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Chas - Regarding dropouts, trials I know of that have required much greater patient engagement than R-It (behavioral change) have dropout rates as high as 15%. I assume R-It would be much lower. Given no major side effects of V and no psychoactive effects to be able to tell whether they are on V or placebo, there is no compelling reason for there to be differential dropout across groups. It is possible that a small number of Placebo group patients might see no change in TG levels when they go to their private MD, might figure out they are getting Pl, and might dropout if they felt strongly about getting on V (but since many MDs don't believe TG lowering is critical and evidence for V reducing MACE risk is still unknown until R-It completion, I believe the motivation for dropping out would not be that great). Assuming dropout across groups is random (e.g., just tired of having to do study visits or take medications), this should not systematically affect results in either direction. Sample is large enough to handle 10% dropouts (n=800) and still have adequate power assuming the RRR is 18% (approx) or greater.
Chas - Thanks for the accurate and detailed info.
You could definitely have high RRR (and potentially significant results) for SEs (i.e., certain patient subgroups, uch as diabetics) even if the RRR for the PE was small and nonsignificant. Gets at the whole issue of precision medicine - most drugs work best in certain groups, but in most cases for existing drugs we don't know who those responders are. R-It would potentially tell us who responds best to V.
Can't recall exactly but I believe around 2 years.
Pyr -
Yeah, I guess it was lower at first, but then event rate (and possibly enrollment) was too slow, so they changed crit to allow higher risk patients in.
JF - Totally agree.
Most posts here are just opinions regardless of who is doing the posting, some more informed than others. I personally do not mind reading any posts that raise legitimate questions, issues, etc. I find that most posters here have something useful to say at least some of the time. To try to avoid getting slammed too often here for posting the "wrong" thing, I do try to restrict my comments (mostly) to issues that I know something about professionally (research design, stats, and to some extent FDA processes). Sometimes when the PPS is moving up a lot, though, I have been tempted to engage in pointless speculation. I hope to have that opportunity again soon
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JL - I agree that observed RRR is likely to be higher (I lowballed my guess in HD's contest just to see if I could win the contest that way). When I said stop rule, I meant the minimum RRR they would prespecify as having to be reached before an early stop could be considered. The actual RRR could be much higher than this, and probably will be. What I am getting at is the company may not have set a stop rule that is any more difficult to achieve at the 80% than it is at the final readout - it could be the 15% RRR they targeted when designing the study (which would be significant at 80% IA). I agree that there are repetitive and not very well considered posts from some on this site at times, but do not think my original post was one of them. It is a totally valid SPA question.
Kiwi - Interesting study. Thanks for sending it. MI rates in the revascularized group seemed surprisingly high (18%), but links between diabetes and need for revascularization make sense. If R-It indeed was preferentially enrolling stented patients, and combine that with the relatively high proportion of diabetics that seems to have been expected, the R-It population would seem to be a group that would have a particularly high rate of MACE events (higher than the 5% or so assumed in the placebo group). This might lead to the study achieving the targeted number of events sooner rather than later (maybe we will find out at the next quarterly conference call). It may be that the selective enrolling of stented patients you noted was part of a pre-planned stratified enrollment, so that they were targeting certain proportions of different types of patients, and they might have needed to oversample certain groups at certain times if they were not getting enough of that type of patient. However, I have never seen any description of a formal strategy for getting a certain % of different types of patients in R-it.
I don't recall it being discussed here previously, but I wonder if the "stop rule" for the PE is a significant RRR that is at least 15%, which is what the trial was publicly stated to be designed to test (in investor presentations). With the number of subjects and events at the 80% IA, 15% RRR should get us to the required p value to stop at that point (by my calculations at least). Anyone heard anything different about targeted RRR?
Interesting note about enriching the R-It sample for stented patients. Re: your question - I have no idea. I have not seen stented % (or any specific demographic info about the R-It sample) listed in any public information. The company probably could access this information, as this does not require unblinding of treatment assignment (demographics are know before random assignment), and the DMC certainly knows. I agree about the CHERRY study results being important - showed V + statins objectively reversed plaque more than statins alone (IIRC).
Kiwi - That is because CO-Q10 supplements are poorly absorbed orally. This does not indicate that the CoQ10 effects of statins are not a problem.
Thanks Kiwi - There are complex statistical procedures that can be used to tease all of these different factors out. For example, Bayesian statistics will tell you the probability of X event given any given set of a priori conditions (e.g., multiple diagnoses), and these probabilities could be compared for V versus no-V. Even better (and more mysterious to me) is "machine learning", which can tease out significant outcome predictors from very complex and messy big data datasets like insurance records. Machine learning procedures are now commonly used to make sense of big data from brain imaging studies (millions of data points) and huge patient record datasets.
No - In my limited experience with insurance data, they do not have high granularity. They would know diagnosis of hyperlipidemia, diabetes, etc., but would probably not be able to access actual lab numbers for Trigs or anything else (HIPAA issues). On-label vs. off-label could only be inferred from the diagnoses. So, for example, they could tell that diabetics with hyperlipidemia who take V have lower costs related to CV-hospitalizations than those not taking V. Very useful information but not down to the fine-grained variables that may differ from patient to patient that might influence efficacy in a specific patient. That will have to wait for R-It.
