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These events also make me wonder whether recent stock price fluctuations on relatively high volume (2-4 mill daily) even when Wall Street is all out on vacation may have somehow been related to the reworking of company finances. Maybe just paranoia. In either case, this seems like a good thing, and when summer officially ends and everyone gets back to their financial jobs, maybe we will start seeing the stock "ramp up" everyone expects.
Not a finance person but just doing enough reading to be dangerous. One source noted that you have to have enough shares available to do the conversion, which makes we wonder whether the new share offering recently was part of this whole deal (i.e., to put enough shares out there to convert).
Another source said: "If, however, the bonds are converted into stock, the shareholder equity will increase and debt will decline by equal amounts. Conceptually, this makes sense, because suddenly the company has gotten rid of a payment obligation, which results in a windfall profit. However, there are more shareholders than before, and the profit per share may go down."
So - If the shares were already diluted at the last new offering, and debt on the books goes down, seems to me like this is a net good thing for the balance sheet.
Anyone understand this better?
Trading a guaranteed interest rate on notes for equity that can up (or down) in value seems to me to indicate confidence that shares are headed up. I do not think this means there is any DMC leak but maybe does indicate confidence in management?
Anyone think the slump today in share price has to do with general anti-pharma reaction to the epi pen price gouging? I cannot find any other reason.
Maybe someone else knows the correct answer to this, but my impression is that the approach they are using allows them to shift the increased risk of error around between the three looks at the data, such that they can more or less maintain the original p values they wanted as the significance criterion in the final data look by taking a bigger hit to the p value criterion at the interim looks.
The simplest conceptual way to look at the "penalty" for doing repeated interim analyses is this. When you set a criterion for significance of P<.05 (for example), you have a 5% chance (.05) of erroneously showing a significant difference between groups when there in fact is not a real difference. This assumes a single look at the data. If I look at the data repeatedly, the risk of this error increases beyond the nominal 5% risk of error I was originally willing to accept. With repeated looks at the data, I actually have to set a significance criterion of less than 5% in order to get a real 5% chance of erroneously concluding there is a significant difference. For example, if I am being highly conservative, I might divide the p value I really want (the risk of mistaken conclusions I am willing to accept) by the number of times I am looking at the data, so with 2 interims and a final analysis, p<.05 divided by 3 gives an adjusted p value of p= 0.0166 as the criterion for significance I should be looking at. This is an overly simplistic and conservative option ("bonferroni correction"). I think the R-IT trial design is simply using a more finessed and less conservative way of doing these adjustments.
Technically, there is no reason they could not be using a 1-tailed statistical test, and have a much higher chance of showing significance - they clearly have a directional hypothesis (V better than P). However, in the research world, 1-tailed tests have a stink about them, as if the researcher is cheating to get significant results.
By the way - "phlogiston" is a word I have never heard used before in an actual sentence. I will have to work that into conversation with my wife tonight
No - Taking a peek at the data in an interim analysis does nothing to change how big the treatment effect is. In a trial like this, though, you have to pre-specify what p value you are going to consider statistically significant at each point you look at the data. When you take interim looks at the data, you pay a "penalty" that makes the p value you will consider significant a little harder to meet. That said, the p value criterion for significant results at the 80% interim appears quite easy to meet assuming there are any reasonably sized treatment effects. The p value at the 60% interim has been set to a fairly high bar, to make sure they do not stop the trial prematurely. What is nice is that at the 80% interim, not only does the p value criterion for significance drop substantially, but we have also increased the sample size of MACE outcomes, which means that there are two different aspects of the data that are making it easier to reach statistical significance.
I interpret "de-risked Vascepa program" in this context as meaning that positive R-IT results at either interim eliminates almost all of the downside risk to the company going forward. Their market can do nothing but expand.
For what it's worth, on the Fidelity brokerage website, AMRN research now lists 6 independent firms with a mean accuracy weighted opinion (Equity Summary Score) for AMRN of 9.5/10 ("Very Bullish"). A different index also weighted for accuracy of predicting stocks in the sector has 5 of the 6 most accurate firms with "Buy" or "Outperform" ratings for AMRN.
