12 ms·

The press-release conviction of a biotech CEO and its impact on research

49 points by bqe 13y ago

DanBC 13y ago
> This mild-to-moderate subgroup wasn’t one the researchers said they would analyze when they set up the study. Subdividing patients after the fact and looking for statistically significant results is a controversial practice. In its most extreme form, it’s scorned as “data dredging.” The term suggests that if you drag a net through a bunch of numbers enough times, you’ll come up with something significant sooner or later. He could have just kept that data secret, and ran another trial but specifically targeted at people with mild to moderate illness. That would have protected him legally, and made the numbers look even better. That's the kind of thing that many people are campaigning against. Companies should release all the research they do rather than cherry picking the useful (to them) results.
- Bakkot 13y ago
  Actually, that would have been way better. If they had done the study you suggest, and the result had still been significant, then he would have been entirely justified in reporting what he did. The issue is that dividing the participants after the fact and then looking for correlation in the existing data reduces the significance of the statistic considerably (we have other statistics for that). The p-value is not representative when used that way. But if you do another study focused on that group in particular and still get a significant result, you're fine! The problem isn't that they located a group on which the drug worked in a dishonest way, or some such - the problem is that they were dishonest to claim they had significant evidence that the drug worked on that group. If they'd done an additional study on that group in particular, they would have their evidence (or, of course, a null result).
  - andrewcooke 13y ago
    i think the person you were replying to was implying that the same data be used, while what you are arguing is that there should be new observations made (and i agree with you, if the new work is independent; i just wanted to explain why i think the original comment was arguing for greater transparency).
    - Malician 13y ago
      I believe he's suggesting that the doctor could have legally covered up the results of the first trial (by simply not releasing them,) then run a second trial on only the most beneficial population, releasing those results without mentioning the first trial. At that point, his product would look great, hiding its failures. This way, while he misinterpreted the P value in an illegal and fraudulent way, he did release all relevant information - ironically, better for the informed reader than if he had rerun the trial legally.
      - feral 13y ago
        Malician, I'm not sure you and DanBC understand this fully? It would be absolutely fine to run a new trial on the supposedly most beneficial population (those with mild/moderate lung damage; lets call them 'the subpopulation'). If that second trial succeeded, then it would be strong evidence that the drug was beneficial for the subpopulation. There would be no need to hide the results of the first trial, as the first trial did not provide evidence that the drug didn't work on the subpopulation. If you read the article to the end, they did in fact do such a trial on the subpopulation. And they got evidence it wasn't working on the subpopulation - which is how science goes. The problem was that the first trial wasn't set up to examine the subpopulation, but they reported results as if it was. You can't do that with standard NHST, as it invalidates the assumptions of the statistical framework being used. But you can absolutely decide to run a whole new test on a new sub population, based on hints you get from the first results. And, while it'd in general be better if all test results (positive AND negative) were published, that is not relevant to this situation - the first trial said nothing bad about the effects on the subpopulation, so there'd be nothing to gain from hiding it, if you just wanted to claim it worked on the subpopulation. Its not like a situation where they got evidence that the subpopulation would not benefit in the drug in the first test, and then decided to do another test, planning to only report the second.
        pessimizer 13y ago
        I think you're getting hung up on their use of the word "hide." What they're saying is that the first study could have been disregarded except as a good reason to run the second study. Of course, that later happened, and the effect disappeared - but maybe it wouldn't have. That's how science works. I don't thing that you're disagreeing with them, just reiterating.
- zebra 13y ago
  After the company research there should be another independent research (by WHO maybe).
- fpgeek 13y ago
  I'll suggest reading to the end of the article. They did do another trial targeted at people with mild to moderate illness. It failed: "A little more than a year into the study, more people on the drug had died (15 percent) than people on placebo (13 percent)."
  - aheilbut 13y ago
    They don't report whether that difference was significant though, do they...
    - DanBC 13y ago
