Paul Graham has a very interesting article on the schedules of the manager and maker. As someone expected to perform both functions at the same time I find the tension between the two rather intense. I often end up asking whether I will ever be able to do any of the work I have to meet about.
Bonus: Stephen Dubner's take. He's in a similar position, only serially.
Thursday, July 30, 2009
Saturday, July 11, 2009
Causal inference and biostatistics
I've been following the discussion on causal inference over at Gelman's blog with quite a bit of interest. Of course, this is in response to Judea Pearl's latest book on causal inference, which differs quite a bit from the theory that had been forwarded by Donald Rubin and his colleagues for the last 35 years or so.
This is a theory that I think deserves more attention in biostatistics. After all, it goes back to the root of why we are studying drugs. Ultimately, we really don't give a damn about whether outcomes are better in the treated group than in the placebo group. Rather, we are more interested in whether we are able to benefit individuals by giving them a treatment. In other words, we are interested in the unknowable quantity of what each person's outcome is if they are treated and what it is if they are not. If there's an improvement and it outweighs the (unknowable) risks, the drug is worth while. The reason we are interested in outcomes of a treated group and outcomes of a placebo group is that it's a surrogate for this unknowable quantity, especially if you use the intention-to-treat principle. However, as mentioned in the linked article and the research by Rubin, the intention to treat principle fails to deliver on its promise despite its simplicity and popularity.
Some clinical trials are now being run with causal inference as a central part of the design. Tools such as R and WinBUGS and Bayesian concepts now make this logistically feasible. Further advances in statistical handling of partial compliance to treatment, biological pathways of drugs, and the intention to treat principle itself make causal inference look much more desirable by the day. It's really only inertia caused by the popularity and (apparent) simplicity of intention to treat that makes this concept slower to catch on.
This is a theory that I think deserves more attention in biostatistics. After all, it goes back to the root of why we are studying drugs. Ultimately, we really don't give a damn about whether outcomes are better in the treated group than in the placebo group. Rather, we are more interested in whether we are able to benefit individuals by giving them a treatment. In other words, we are interested in the unknowable quantity of what each person's outcome is if they are treated and what it is if they are not. If there's an improvement and it outweighs the (unknowable) risks, the drug is worth while. The reason we are interested in outcomes of a treated group and outcomes of a placebo group is that it's a surrogate for this unknowable quantity, especially if you use the intention-to-treat principle. However, as mentioned in the linked article and the research by Rubin, the intention to treat principle fails to deliver on its promise despite its simplicity and popularity.
Some clinical trials are now being run with causal inference as a central part of the design. Tools such as R and WinBUGS and Bayesian concepts now make this logistically feasible. Further advances in statistical handling of partial compliance to treatment, biological pathways of drugs, and the intention to treat principle itself make causal inference look much more desirable by the day. It's really only inertia caused by the popularity and (apparent) simplicity of intention to treat that makes this concept slower to catch on.
Wednesday, July 1, 2009
PK/PD blogging
Well, it seems that for any topic, there is someone willing to blog it. In this particular case, that is a very good thing. Via Derek Lowe's excellent blog, I found someone blogging on pharmacokinetics and pharmacodynamics (PK/PD). Drug development is very inefficient as it stands right now, and PK/PD modeling and simulation is one up and coming way to make it more efficient. I'll look forward to seeing what the author has to say in the upcoming weeks.
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