While the absolute reductions in risk seem small, you have to look at the effects at the population level. Teaching medical students about research 20 years ago, I used the example of daily low dose aspirin as a way of reducing MI risk. Everyone accepts that there is a link and that it is appropriate to recommend daily aspirin to those at risk of MI. I then showed the actual size of the significant association (at least as it was known at that time) based on large population studies, which was a correlation of about r = 0.04 as I recall. This equates to aspirin use explaining approx 0.16% of the variability in MI rates. This seems tiny, but when applied to the whole U.S. population of 400 million people, it equate to a benefit for more than 10 million people (400 million X 0.04). Bottom line - Absolute risk reduction does not have to be huge to have a meaningful benefit across the whole population.
If I recall correctly, the new statistical plan made clear that it is not absolute number of MACE events that would be used for testing statistical hypotheses, but rather the time to occurrence of those events. I may be wrong, but it is possible that at the 60% interim, both the Pl and V group could have the same absolute number of MACE events, but they occurred significantly earlier in the PL group. The benefit of V in this case would be to provide more "healthy years" relative to Pl. This seems like it would provide an additional way to show beneficial effects of V. Anyone with expertise in survival analysis would be welcome to chime in at this point...
I am thinking that big investors may be pricing an assumed continue at the first interim analysis into the stock price (i.e., no change). I continue to believe a stop at the 80% interim is much more likely due to all the sunk costs they have in the study and the important subgroup data they will obtain if the study continues a while longer. Stopping trials really early is rare. I thought the SunTrust info was interesting - it highlights that this trial is not a Phase 3 dichotomous approve/disapprove. Vascepa is already an FDA approved drug. Even if primary endpoint is not significant in the full sample, there may be very strong effects in certain subgroups that could still justify expanded labeling/marketing which could boost the stock price. A fail is not necessarily a fail (but I still have high hopes that the primary will succeed).
What I have heard from my physician friends is that they struggle to get people to change behavior even when failure to make that change has dire consequences. I guess when people "hit bottom" with their health, they might take two very different trajectories. Glad to hear you were able to successfully make so many beneficial changes though.
Regarding the instruction in R-IT to "eat a mediterranean diet" - This will have minimal impact on results in my view, because patients are notoriously bad at following advice that requires changing behavior. The best predictor of future behavior is past behavior....
JL - Intermittent ischemia followed by reoxygenation of tissue also leads to ischemic reperfusion injury related to oxidative stress. It seems that this might be relevant to cardiac patients as well. I wonder if EPA may have meaningful antioxidant effects that would help protect cardiac tissue via a second, non-eicosanoid mechanism? Here is an animal study suggesting antioxidant effects of EPA: http://www.ncbi.nlm.nih.gov/pubmed/27190274
JL - I think you missed my point. I believe that the R-IT focus on very high risk people INCREASES the chance of trial success. We want a lot of events in the P group so that V can show a strong effect. That is a good thing. I think that studies such as are included in the large meta-analyses that have high proportions of relatively healthy people are why EPA doesn't seem to work in some population studies and trials. Not to mention the rancid or weak fish oil in some studies you note. I think we are in total agreement.
True - Trends do not indicate significance. But, you then have to look at sample size. If the effect size (RRR) stays roughly similar across studies, a trend quickly becomes "highly significant" if you add a bunch of extra patients. R-IT has 8,000, and the studies you are referring to have how many?
