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Risk in the Pharmaceutical Industry
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Topic: Pharmaceutical Industry
Sort Desciption: Risk in the Pharmaceutical Industry. Stan Young. NISS. Susan Ellenberg. University of Pennsylvania. Bob Obenchain. Eli Lilly ...
Content Inside: 1 Risk in the Pharmaceutical Industry Stan Young NISS Susan Ellenberg University of Pennsylvania Bob Obenchain Eli Lilly 2 History – Stan at Eli Lilly Long-term rodent carcinogenicity studies. Rats/mice; males/females; Cont/Trt; hundreds of tumors. NTP 800-1000 statistical tests of hypothesis Statistics – massively excessive statistical testing. Biology – Rats and mice are not men! Peter Westfall – 1985 Biometrics. Adjusted confidence limits . 3 Symmetry To clear a drug: 1. $800,000 to $1.8B. 2. Two 0.05 independent clinical trials. 3. Expert risk/benefit analysis. To kill a drug: Essentially NO rules – wild, wild west. Why not two independent epi studies? 4 Cox-2 Lesson 1. All drugs have side effects. 2. Follow on studies are larger than original clinical trials. 3. New side effects will be found. 4. No drug is save. => Sell your drug stocks. 5 Greatest Dangers Epidemiology Trial lawyers FDA – Government 6 Technical Challenges 1. Massive health data sets. 2. Massive multivariate response. 3. Massive numbers of diffuse predictors. 4. Decision science – risk/benefit. 5. Hidden multiplicity. 7 Multiple testing solutions 1. Test fewer questions – clinical trials. 2. Test and holdout data sets. 3. Resampling-based multiple testing. 4. Require replication – clinical trials. Etc. Repeat findings, not p-values, are the thing. 8 What can you do? 1. Take up a technical challenge. 2. Write letters. Science – data sharing. JAMA – Randomness as a “prime moverâ€. Track www.junkscience.com www.quackwatch.org Come to multiple testing workshop. 9 Individual versus Society Republican Rome versus United States Many/most ethical conundrums are a confusion of individual good versus good for society. 10 Dangers - 2 Ignorance 1. Incorrect knowledge. 2. Poor reasoning. 3. Human focus on one-thing-at-a-time.
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