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DBL%20Hendrix%20small.png College chemistry, 1983

Derek Lowe The 2002 Model

Dbl%20new%20portrait%20B%26W.png After 10 years of blogging. . .

Derek Lowe, an Arkansan by birth, got his BA from Hendrix College and his PhD in organic chemistry from Duke before spending time in Germany on a Humboldt Fellowship on his post-doc. He's worked for several major pharmaceutical companies since 1989 on drug discovery projects against schizophrenia, Alzheimer's, diabetes, osteoporosis and other diseases. To contact Derek email him directly: derekb.lowe@gmail.com Twitter: Dereklowe

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May 31, 2006

As Merck Caroms Off Another Tree

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Posted by Derek

You have to figure that Merck is getting tired of restating and re-explaining its Vioxx numbers. I certainly am. The only people who aren't fed up with it, I'm sure, are the hordes of lawyers for the plaintiffs. They're munching popcorn and waving pom-poms as Merck staggers around in circles.

The latest episode is a statistical mixup of the APPROVe trial, which I last wrote about here. In the NEJM publication of the results and in Merck's submission of the data to the FDA, they mention using log(time) as a variable in their primary statistical method. But they report statistical tests in the paper which used a model with plain old linear time as a variable. Merck says that "The results of diagnostic steps specified in that data analysis plan indicate that the linear test is an appropriate method to assess changes in the relative risk over time", although they'd surely rather not have to backtrack and make that argument.

This issue affects the measurement of the change in relative risk over time, not the magnitude of the risk itself. Merck's taking pains to point out that the overall magnitude of those relative risks were described correctly. That's fine, I guess, as far as it goes. The problem is, Merck has already been making a big deal out of that change in risk with time, namely that patients weren't at risk unless they'd been taking Vioxx for at least 18 months. So this is, unfortunately for them, a relevant issue.

What's the difference come to? For the difference between risk levels before and after the 18-month threshold, Merck reported a p-value of 0.01 using linear time, but if you run the method the way it's actually outlined in the paper (log time), you get p = 0.07, which is certainly worse. In fact, in my experience, you start losing your audience at p values of 0.03 or 0.05, and that's what seems to be happening. When Merck says that this error does not affect the conclusions of the study, they're only partly correct. What it affects are the believability of the conclusions, and once again, the revision makes things look worse for them.

Honestly, guys. What's with you these days?

Comments (3) + TrackBacks (0) | Category: Cardiovascular Disease


COMMENTS

1. MTK on June 1, 2006 9:46 AM writes...

Hhhmmm. Interesting. Using Merck's own "not statistically significant" defense, this means that the increased risk is the same whether you used it for 18 months or not, correct? (Someone help here) If so, that throws a spanner into the works doesn't it. They can't rightly use the not statistically significant argument when it helps them, then toss it away when it doesn't. Juries will see throw that charade.

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2. LNT on June 1, 2006 9:57 AM writes...

Wow -- it just seems to be getting worse and worse for Merck.

What do you think the ultimate outcome of all this will be? If the floodgates open for short-term users of Vioxx, do you think that the company will be able to survive? Once the damages start hitting the tens of billions of dollars, that's a serious chunk out of the market cap of the company (currently sitting at $73 billion).
I'm just glad not to be a scientist at Merck right now. But I have a (bad) feeling that this situation will impact the whole pharmaceutical sector, not just Merck.

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3. JSinger on June 2, 2006 4:25 PM writes...

But they report statistical tests in the paper which used a model with plain old linear time as a variable.

I'd say that's suspicious right there -- when have you ever heard of a statistician passing up the chance to log-transform something?

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