      (fpgeek is right, I should have read further) It was significant enough to stop the trial. (http://www.ncbi.nlm.nih.gov/pubmed/19570573?dopt=Abstract http://www.ncbi.nlm.nih.gov/pubmed/19570573?dopt=Abstract) > FINDINGS: At the second interim analysis, the hazard ratio for mortality in patients on interferon gamma-1b showed absence of minimum benefit compared with placebo (1.15, 95% CI 0.77-1.71, p=0.497), and indicated that the study should be stopped. After a median duration of 64 weeks (IQR 41-84) on treatment, 80 (15%) patients on interferon gamma-1b and 35 (13%) on placebo had died. Almost all patients reported at least one adverse event, and more patients on interferon gamma-1b group had constitutional signs and symptoms (influenza-like illness, fatigue, fever, and chills) than did those on placebo. Occurrence of serious adverse events (eg, pneumonia, respiratory failure) was similar for both treatment groups. Treatment adherence was good and few patients discontinued treatment prematurely in either group.
      - aheilbut 13y ago
        My point is that by saying that "more patients on the drug died", they're implying that the drug was itself killing people. With 35 patients, the 2% difference is about one person. They stopped the trial because the drug wasn't doing anything, but it's misleading to suggest that it was contributing further to mortality.
        fpgeek 13y ago
- jonlucc 13y ago
  I agree that they needed the additional study (which is underway, I believe). However, I think it is important to realize the cost of another study of that size is very expensive and often small vaccine companies can't afford unplanned major studies without going back to the financial drawing board.
- JshWright 13y ago
  He wouldn't even need to keep it secret. It's completely legitimate to say "We were trying to prove XYZ and didn't, but the data does hint that ABC might be true. Let's do another study looking specifically at ABC." This is how a lot of Science gets done.
jerrytsai 13y ago
As a trained (and hopefully ethical) statistician, I agree with the interpretation that Dr. Harkonen did mis-represent the results of the trial. This article does a pretty good job describing the controversy to a layperson audience, but I do not feel the result is all that controversial. It is well-known to statistically-minded people that p-values are computations that rely on particular assumptions being made. One of those assumptions is that only a single, pre-specified hypothesis is being tested. By making additional comparisons, the p-value that was reported by Dr. Harkonen mis-represented the true significance of the trial. Perhaps factually the p-value was 0.004, but publicizing the p-value as if it were obtained by a fair trial, as opposed to finding it in a hunt for (quasi-)statistical significance, was a manipulation of the facts to support one's personal interests. That's not science; it's bias, and self-serving bias at that.
- mbreese 13y ago
  I'm not as convinced as you are. Just because the original study wasn't found to be significant, that doesn't mean that an already-existing sub group wasn't significant. They used an existing clinical trait as a separate classifier to look at the patients in a different way. That isn't too controversial (if you have a high enough patient count, which was probably the biggest fault of this post-hoc analysis). Then again, I'm a biologist. We're trained to not trust statisticians. (Or course, we're also trained to not speak in absolutes and cover every statement in doubt, so he failed in that regard too).
  - dalek_cannes 13y ago
    I initially thought he was partially justified in claiming the number with the 'mild-to-medium patients' qualification if there was a real benefit in the drug that the study had not been designed to detect. Except that a follow up study focusing on the mild-to-medium subgroup failed to show statistically significant benefits.
  - jerrytsai 13y ago
    What is likely, although we cannot of course know for sure, is that the doctor looked at more than disease severity (the "existing clinical trait") as a separate classifier, hunting for subgroups for which the p-value indicated a promising trend. The principle behind the proscription against multiple comparisons is well-known to statisticians. If we consider a 1 chance in 20 result to be statistically significant, then, randomly, on average 20 "trials" will yield one statistically significant result. By dividing the patients into disease severity subgroups, Dr. Harkonen increased the number of "trials" from 1 to 4, thereby elevating the likelihood of yielding an effect that appeared to be statistically significant. If he also examined other subgroups in his quest to find a positive result, then he elevated the likelihood of finding a positive result toward certainty. Our desire to find patterns and see cause and effect make us prone to confirmation bias. We can guard against this bias with care, including the use of statistics. It was not a surprise that a subsequent study looking only at the "mild-to-moderate" group did not demonstrate any benefit of the treatment. The belief that the treatment would benefit "mild-to-moderate" patients was speciously derived.
JoeAltmaier 13y ago
Alternate title: "Man commits fraud to profit from terminally ill patients, gets slap on wrist"?
jonnathanson 13y ago