I go back and forth between blind optimism that the RI trial will work, and fear that relatively negative results in some past studies as highlighted by Pyrrh indicate that RI will fail. When I step back as an investor and put on my scientist hat, and look at the studies noted by Pyrrh and which can be identified on a simple Pubmed search, what we see is a mixed bag of studies. For every discouraging result there is a positive result. There is an AHRQ (government sponsored) publication from 2012 that is a very sophisticated meta-analyses and it shows EPA does have some benefit on CV outcomes, although maybe not quite as large as we all hope. I think the issue of "generalizability of samples" is the key issue. That is, results from prior studies can only be applied to populations with characteristics similar to the patients in those study samples. The RI trial, as far as I can tell, is unique in focusing simultaneously on: 1) pure high dose EPA, 2) applied in combination with statins, 3) to individuals with high CV risk, and 4) very high triglycerides. They have oversampled to get the sickest people possible, and effects of pure EPA + statins in this population might be expected to be very different than in populations with less CV risk and/or lower lipid levels. I think the bottom line may be that there is NO prior work really similar to the RI trial, and we cannot tell from prior work one way or the other what RI will show. It is a well-thought out gamble, with high risk but potentially life changing results, potentially similar to the introduction of statins. Just my opinion...
I get that the current research may show lowering LDL does not help CV outcomes, but the comments from my Cardio friend suggest that the company has work to do in educating them. Clinical practice and insurance always lags behind the most current research findings. I do not believe this knowledge gap/bias is unchangeable by any stretch.
For what it's worth, I talked to a friend of mine who is a very smart Harvard-trained cardiologist. Her comments on Vascepa based on its mechanism of action included:
1) Currently, insurance providers do not pay for drugs that do not lower LDL, because TG lowering alone has not (yet) been shown to reduce cardiac mortality (which is what R-IT would show if successful).
2) The CV drug space is a very competitive market to enter.
3) Even newer drugs that lower LDL (e.g., PSK9 inhibitors) are not being covered by insurers unless a patient has failed statins and meet very strict criteria.
4) She could foresee Vascepa being targeted at the diabetic market, which is growing rapidly and has unique problems with both high LDL and high TG. She also noted that combining statins+fibrates (for TG lowering) in this population has increased adverse events, and statins + Vascepa might have advantages in this regard (although she noted even Vascepa has multiple drug interactions listed to be concerned with).
It is pretty rare for large clinical trials to be stopped as early as 60% interim. We might get lucky, but I think the likelihood is low, and people who know anything about conducting clinical trials will know this (including large institutional investors focused on biotech). For that reason, I don't think there will be a huge, lasting run to the exits if/when the 60% interim decision is to continue the trial.
I think it is much more likely to get a stop at the 80% interim - this represents 33% more MACE endpoints in the analysis, which makes getting significant results substantially easier. If that 80% stop does not happen, I will have to keep reminding myself that most SUCCESSFUL trials go the full study duration rather than stopping early. That is why they were designed with the sample size and study duration they chose in the first place - they assumed the full trial would be needed. In my view the 60% and 80% are "freebies" with high upside and little clear downside risk, but I have to admit I will be much more nervous if it comes down to a dichotomous success/fail at the end of the RI trial.
Assuming the RRR they thought they would see associated with V was correct (which would have been based on looking at prior similar studies), then the study is clearly NOT underpowered. It is overpowered, which buys a little extra safety margin in case the RRR is a bit smaller than they anticipated.
I agree with those of you who feel the stock offering is a good thing, which insures the company can continue on its own to the end of RI without problems. It seems to me that the management stands to gain the most in their personal income by insuring that RI succeeds and that they do not have to partner (and share profits), which would raise the stock price and make the stock options which I assume are a big chunk of their compensation much more valuable.
For those of you who know how "the street" works, I am curious about something. I noticed that both Jeffries and HC Wainwright are involved in the new stock offering, and both in the past 6 months upgraded AMRN to a "Buy." Does this indicate they are pumping the stock in a quid pro quo for getting this work (which would seem counterproductive if they want to buy the stock cheaply before good news)? Or, does this indicate that these independent firms have evaluated the AMRN books and prospects closely, and really do strongly believe the price will go up substantially? Or, is it meaningless? I guess my question comes down to whether this is a situation where these firms' involvement in the stock offering and their recent upgrades can be taken as real evidence that those in the know feel confident about the future prospects for AMRN. Any opinions on this would be appreciated.
