98 0 1
20.507 97 0 1
20.618 96 0 1
20.729 95 0 1
20.73333333 94 0 1
20.84 93 0 1
20.951 92 0 1
21.062 91 0 1
21.173 90 0 1
21.284 89 0 1
21.395 88 0 1
21.506 87 0 1
21.617 86 0 1
21.728 85 0 1
21.839 84 0 1
21.95 83 0 1
22.061 82 0 1
22.172 81 0 1
22.283 80 0 1
22.394 79 0 1
22.505 78 0 1
22.616 77 0 1
22.727 76 0 1
22.838 75 0 1
22.949 74 0 1
23.06 73 0 1
23.171 72 0 1
23.282 71 0 1
23.393 70 0 1
23.504 69 0 1
23.615 68 0 1
23.726 67 0 1
23.837 66 0 1
23.948 65 0 1
24.059 64 0 1
24.17 63 0 1
24.281 62 0 1
24.392 61 0 1
24.503 60 0 1
24.614 59 0 1
24.725 58 0 1
24.836 57 0 1
24.947 56 0 1
25.058 55 0 1
25.169 54 0 1
25.28 53 0 1
25.391 52 0 1
25.502 51 0 1
25.613 50 0 1
25.724 49 0 1
25.835 48 0 1
25.946 47 0 1
26.057 46 0 1
26.168 45 0 1
26.279 44 0 1
26.39 43 0 1
26.501 42 0 1
26.612 41 0 1
26.723 40 0 1
26.834 39 0 1
26.945 38 0 1
27.056 37 0 1
27.167 36 0 1
27.278 35 0 1
27.389 34 0 1
27.5 33 0 1
27.611 32 0 1
27.722 31 0 1
27.833 30 0 1
27.944 29 0 1
28.055 28 0 1
28.166 27 0 1
28.277 26 0 1
28.388 25 0 1
28.499 24 0 1
28.61 23 0 1
28.721 22 0 1
28.832 21 0 1
28.943 20 0 1
29.054 19 0 1
29.165 18 0 1
29.276 17 0 1
29.387 16 0 1
29.498 15 0 1
29.609 14 0 1
29.72 13 0 1
29.831 12 0 1
29.942 11 0 1
30.053 10 0 1
30.164 9 0 1
30.275 8 0 1
30.386 7 0 1
30.497 6 0 1
30.608 5 0 1
30.719 4 0 1
30.83 3 0 1
30.941 2 0 1
31.052 1 0 1
25.6717551
Mean survival time:
Mean survival time (Months<30.940999999996)
Mean survival time (Months<30.940999999996) Standard deviation Lower bound (95%) Upper bound (95%)
20.391 0.375 19.656 21.125
Quantiles estimation:
Quantile Estimate Lower bound (95%) Upper bound (95%)
75%
50% 17.000 16.000 17.000
25% 16.000 15.000
Abeta
Nice work. However your analysis does not take into account the shape of the curves. One can easily have equivalent median survivals that have substantially different overall survival outcomes. This is especially the case in situations where one curve has a long tail compared to the other curve.
To explain the latest press release, in simple terms, transparently, is that the trial is succeeding.
Their primary endpoint, Progression free survival, has the number of events necessary for the trail to be analyzed. Based on historical, data sets, regarding the length of time that it took to achieve the number of events, lead me to believe that this will be a statistically significant result favoring the experimental treatment arm.
Overall survival, nearly always takes longer to event out then does progression free survival.
It would appear that there still may be time to benefit from the low capitalization of the company at this time, Prior to more Data analysis and review.
It looks like they might be shooting for a ASCO presentation.
Canis. Overall survival is actually the easiest of the statistical parameters to calculate. Primarily, this is because of death is such a definitive unequivocal infallible event.
All you need to calculate Kaplan-Meier survival curves is the date the patient entered the trial, the date of last follow up or death and whether the patient was alive or dead at that date.
Progression free survival however is open to much more interpretation regarding whether or not the patient is coded as having progressed. Trial such as this will always compare the experimental arm with the control arm irrespective of whether or not patients received the experimental treatment in a crossover arm after progression.
As I have indicated in numerous previous post, the delay that we are seeing regarding events occurring is extremely positive feature the overall trial results.
GET READY we are NOW GETTING CLOSE
I reviewed my prior dataset and assumptions; Any amount of time that elapses without discloser of achieving the 248th event, After the beginning of Aug 2016 is golden and represents treatment efficacy and significant treatment effect.
Please review my prior posts. In Nov 2016 we will nearly 4 months from when the expected number of events should have occurred based upon SOC.
POST 47777
Significant PFS improvement and Time Analysis for DCVax-L GBM trial
To better understand this post and my model, Please reference my prior posts regarding modeling for the DCVax-L GBM trial.
IHUB POST 3433 uses the STUPP trial as the Model for the control patients.