Was this article written by Dr. Harkonen's publicist or something? The case seems pretty clear: he knowingly misrepresented the results of a drug trial for the financial benefit of his firm and, by association, himself. He did this at the possible expense of critically to terminally ill patients, and at the further expense of the scientific and medical integrity of his research. And he received 6 months of house arrest at his cushy, 3-story San Francisco home as punishment. Forgive me if I don't strain myself reaching for my violin. I sincerely hope this piece is not representative of the journalistic integrity of the Post under its new ownership. The article's blatant slant, its casual blending of editorial opinion and facts-based reporting, and its weirdly patronizing tone (ex: "the so-called 'p-value'") do no justice to the reputation of the newspaper. The author opens with a rather silly rhetorical question, one with an obvious answer: "Is it a crime for a medical researcher to hype his results? To put a heavy spin on the findings when there are millions of dollars, and possibly lives, at stake?" Yes. Yes, it is. Especially when there are millions of dollars, and possibly lives, at stake. You don't get to cut corners in the scientific method because you think you're on to something.
- nkurz 13y ago
  I got the opposite impression. In fact, I think this may be the best medical statistics article I've ever seen in the popular press: But there was a problem. This mild-to-moderate subgroup wasn’t one the researchers said they would analyze when they set up the study. Subdividing patients after the fact and looking for statistically significant results is a controversial practice. In its most extreme form, it’s scorned as “data dredging.” The term suggests that if you drag a net through a bunch of numbers enough times, you’ll come up with something significant sooner or later." I don't presume it has anything to do with Bezos, but if it does, I hope stays on a buying spree! I was inspired to find out more about the author: http://www.washingtonpost.com/david-brown/2011/02/28/AB2Y0sM_page.html http://www.washingtonpost.com/david-brown/2011/02/28/AB2Y0sM... It turns out he's a part-time journalist and part-time licensed physician: "He works four days a week at the Post and two-thirds of a day at a general internal medicine clinic in Baltimore supervising third-year medical students." I also didn't find it biased toward Harkonen at all. Consider the closing: InterMune did run another trial. It was big — 826 patients at 81 hospitals — in order to maximize the chance of getting clear-cut results. It enrolled only people with mild to moderate lung damage, the subgroup whose success was touted in the press release. And it failed. A little more than a year into the study, more people on the drug had died (15 percent) than people on placebo (13 percent). That was the death knell for the drug. It even links to the actual study: http://www.ncbi.nlm.nih.gov/pubmed/19570573?dopt=Abstract http://www.ncbi.nlm.nih.gov/pubmed/19570573?dopt=Abstract I don't know if I've ever seen a major newspaper link directly to a primary scientific paper as a source. I encourage you to read it again and see if your view changes. I've read it twice now and think it is an absolutely stellar piece of science writing, worthy of a Best Science Writing compilation.
mnbvcxza 13y ago
Who's got the Dune quote for this?
- foobarbazqux 13y ago
  Science is made up of so many things that appear obvious after they are explained.
- seehafer 13y ago
  HE WHO CONTROLS THE [STOCK] PRICE, CONTROLS THE UNIVERSE!
greenyoda 13y ago
Here's a statistician's take on this story: http://wmbriggs.com/blog/?p=9308 http://wmbriggs.com/blog/?p=9308
- sanskritabelt 13y ago
  Everybody reading this and saying 'oh! a statistician!' should remember that Briggs is also, among other things, a global warming denier.
  - newnewnew 13y ago
    A lot of statisticians tend to be so.
  - nkurz 13y ago
    Thanks for pointing that out. Yes, it's hard to believe that someone with so much education (degrees in Statistics, Atmospheric Science, Meteorology, and Math) and so much professional experience (University Professor, Wall Street Quant, National Weather Service, US Air Force) would get that completely wrong. What do you figure the chances of that are? ;) http://wmbriggs.com/blog/?page_id=1085 http://wmbriggs.com/blog/?page_id=1085
    - sanskritabelt 13y ago
      Yeah its almost as if he has a track record of going against the evidence in favor of acting the iconoclast.
yarou 13y ago
This is very interesting. In most papers I read during uni, the p-value was always set to 0.10. But I suppose it makes sense to have a more rigorous null hypothesis testing when you are talking about saving lives. I'm curious to see, on the whole, if all researchers in pharma try to move the goalposts like this guy did.
fiatmoney 13y ago
Thank God R.A. Fisher still had all his toes when he invented the concept, or we could have been stuck with a P-value threshold of 0.052631579...
downandout 13y ago
This case, like so many others, appears to be the product of an overzealous prosecutor looking to add to his resume before he begins applying to work at much higher paying private law firms. The concept of moral hazard does not exist for prosecutors - they can take all the shots they want at other people with no consequences. Until there are consequences, we will continue seeing blatant abuses of our justice system for the personal gain of those that work within it. Though it will never happen, private law firms should simply refuse to hire former prosecutors - many of these nonsensical prosecutions would vanish overnight.

