Umibe - I agree that NWBO is not a scam and there are legitimate ways to analyze the data that NWBO has laboriously collected despite crossover confounds. I do biostatistics as part of my day job, and posted what is below several months ago, but think it bears repeating. While the specifics below may not be exactly what the SAB ends up recommending after FDA consultations, I think it shows that there are several legitimate statistical approaches that can make sense of the DCVAX data regardless of how the crossover has affected things. Here is what I posted previously:
Here are some scenarios I thought I would share, with one caveat - I am a researcher and do my own statistics, but do not do KM survival analysis (although what I am describing applies conceptually regardless). Although clinician biases in trying to figure out whether a drug works for a specific patient based on simply observing that patient are well known, I think the long history of DCVAX use by a highly experienced clinician (LL) who believes it works based on her clinical practice means that we do not have to even consider the scenario where DCVAX proves entirely ineffective.
Scenario 1: PFS (primary outcome) is significant. No idea how likely this is, but if this were the case, that plus the long tail results would IMO be plenty to get DCVAX approved, regardless of whether traditional OS (overall survival) measures are significant.
These other scenarios all assume PFS is NOT significant.
Scenario 2: Significant main effect of DCVAX on OS based on the condition to which patients were originally assigned at baseline (Intent to treat analysis). This pattern is what we would see if DCVAX works well only in newly diagnosed GBM, but does not work at all for recurrent GBM. Visualize a graph with a survival curve steeply sloping downward towards death (placebo group regardless of crossover status) and a clearly separate curve sloping downward above it less steeply (DCVAX). This is the most straightforward homerun result we could get for the key prespecified secondary outcome (OS). I don't think the FDA would provide much pushback against this even though it is technically a secondary endpoint, especially in context of the long tail milestone results (e.g., 3 and 4 year survival rates). FDA approval would be VERY likely despite technical failure of primary outcome (PFS).
Scenario 3: This could be tested as the interaction between original treatment group assignment (intent to treat) and the timing of DCVAX administration (i.e., those originally assigned to placebo who never crossover would be coded as 0, placebos who crossed over to DCVAX after recurrence would be coded as 1, and those assigned to DCVAX originally would be coded as 2). Alternatively, you could totally ignore the initial group assignment, and simply look at the main effect of the timing of when the patient got DCVAX (those who never got DCVAX would be coded as a 0 and all others would be coded as the number of months after enrollment when they received DCVAX). A significant analysis like this would indicate that the efficacy of DCVAX depended on when it is given. This would be the most relevant analysis if DCVAX worked well for newly diagnosed GMB and worked somewhat, but less well, for those with recurrent GMB. Visualize the steepest downward sloping survival curve for placebos who did not cross over, a separate intermediate downward sloping curve for original placebo patients who did crossover, and the shallowest curve (i.e., longest survival) for those patients originally assigned to DCVAX. If this main effect of DCVAX timing was significant, the pattern of this effect would be biologically plausible (it works best when given shortly after diagnosis), and because of this, I believe it would support the efficacy of DCVAX, and in context of strong long tail milestone results, would also likely support FDA approval.
Scenario 4: This is the best case scenario for patients but the worst case scenario in terms of interpretability. Visualize the curves for the patients originally assigned to get DCVAX and those original placebo patient who later crossed over after GBM recurrence overlapping extensively, with both showing a shallow downward sloping curve, with a much steeper downward sloping curve (lower % surviving) for the non-crossover placebo patients. Clearly DCVAX seems to be doing something (visually) but you would get a nonsignificant main effect of DCVAX in ITT analyses (because there are so many crossovers with long survival who would dilute the treatment effect, with the two curves with the most patients overlapping). This is the only scenario in which comparison to SOC controls becomes crucial. There would be no direct statistics to show that DCVAX works, so approval could only be based on showing greater long-tail OS percentages at key milestones (e.g., 2 year, 3 year, 4 year milestones) than you would expect in SOC. These comparisons could be tested statistically, but how convincing that argument is in terms of FDA-approvability depends on whether an SOC number can be quantified for a group similar enough to the DCVAX trial population. While this may be difficult, it would be reasonable to handle this by doing statistical comparisons for milestone survival % between DCVAX patients (whether initially assigned to DCAVX or later crossing over to DCVAX) and prior SOC samples, including all relevant individual difference variables in the model as covariates in a propensity score matching approach (in this case, percentage of patients in each study with partial surgical resection, +MGMT status, etc). I think a meta-regression approach (like meta-analysis but looking at the influence of other key sample characteristic variables) might be best for comparing the DCVAX trial results to a composite of all other similar SOC trials. In theory, if the DCVAX effect in these meta-regression models is significant even after controlling for all relevant sample characteristic differences between studies, then we would be justified in concluding that DCVAX works to increase long-tail survival. Given the orphan status of GBM and the current regulatory climate at the FDA for orphan disease interventions, I think this has good odds for FDA approval even in this worst case scenario.
When I walked through these scenarios in my head, I realized that the crossover issue is not necessarily an issue impossible to address. I have no idea what approach they actually plan on using, but I think they are paying smart consultants to generate these proposed statistical models, and am sure they can collectively figure out a way to show DCVAX works if indeed it does work (and I strongly suspect it does based on the blinded analyses presented so far). Hopefully we find out in the next couple of months exactly what these analyses show and will proceed shortly thereafter to PPS liftoff. I do not think they will wait for a conference to present these data - they will be announced shortly after they are available, and this should be no more than a few weeks after the dataset is locked.
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