Interesting issue - Even as we wait on R-It, insurance companies could easily (and probably do) look at whether drugs they are reimbursing pay off. Would be very easy for them to find out clinically whether patients on V cost them less in terms of MACE or any other outcome they wanted to look at. Insurance may actually be ahead of the curve on this, and may already know that V reduces CV risk even as we wait for formal proof from R-it. They are the ones paying for V, so they have an incentive to look at these issues, and I know they have both the data (ICD-10 codes, CPT codes) and in-house Big data stats people looking at risk management.
Zum - You may be right, but it may not be just ethical considerations. Given their lack of excess cash, I would think financial issues might also play into the stop decision. It depends on whether they factored in the need for quicker expanded approval into their latest list of SEs in the SPA last year (i.e., how they described the stop rules for the SEs). Time will tell.
The only past trials that have continued after a DMC Stop recommendation that I know of were because the studies had relatively small samples (in the hundreds, so relatively few events) and they wanted more data before stopping. The results were significant but they continued. In one case, results were no longer significant by the time the study ended (that is why they are so cautious about interim analyses).
Yes, with the exception that the DMC could (in theory) say "Stop" and the company could say, "No, Continue to the end." I saw in an old article on problems with interim analyses where this has happened on occasion. Highly doubtful in this case in my view.
In context, this article makes more sense. What the Seeking Alpha article says is that more cash may be needed to expand promotion post-R-It. I think the two are clearly tied together - if there is no 80% IA stop, the expanded promotion and cash required for this would presumably be delayed until the end of R-It. A lot can happen in the interim.
Yet more incentive for them to stop at the 80% IA if the primary MACE outcome is significant.
Agreed, but my point is that these were all clinical and demographics measures that they have always had as part of the study. You cannot add new measures in mid-study. They just added these as SEs officially to the SPA. That is a good thing because it means that they do not need extra time to collect these new measures for a long enough period of time - they have always had them.
I don't believe they actually "added" any new SE measures. You cannot add measures in the middle of a study. They simply incorporated into the SPA explicit language that they planned to look at these SEs or patient subgroups (e.g., diabetics) when they analyze the data, presumably to set the groundwork for going to the FDA for additional indications. They don't need to go an extra year in the study just to collect new data on any new SEs - they just want to make sure they have enough subjects with these SE subgroups/outcomes to have enough statistical power to show they are significant. As I stated in another post, assuming V is particularly effective in certain subgroups or on certain outcomes, the greater RRR will offset the smaller sample and they still could show significance at the 80% IA.
PE will be significant at 80% IA if RRR is at least what they expected (15%). No reason to think that key SEs will not also be significant at 80% IA. The one mentioned repeatedly is diabetics. If half the sample is diabetic (n=4,000), and RRR is even larger in this group (25%-30% ??), still would be significant at 80% IA, since event rates in the placebo group would likely be higher than in the overall sample. I think the company mantra of "we expect to go to the end" is them reducing expectations just in case. There is no reason to have added the 80% look and "spend alpha" from the final analysis if they did not think they had a high likelihood of stopping at that point. Just my opinion though.
Stac - Very interesting study, indicating R-It is on the right track. Also unbiased as it was NOT funded by AMRN or any other company.
Not true - There is a journal called Trials that publishes nothing but trial designs before actual results come out. It is listed on PubMed, so it is at least somewhat respectable. I will say again that there is no way NEJM will publish a trial design without any data, for R-It or any other trial. Don't know how that rumor ever got started. Only way R-It gets into NEJM without data is in the context of a review article that might mention ongoing R-It in passing. Once R-it succeeds, I fully expect it to be published in NEJM - that is certainly where the investigators would send it and I have no doubt the successful trial would be accepted there. It would be groundbreaking.
As per a post several months ago, a previous response from the company to an inquiry about the middle east market issue indicated that it would be a small market. They did not say where.
That is a fair point - The ADCOM moved the goalposts in the middle of a kick. That was unfair and indicates that whole process was flawed. That said, I agree with Raf that the FDA was probably justified in saying proof of actual effects on CV outcomes rather than surrogate measures (TG levels) was needed before V could be marketed as a drug to treat CV disease. AMRN just got caught in the middle of a sea change. I think it would be great if they granted expanded approval before R-It, but I am very doubtful that will happen.
Yes - This meta-analysis suggests positive R-It results to some degree. The actual article attempts to break out EPA vs. DHA effects, and found a bigger effect for DHA (effect size nearly twice as large). Effects of EPA specifically were only borderline significant (p = 0.08). The authors admit that it is hard to look at EPA-specific effects because there are so few studies looking at this as an intervention (JELIS was the biggest). So most studies evaluated the mixed the effects of EPA and DHA, and results cannot be disentangled completely. Also, if you look at the studies included, the dose of Omega 3's was usually low (<1g per day), 4 times lower than in R-It. The fact that overall conclusions were positive, particularly for patients with high TGs (as in R-It), does I believe bode well for eventual R-It success.