49 points by bqe 13y ago

DanBC 13y ago

> This mild-to-moderate subgroup wasn’t one the researchers said they would analyze when they set up the study. Subdividing patients after the fact and looking for statistically significant results is a controversial practice. In its most extreme form, it’s scorned as “data dredging.” The term suggests that if you drag a net through a bunch of numbers enough times, you’ll come up with something significant sooner or later. He could have just kept that data secret, and ran another trial but specifically targeted at people with mild to moderate illness. That would have protected him legally, and made the numbers look even better. That's the kind of thing that many people are campaigning against. Companies should release all the research they do rather than cherry picking the useful (to them) results.

Bakkot 13y ago
Actually, that would have been way better. If they had done the study you suggest, and the result had still been significant, then he would have been entirely justified in reporting what he did. The issue is that dividing the participants after the fact and then looking for correlation in the existing data reduces the significance of the statistic considerably (we have other statistics for that). The p-value is not representative when used that way. But if you do another study focused on that group in particular and still get a significant result, you're fine! The problem isn't that they located a group on which the drug worked in a dishonest way, or some such - the problem is that they were dishonest to claim they had significant evidence that the drug worked on that group. If they'd done an additional study on that group in particular, they would have their evidence (or, of course, a null result).
- andrewcooke 13y ago
  i think the person you were replying to was implying that the same data be used, while what you are arguing is that there should be new observations made (and i agree with you, if the new work is independent; i just wanted to explain why i think the original comment was arguing for greater transparency).
  - Malician 13y ago
    I believe he's suggesting that the doctor could have legally covered up the results of the first trial (by simply not releasing them,) then run a second trial on only the most beneficial population, releasing those results without mentioning the first trial. At that point, his product would look great, hiding its failures. This way, while he misinterpreted the P value in an illegal and fraudulent way, he did release all relevant information - ironically, better for the informed reader than if he had rerun the trial legally.
    - feral 13y ago
      Malician, I'm not sure you and DanBC understand this fully? It would be absolutely fine to run a new trial on the supposedly most beneficial population (those with mild/moderate lung damage; lets call them 'the subpopulation'). If that second trial succeeded, then it would be strong evidence that the drug was beneficial for the subpopulation. There would be no need to hide the results of the first trial, as the first trial did not provide evidence that the drug didn't work on the subpopulation. If you read the article to the end, they did in fact do such a trial on the subpopulation. And they got evidence it wasn't working on the subpopulation - which is how science goes. The problem was that the first trial wasn't set up to examine the subpopulation, but they reported results as if it was. You can't do that with standard NHST, as it invalidates the assumptions of the statistical framework being used. But you can absolutely decide to run a whole new test on a new sub population, based on hints you get from the first results. And, while it'd in general be better if all test results (positive AND negative) were published, that is not relevant to this situation - the first trial said nothing bad about the effects on the subpopulation, so there'd be nothing to gain from hiding it, if you just wanted to claim it worked on the subpopulation. Its not like a situation where they got evidence that the subpopulation would not benefit in the drug in the first test, and then decided to do another test, planning to only report the second.
      - pessimizer 13y ago
        I think you're getting hung up on their use of the word "hide." What they're saying is that the first study could have been disregarded except as a good reason to run the second study. Of course, that later happened, and the effect disappeared - but maybe it wouldn't have. That's how science works. I don't thing that you're disagreeing with them, just reiterating.
zebra 13y ago
After the company research there should be another independent research (by WHO maybe).
fpgeek 13y ago
I'll suggest reading to the end of the article. They did do another trial targeted at people with mild to moderate illness. It failed: "A little more than a year into the study, more people on the drug had died (15 percent) than people on placebo (13 percent)."
- aheilbut 13y ago
  They don't report whether that difference was significant though, do they...
  - DanBC 13y ago
    (fpgeek is right, I should have read further) It was significant enough to stop the trial. (http://www.ncbi.nlm.nih.gov/pubmed/19570573?dopt=Abstract http://www.ncbi.nlm.nih.gov/pubmed/19570573?dopt=Abstract) > FINDINGS: At the second interim analysis, the hazard ratio for mortality in patients on interferon gamma-1b showed absence of minimum benefit compared with placebo (1.15, 95% CI 0.77-1.71, p=0.497), and indicated that the study should be stopped. After a median duration of 64 weeks (IQR 41-84) on treatment, 80 (15%) patients on interferon gamma-1b and 35 (13%) on placebo had died. Almost all patients reported at least one adverse event, and more patients on interferon gamma-1b group had constitutional signs and symptoms (influenza-like illness, fatigue, fever, and chills) than did those on placebo. Occurrence of serious adverse events (eg, pneumonia, respiratory failure) was similar for both treatment groups. Treatment adherence was good and few patients discontinued treatment prematurely in either group.
    - aheilbut 13y ago
      My point is that by saying that "more patients on the drug died", they're implying that the drug was itself killing people. With 35 patients, the 2% difference is about one person. They stopped the trial because the drug wasn't doing anything, but it's misleading to suggest that it was contributing further to mortality.
      - fpgeek 13y ago
jonlucc 13y ago
I agree that they needed the additional study (which is underway, I believe). However, I think it is important to realize the cost of another study of that size is very expensive and often small vaccine companies can't afford unplanned major studies without going back to the financial drawing board.
JshWright 13y ago
He wouldn't even need to keep it secret. It's completely legitimate to say "We were trying to prove XYZ and didn't, but the data does hint that ABC might be true. Let's do another study looking specifically at ABC." This is how a lot of Science gets done.