Great story. My favorite stats quote (not sure of source) is: "He uses statistics like a drunken man uses a lamp post, for support rather than illumination"
"REDUCE-IT is designed to provide 90% power to detect a 15% relative risk reduction between arms"
This simply means that: 1) The sample size of 8,000 patients they chose will let them detect a real effect V in reducing their MACE rates of 15% or greater relative to P at whatever p value criterion for significance they chose, AND 2) IF there is no difference between groups, they can be 90% confident that the lack of effect was not due to the sample being too small.
There will be a post-hoc power associated with whatever the unblinded results show. However, this is really an independent issue from the p value you get, which is what drives the binary decision of success/fail for the primary outcome. The observed power at the end of the study is relevant only for interpreting negative results. In a clinical trials context, 90% power means that we are 90% likely to be correct in "failing to reject the null hypothesis" that P and V groups are equal (which is the "statistical-ese" way of saying there is no treatment effect). Power isn't really that important if RI fails, other than to say with confidence that future studies of the issue are not warranted. Maybe what HD (whose posts are well thought out) was referring to was the RRR (effect size) necessary for the desired p value for study stop? I do agree that having the biggest effect size (RRR) possible is important (i.e., getting beyond bare minimum p value to say study was successful). This will give AMRN scientists a better ability to test subgroup hypotheses that may help expand the indication labeling to a broader patient population. As much as I would like to see the study stop sooner rather than later, I think the company is doing the right thing to make sure key secondary outcomes are robust. They have spent tens of millions with the majority of the costs behind them (trials typically fund study sites by paying a certain amount when each enrolled patient reaches various study milestones, so as more patients finish the study as their final endpoint is reached the study costs start to tail off). Stopping the study prematurely right before they might get significant findings for key subgroups (e.g., diabetics) just does not make financial sense to me. Everything I hear from management suggests they they have good scientists designing and overseeing this trial (the PI is at Harvard), and I am confident whatever decision they make will be based on a well thought out strategy.
I have been "lurking" on the board for the past few weeks and recently bought 22,000 shares at $3.03 (in for the long haul). I do medical research for a living and have been involved (on the side) in developing clinical trials but not in the CV space. One thing I have noticed on the board is some confusion about how power, the effect size assumed when designing the trial(RRR), and the significance (p value) for stopping the trial interrelate. The effect size, p value, and sample size all are directly related. For a given RRR, the larger the sample, the lower the observed p value goes (so it is easier to meet your prespecified stop criterion). If the sample size is fixed, as in the RI trial, the bigger the RRR, the lower the observed p value. Power as described in the RI materials assumes a reasonable effect size (e.g., 15% lower risk with V versus placebo) and was used solely for planning how large a sample size they would seek to accrue before it ever began. Once a study is taking place, having high power is useful ONLY in the context of making sense of NEGATIVE results. A study with low power could still turn out to have significant results, but you would be unable to interpret negative findings in such a study. Powering a study a priori at 80% power is typical of NIH-funded studies, so in my view RI is OVER-powered at 90%. This should help insure that if there are real V treatment effects, they will have plenty of patients to make results "highly significant." The criterion for significance at the 2nd interim of approx p<.02 is quite modest and should be easy to achieve assuming V works even moderately well. As Pyrrh has noted, it is always possible that findings from subgroup analyses from smaller trials will not replicate in a larger study like RI, but given the diversity of consistent evidence suggesting EPA works, I am reasonably confident in a stop at the 80% interim analysis (which I think was a great addition to the SPA agreement). Two other comments: 1) In response to those thinking FDA staff are being intentionally malicious towards AMRN, my experience with FDA staff in person was that they are "bean counters" and bureaucrats who VERY strictly follow rules (to the point of making decisions that make little logical sense in my experience). 2) The DMC is totally independent and company staff will remain totally blinded to study results (other than safety issues) until the "blind is broken" once the study is stopped. No one should read anything into comments from AMRN personnel one way or the other until unblinding occurs.
Anyway, I am looking forward to an exciting next 9-12 months.