JL - I agree with you that T2DM is likely to be their key focus as a secondary stop rule. That may end up being a primary market for V.
80% Interim Analysis example outcomes - Just for fun, I took the number of events at the 80% analysis which we do know (1,290), and worked out a few scenarios splitting the total sample of n = 8,175 patients evenly across the 2 groups. P values are for a simple model not using the fancy survival analysis that will be used in real analysis. See below for results. In order, the columns are: Control MACE events, Control Non-Events, Vascepa MACE Events, Vascepa Non-Events, RRR, and p value.
685, 3402, 605, 3482, 12% p=.015
695, 3392, 595, 3492, 14% p=.0025
700, 3387, 590, 3497, 16% p=.0009
730, 3357, 560, 3527, 23% p<.0001
The 23% RRR was the lower bound of the 95% confidence interval observed in the JELIS subgroup of patients most similar to R-It (low HDL-C, TG > 150, 22% diabetics). This means that the true RRR is likely to be at least that big even if run in a totally different sample from the same population.
Because even a 12% RRR reaches the statistical cutoff for stopping the trial at the 80% IA, I find it hard to believe the trial won't be stopped if anything close to the assumed RRR is reached. Only caveat is a possible no stop decision if key secondary endpoints are still not showing what they hope.
Tocopherols (vitamin e related) are to reduce oxidation.
I think it is patent infringement, based on combo of very high purity EPA plus nearly absent DHA. However, patents only protect if you enforce them, and for whatever reason the company has not yet tried to stop OmegaVIA. Maybe waiting until after R-It? I cannot imagine they would never try to enforce this patent against DS manufacturers.
I guess the "urgent job postings" for business development you noted put to rest any ideas that BOD member Healy leaving was related to jumping ship based on leaked bad news.
Interesting tidbit - I was doing some required training that covered good clinical practice standards. Turns out there have been efforts to standardize clinical trial requirements across the US, Europe, and Japan (International Conference on Harmonisation of Technical Requirements for the Registration of Pharmaceuticals for Human Use). Compliance with these standards for purposes of FDA approval is not required, but is strongly encouraged. Assuming AMRN complied with these recommended standards, they would be able to submit the same R-It data they submit for FDA approval to the Eurpoean market for approval there as well without having to do additional trials. Anyone have any idea whether R-It might comply with these standards? Opens up some easy ways to increase their market if so (unless their patents do not cover Europe).
JL - That helps explain it. Makes sense. Thanks for giving this background info.
I think BOD member Healy leaving could be due to several reasons, not necessarily indicating anything bad is going on. Could be for something investment neutral, like health reasons or overcommitment (family reasons). If he left because of impending disaster, others on board and in management would know (only possible reason might be leak of negative R-IT results) - if this were the case, I think you would not see big funds buying millions of shares of AMRN stock recently because word of negative news would likely get out quickly to the investment community. One could even take a positive view of this under a best case scenario - maybe he sits on another board of a company that is seeking to buy AMRN, and he has a conflict of interest, so stepped down (I am NOT trying to start buy out rumors though). Alternatively, maybe he strongly feels AMRN should seek a buyout and the other board members disagree, so he is resigning in protest. Bottom line - I do not believe that this resignation tells us anything one way or the other about what is going on in the company. It has too many possible explanations.
STS - Agreed this would be great. And if the company let's their MD researcher collaborators have access to their full dataset with TGs and MACE events assessed repeatedly over time, they will have an answer to whether V reduces MACE BECAUSE of its TG lowering effects. Companies are sometimes cautious about letting their data out though. The good news is that even if they don't address this causality question, all the FDA cares about is that V reduces MACE events.
"Open label extensions" after the primary randomized phase of a trial has ended are a standard design element in many clinical trials. People originally getting placebo can choose to cross over to the active drug. It does not allow placebo control, but it can provide information on long term effects of being on the drug (do benefits continue in a stable manner), and does the group originally getting placebo start to show significant benefits on active drug compared to how they were doing when on placebo. It is just a rational stats argument rather than a placebo controlled conclusion (i.e., do death rates in the placebo group drop significantly after they start using the active drug compared to when they were on blinded placebo). The FDA approves these kinds of open label extensions in analgesic trials all the time.
FWIW - I just attended a weekend meeting discussing drug approvals, and in attendance was a recent head of a main FDA drug approval division. One point made was that if you want approval of X drug as a means of preventing X negative health outcome, the FDA is really not that concerned with the mechanism, just that there is a convincing empirical relationship. For V, this means as long as it reduces MACE events sufficiently, it really won't matter from the FDA persepctive whether this is due to lowering trigs or antiiflammatory effects. An approval can result in either case. That would certainly seem to be e beneficial thing.
Regarding sales, per stock twits: V trx 18927 v 18518. Nrx 7334 v 7243. Gen L TRx 1912 v 2069. Nrx 698 v 822
Still increasing. You should have been able to guess this from the paradoxical decrease in share price that seems to be tied to any good news.