jerrytsai 13y ago

As a trained (and hopefully ethical) statistician, I agree with the interpretation that Dr. Harkonen did mis-represent the results of the trial. This article does a pretty good job describing the controversy to a layperson audience, but I do not feel the result is all that controversial. It is well-known to statistically-minded people that p-values are computations that rely on particular assumptions being made. One of those assumptions is that only a single, pre-specified hypothesis is being tested. By making additional comparisons, the p-value that was reported by Dr. Harkonen mis-represented the true significance of the trial. Perhaps factually the p-value was 0.004, but publicizing the p-value as if it were obtained by a fair trial, as opposed to finding it in a hunt for (quasi-)statistical significance, was a manipulation of the facts to support one's personal interests. That's not science; it's bias, and self-serving bias at that.

mbreese 13y ago
I'm not as convinced as you are. Just because the original study wasn't found to be significant, that doesn't mean that an already-existing sub group wasn't significant. They used an existing clinical trait as a separate classifier to look at the patients in a different way. That isn't too controversial (if you have a high enough patient count, which was probably the biggest fault of this post-hoc analysis). Then again, I'm a biologist. We're trained to not trust statisticians. (Or course, we're also trained to not speak in absolutes and cover every statement in doubt, so he failed in that regard too).
- dalek_cannes 13y ago
  I initially thought he was partially justified in claiming the number with the 'mild-to-medium patients' qualification if there was a real benefit in the drug that the study had not been designed to detect. Except that a follow up study focusing on the mild-to-medium subgroup failed to show statistically significant benefits.
- jerrytsai 13y ago
  What is likely, although we cannot of course know for sure, is that the doctor looked at more than disease severity (the "existing clinical trait") as a separate classifier, hunting for subgroups for which the p-value indicated a promising trend. The principle behind the proscription against multiple comparisons is well-known to statisticians. If we consider a 1 chance in 20 result to be statistically significant, then, randomly, on average 20 "trials" will yield one statistically significant result. By dividing the patients into disease severity subgroups, Dr. Harkonen increased the number of "trials" from 1 to 4, thereby elevating the likelihood of yielding an effect that appeared to be statistically significant. If he also examined other subgroups in his quest to find a positive result, then he elevated the likelihood of finding a positive result toward certainty. Our desire to find patterns and see cause and effect make us prone to confirmation bias. We can guard against this bias with care, including the use of statistics. It was not a surprise that a subsequent study looking only at the "mild-to-moderate" group did not demonstrate any benefit of the treatment. The belief that the treatment would benefit "mild-to-moderate" patients was speciously derived.

JoeAltmaier 13y ago

Alternate title: "Man commits fraud to profit from terminally ill patients, gets slap on wrist"?

jonnathanson 13y ago

Was this article written by Dr. Harkonen's publicist or something? The case seems pretty clear: he knowingly misrepresented the results of a drug trial for the financial benefit of his firm and, by association, himself. He did this at the possible expense of critically to terminally ill patients, and at the further expense of the scientific and medical integrity of his research. And he received 6 months of house arrest at his cushy, 3-story San Francisco home as punishment. Forgive me if I don't strain myself reaching for my violin. I sincerely hope this piece is not representative of the journalistic integrity of the Post under its new ownership. The article's blatant slant, its casual blending of editorial opinion and facts-based reporting, and its weirdly patronizing tone (ex: "the so-called 'p-value'") do no justice to the reputation of the newspaper. The author opens with a rather silly rhetorical question, one with an obvious answer: "Is it a crime for a medical researcher to hype his results? To put a heavy spin on the findings when there are millions of dollars, and possibly lives, at stake?" Yes. Yes, it is. Especially when there are millions of dollars, and possibly lives, at stake. You don't get to cut corners in the scientific method because you think you're on to something.

