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    Subdividing patients after the fact and looking for statistically significant
    results is a controversial practice

If by controversial they meant "is considered outright fraud" ... then yes, it is controversial. This is one of the parts that made me see an unacceptable level of bias.

There are a lot of parts that are similar to this, it might do a better than average job of explaining p-values and talking about the realities of experimental design but the tone in every case is dismissive in the "bunch of eggheads worrying about little details" kind of way.

This is so biased I assume there is an agenda here rather than just bad journalism, the other explanation is that the writer is a good writer, researched the unfamiliar terms but didn't understand any of it and is good enough at regurgitating information to make it look like they did understand it. Either way, it's awful science writing even if they did use a lot of science words correctly. I have a higher standard for my hypothetical Best Science Writing awards I guess ... like requiring they get the science right.

> If by controversial they meant "is considered outright fraud" ... then yes, it is controversial

Hold on there... controversial, yes, but fraud - no. If the original data was reported, and after the fact you discovered a class of patients responded differently than the rest and you could find a good classifier, then it's certainly not fraud. In this case, they weren't just taking all the "good" patients and putting them in one adhoc group and all the "bad" patients and putting them in another. They found they were able to sub-classify the patients using existing clinical traits.

I thought the article did a pretty good job trying to explain the statistical issues at play. As described, the study isn't very clear, but it's hardly fraud to spin the results in that way. Is it reaching? Perhaps, but I so is calling this criminal. Remember using 0.05 as a p-value cutoff for significance is just a convention the number itself is arbitrary. In my work, I have to use something much more stringent such as 0.001 due to multiple testing issues.

In my reading, the issue wasn't the analysis. The issue was the hype of the analysis. In this case, he definitely went overboard, but I'm not sure it rose to the level of fraud.

If you divide patients in 20+ arbitrary groups (say, based on first letter of their name) and give them a 0 efficiency treatment, then almost always you'll see a group with a p-0.05 significant improvement; that' just basic stats.

So, if you afterwards discover some class of patients with this different response, it is grounds only to investigate that group further separately, not anything more - claiming that there is such an effect is fraud. You can't make extra claims until you have verification of that separate group.

For an illustration, see http://xkcd.com/882/

Of course, but they divided the patients into two groups: Mild/moderate and severe clinical phenotypes. It's not optimal, but it is far from subdividing into 20 classes or making up arbitrary classifiers until you got the number you were looking for. The clinical phenotypes were already established prior to the analysis.

> and after the fact you discovered a class of patients responded differently than the rest and you could find a good classifier, then it's certainly not fraud

Or you could say "after the fact you chose/created the class of patients that responded differently".

It certainly is fraudulent, I suppose this issue isn't quite a Stats 101 thing, but it's certainly undergrad level because it's only a little bit subtle.

A useful metaphor to keep in mind is the one that gives the Texas Sharpshooter Fallacy it's name: "The name comes from a joke about a Texan who fires some shots at the side of a barn, then paints a target centered on the biggest cluster of hits and claims to be a sharpshooter." The examples on wikipedia are also instructive (http://en.wikipedia.org/wiki/Texas_sharpshooter_fallacy)

As PeterisP pointed out, the only correct interpretation is to say "That's interesting, we need to do another study with that subgroup to see if that's a real effect" (although that xkcd comic illustrates what's called "The Filedrawer Effect" or simply "publication bias" which is the same error made a different way).

Give me a dataset of people rolling dice many times and the ability to group them after examining the data and I will find you a group that seems to have magical craps playing abilities that don't actually exist. It might be men over 6' with brown hair or it might be married people who are left handed or maybe bilingual introverts ... who knows, I can't tell you what that group will be until I see the data (and that last statement is why you know it's not a real ability). This is precisely why experimental design depends on defining your tests before you examine your data or, put another way, you cannot use the data that suggested an effect to prove an effect.

The idea that the CEO of a biotech company didn't know this is absurd, because this is Running a Scientific Experiment 101. Selecting groups after seeing the data is probably the most common method chosen to tweak failed experiments by those committing fraud, second is probably using the filedrawer (just rerun the experiment until you get a result by chance and pretend the failed experiments never happened). These are so rife that there are hundreds of articles about the prevalence of this type of fraud published in the last few years. This type of behaviour is a big deal these days.

Tone of the article was certainly skewed in Harkonen's favor, so I stopped mid-second page and turned to HN comments.

It's great that the journalist found the debunking study and mentioned it. But does the order matter? He goes on and on about how "a sizable proportion of [scientists] might be in jail today".

Most readers would drop half way through, as I did. What will they learn? Oh, there was evidence that drug can be lifesaving but technicality killed the white knight.

The problems with after-the-fact analyses are well known. I reckon there should be a strict mathematical proof why those are wrong. We just got yet another example of that. How else can you keep charlatans out if not by being rough on those who break the rules? Especially when breaking the rules possibly has lethal consequences for less sophisticated yet desperate consumers.

I'm not saying Harkonen is a charlatan, but he certainly jumped the gun. For whatever reason, but he did.