IHUB POST 14585 used the IMUC data to further refine the model data.
IHUB POST 17001 from Flipper
FIRST Things we know ... Dec 2013 there were 66 events disclosed prior to the trial being expanded. We know the published STUPP and IMUC Control and Treated patients progression free survival (PFS) curves.
Furthermore, recently it was disclosed in August 2015 that the DCVax-L trial had accrued 300 patients randomized. This discloser gives us the average accrual rate for the trial from Dec 2013 to August 2015.
Using the above data, I then proposed the following model and thought experiment.
For the purpose of this model analysis, we make the following assumptions. Let us pretend that after the trial was expanded in Aug 2014, increasing the N to 348, that the experimental DCVax-L treated patients and non-treated, all had exactly the same progression free survival PFS, as did the control patients from the known STUPP and IMUC trials.
Using these parameters, our model would then provide us a worst case scenario in terms of when the number of events in the DCVax-L Trial would occur to give us 148 events, (first interim Analysis), 198 events (second interim analysis), and 248 events (Trial end). When one models the data in this manner, one obtains the following results.
The 149th event would have occurred in June 2015, and the 198th event would occur in Feb 2016 and the 248th event would occur on August 2016.
Therefore each month that passes after June 2015 without any announcement that the first interim analysis (148 events) has been reached, indicates that during this time period there have been less events than what would have been expected, had the patients progressed in a manner similar to the control groups from the STUPP or IMUC randomized trials. Less events means that the DCVax-L treated patients have survived longer without any progression, then what would have been expected by these control groups.
Therefore, each month that there is no announcement provides further evidence that the DCVac-L trial will yield a positive significant result favoring the dendritic treatment.
Discloser: I am long this stock and I am a smart clinical researcher whose hobby is to model survival data with complex statistical software
POST 14585
Updated from prior post and new IMUC Data.
Discloser: I am long this stock; I have no inside information; I am a clinical researcher.
Hypothesis 1: The Control Patients in the DCVax-L GBM trial should relapse at a rate similar to what was shown in the IMUC trial. The IMUC trial is the most similar to the DCVax-L GBM trial in terms of eligibility entry criteria. The progression free survival (PFS) was reported recently at the 2014 American Society of Clinical Oncology (ASCO) meeting. In order to compare the IMUC Trial to the current DCVax-L trial control patients, however, one must add 83 days to the IMUC results because the IMUC data time 0 for PFS was upon first administration of immunotherapy (ie after surgery and RT) whereas the DCVax-L Trial (and every other major trial in this disease) calculated the start day 0 for the PFS curves at the date of surgery. The Median PFS for the control patients in the IMUC trial was 9 months. When one adds the 83 days to this on gets 11.72 months median PFS for the control patients in the IMUC trial. Therefore the control patients in the DCVax-L trial should be around 11.72 months. (And to be conservative I used 12 months median PFS for this modeling.
Hypothesis 2: Given that we know the number of events in the total DCVax-L trial at this time (first interim analysis) is 67, then one can model using JMP(SAS) software, the control arm PFS to be similar to the corrected IMUC trial results and thus obtain an estimate for the probable number of events required in the control arm of the DCVax-L trial to give a 12 month median PFS at the first interim analysis date (Mid Dec 2013). When one does this with a Kaplan Meier plot, one obtains a result of a minimum of 25 (Max-Min; 28-25) events in the control arm with the total number of patients in the control arm of 57. Then one can deduce that the number of events in the experimental arm for the DCVax-L trial would be 67- 25 or 42 events. I used the minimum events in the control arm be conservative and get the maximum potential number of events for the experimental arm.
Hypothesis 3: Using the derived 42 events in the experimental arm, one can model the experimental arm to be similar to the IMUC trial results but, in this case only allow for just 44 events out of 114 total patients in the DCVax-L experimental arm. When one does this with a Kaplan Meier plot, the median PFS for the experimental arm returns > 17.763 month median PFS in the DCVax-L experimental arm. When one applies log-rank and Wilcoxon significance testing to the two curves one obtains a significant result of p=.0364 and a nonsignificant but trending p =.0781 respectively.
My best educated guess is that the DCVax-L trial is going to return a positive result on the primary end point but this is now a much closer horse race. I suspect that analysis such as this has caused NWBO to consider increasing the total N for the trial in order to give a higher probability for obtaining a significant result. Hence the delay until 2015 for study closure which was recently reported.
This however will still be a practice changing result. Just to put this into perspective the Stupp GMB trial PFS only increased 2 months by the addition of temazolamide (TMZ) (5 to 6.9 months). Yet this became almost immediately, the new gold standard of care in this disease and TMZ almost immediately became a blockbuster muli-billion dollar drug.
DCVaX-L increases in the operable patients even more than this and will be the new standard of care in this disease.