nkurz 13y ago
I got the opposite impression. In fact, I think this may be the best medical statistics article I've ever seen in the popular press: But there was a problem. This mild-to-moderate subgroup wasn’t one the researchers said they would analyze when they set up the study. Subdividing patients after the fact and looking for statistically significant results is a controversial practice. In its most extreme form, it’s scorned as “data dredging.” The term suggests that if you drag a net through a bunch of numbers enough times, you’ll come up with something significant sooner or later." I don't presume it has anything to do with Bezos, but if it does, I hope stays on a buying spree! I was inspired to find out more about the author: http://www.washingtonpost.com/david-brown/2011/02/28/AB2Y0sM_page.html http://www.washingtonpost.com/david-brown/2011/02/28/AB2Y0sM... It turns out he's a part-time journalist and part-time licensed physician: "He works four days a week at the Post and two-thirds of a day at a general internal medicine clinic in Baltimore supervising third-year medical students." I also didn't find it biased toward Harkonen at all. Consider the closing: InterMune did run another trial. It was big — 826 patients at 81 hospitals — in order to maximize the chance of getting clear-cut results. It enrolled only people with mild to moderate lung damage, the subgroup whose success was touted in the press release. And it failed. A little more than a year into the study, more people on the drug had died (15 percent) than people on placebo (13 percent). That was the death knell for the drug. It even links to the actual study: http://www.ncbi.nlm.nih.gov/pubmed/19570573?dopt=Abstract http://www.ncbi.nlm.nih.gov/pubmed/19570573?dopt=Abstract I don't know if I've ever seen a major newspaper link directly to a primary scientific paper as a source. I encourage you to read it again and see if your view changes. I've read it twice now and think it is an absolutely stellar piece of science writing, worthy of a Best Science Writing compilation.

mnbvcxza 13y ago

Who's got the Dune quote for this?

foobarbazqux 13y ago
Science is made up of so many things that appear obvious after they are explained.
seehafer 13y ago
HE WHO CONTROLS THE [STOCK] PRICE, CONTROLS THE UNIVERSE!

greenyoda 13y ago

Here's a statistician's take on this story: http://wmbriggs.com/blog/?p=9308 http://wmbriggs.com/blog/?p=9308

sanskritabelt 13y ago
Everybody reading this and saying 'oh! a statistician!' should remember that Briggs is also, among other things, a global warming denier.
- newnewnew 13y ago
  A lot of statisticians tend to be so.
- nkurz 13y ago
  Thanks for pointing that out. Yes, it's hard to believe that someone with so much education (degrees in Statistics, Atmospheric Science, Meteorology, and Math) and so much professional experience (University Professor, Wall Street Quant, National Weather Service, US Air Force) would get that completely wrong. What do you figure the chances of that are? ;) http://wmbriggs.com/blog/?page_id=1085 http://wmbriggs.com/blog/?page_id=1085
  - sanskritabelt 13y ago
    Yeah its almost as if he has a track record of going against the evidence in favor of acting the iconoclast.

yarou 13y ago

This is very interesting. In most papers I read during uni, the p-value was always set to 0.10. But I suppose it makes sense to have a more rigorous null hypothesis testing when you are talking about saving lives. I'm curious to see, on the whole, if all researchers in pharma try to move the goalposts like this guy did.

fiatmoney 13y ago

Thank God R.A. Fisher still had all his toes when he invented the concept, or we could have been stuck with a P-value threshold of 0.052631579...

downandout 13y ago

This case, like so many others, appears to be the product of an overzealous prosecutor looking to add to his resume before he begins applying to work at much higher paying private law firms. The concept of moral hazard does not exist for prosecutors - they can take all the shots they want at other people with no consequences. Until there are consequences, we will continue seeing blatant abuses of our justice system for the personal gain of those that work within it. Though it will never happen, private law firms should simply refuse to hire former prosecutors - many of these nonsensical prosecutions would vanish overnight.