I'll give it another read in light of what you're saying, but in the meantime, to clarify one line:

"I don't presume it has anything to do with Bezos, but if it does, I hope stays on a buying spree!"

I don't presume it has anything to do with Bezos, either. I would assume (?) he hasn't really taken the reins yet, at any rate. I've since edited that statement for clarity. Not trying to blame him for anything, or attribute this to him in any way. Rather, I am hopeful that the quality improves under him.

I've reread the article a few times since reading your post, and while I might moderate the severity with which I first lashed at the author, I stand by my assessment. Here's my rationale:

1) The tone of the article is skewed in Harkonen's favor right from the outset, particularly in the way the author initially and repeatedly presumes the purity of Harkonen's motives ("saving lives"). In fact, we have no evidence of any motives other than the financial ("billion-dollar market," "$500 million opportunity," etc.).

2) Harkonen's methodology was flawed, full stop. While there might well have been something promising in the cohort he analyzed, a remainder cohort from one study does not take the place of a full set of clinical trials. Medical and statistical ethics would demand another study be conducted of the cohort Dr. Harkonen identified as more promising. Jumping the gun was unwarranted and possibly reckless. (As it turns out, a full study was later conducted, and indeed it proved the recklessness with which Harkonen leapt to his early conclusions).

3) Harkonen grossly misstated the results of the study in his press release, and, while it's hard to prove his intent, he is on record stating his financial upside in so doing. The author devotes all of a couple lines to this crucial point, then trots out a couple of irrelevant character-witness quotes to pave it over. (One quote essentially states 'If this is a crime, then a lot of scientists are in trouble!' Well, it is a crime. And if lots of other scientists are similarly cherrypicking their data and making misleading statements about it, then I'd say we have a serious problem on our hands.)

4) Your point about the closing statement in the article doesn't indicate absence of bias in favor of Harkonen. It adds some balance, yes. But it's buried at the bottom of the piece, where the typical reader either won't find it, or will glaze over it.

5) The opening paragraphs of the article, by contrast, paint a tragic portrait of an eminent and well-meaning doctor humiliated and punished for the crime of simply being overeager to help people (with the strong implication that his humiliation and punishment were unjust).

6) The author makes the repeated assertion that there is 'nothing factually inaccurate' about the content of Harkonen's press release. This is true by the letter, but not by the spirit. Harkonen cherrypicked the "facts" that he portrayed, accurately or otherwise, in that press release. And the later study bears out the potentially disastrous consequences of his having done so, negligently and recklessly.

I'll grant you that "fraud" seems a little extreme in this case, and that's where the controversy lies. But we can demonstrate mens rea, and at the very least, Harkonen committed an act of criminal negligence. That he stood to gain significantly from his misleading statements is strong circumstantial evidence in favor of fraud, though the article doesn't give us enough facts to make a definitive claim there. At the very least, we see no evidence that Harkonen was doing this to save lives -- a point the author strongly and repeatedly implies throughout the article.

We are likely starting from different points. I see a media that almost universally amplifies the hype of scientific press releases, and I'm overjoyed to see an article that (I feel) critically examines the details. Perhaps my bias against "science by press release" is so strong that I don't even register the attempts to exonerate Harkonen.

  And if lots of other scientists are similarly
  cherrypicking their data and making misleading statements
  about it, then I'd say we have a serious problem on our
  hands.

I feel this is where we are at, with Harkonen's approach almost at the middle of the road. This isn't to imply his behaviour is acceptable, just that we have a very serious problem. Have you read John Ioannidis' essay "Why Most Published Research Findings Are False"? http://www.plosmedicine.org/article/info:doi/10.1371/journal...

  At the very least, we see no evidence that Harkonen was
  doing this to save lives -- a point the author strongly
  and repeatedly implies throughout the article.

I guess I didn't see that message as strongly as you. I felt it was granted that he had a strong vested interest:

  The prosecutors also emphasized that Harkonen had a
  financial motive for spinning the study in the most
  positive way. This wasn’t hard to find. The third
  paragraph of the press release said: “We believe these
  results will support use of Actimmune and lead to peak
  sales in the range of $400-$500 million per year,
  enabling us to achieve profitability in 2004 as planned.”

But you are right that this wasn't emphasized. I think for me the hard point is trying to understand the boundary between ethics and law. I worry that asking a lay jury to decide guilt based on the contextual interpretation of p-values isn't going to end well.

I also wonder to what extent Harkonen was optimistically self-delusional versus blatantly fraudulent. I presume that as well as desiring financial success, he truly hoped he had found a useful cure for a dread disease. Along that lines, I recently read a great article by a 1930's Nobel Prize winner entitled "Pathological Science" on how even good scientists can occasionally fool themselves into seeing results that just aren't there: http://yclept.ucdavis.edu/course/280/Langmuir.pdf

If the blurriness is too much, there is a good summary here: http://www.columbia.edu/cu/21stC/issue-3.4/turro.html

Thanks for the discussion.