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Unsafety: Making no mockery of honest ad-hockery

Unsafety: Making no mockery of honest ad-hockery. Janet Wittes Statistics Collaborative ASA/FDA 2005. Topic du jour. Sleight of tongue. Game: Remove safety and add virtue in two easy steps D S M C _ _ _ _ _ _ _. Sleight of tongue.

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Unsafety: Making no mockery of honest ad-hockery

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  1. Unsafety: Making no mockery of honest ad-hockery Janet WittesStatistics Collaborative ASA/FDA 2005

  2. Topic du jour

  3. Sleight of tongue • Game: Remove safety and add virtue in two easy steps DSMC _ _ _ _ _ _ _

  4. Sleight of tongue • Game: Remove safety and add virtue in two easy steps DSMC DMC _ _ _ _

  5. Sleight of tongue • Game: Remove safety and add virtue in two easy steps DSMC DMC IDMC

  6. Efficacy • Stop for “overwhelming” efficacy • =0.05; power = 90%; four looks • Probability of stopping early>70% • Early stop: estimate pulls toward null

  7. Our current approaches • Prespecify • Classify precisely • Give lots of data • Rely on mechanism

  8. Divide and (un)conquer (e.g. Neuropathy)

  9. e.g. Neuropathy

  10. e.g. Neuropathy

  11. Other examples • Heart failure • Separate near synonyms • Allocate to heart and lung

  12. Other examples • Heart failure • Separate near synonyms • Allocate to heart and lung • Bleeding: distribute over body systems

  13. Consider mechanism • If you don’t get the drug, you can’t react to it • Eschew ITT: safety population • Modified Daley’s Rule: censor early and often • Don’t collect extraneous information • Appeal to statistical conservatism

  14. Populations • ITT • mITT • Safety: one dose of study med • ATP • Etc.

  15. e.g., Vioxx- short follow-up

  16. Through 36 months

  17. With denominators • Bresalier et al., NEJM, Feb 2005

  18. The denominators Mo Rx Po 0 1287 1299 12 1129 1195 18 1057 1156 24 938 1042 30 896 1001 36 727 835

  19. No data dredging • We test hypotheses • Too many type 1 errors if we dredge

  20. What we typically present • Current behavior • Same tables for interim & final analyses • Long complete listings • What we should do • Interim data are different from final • Presenting too much dulls the mind

  21. Sentinel events • Single event (e.g., death in a vaccine trial) • Several events (e.g., 3 retinal thromboses) • Sentinel event rate (e.g., WHI) Lachenbruch and Wittes, SIM, to appear

  22. How to handle • Identify sentinel event • Establish stat’l method for future events • Need reasonable power • Type I error rate > 1-sided 0.025

  23. Methods • Individual • Number of non-events until the k’th event • Negative binomial • SPRT • Time to the next (or k’th) event (-dist’n) • Rate: event rate in future patients • Normal • Poisson

  24. Pitfalls • Time is subtle • Power is low • Censoring is tricky

  25. Pull-up • Safety hypothesis: • E.g., Cox-2 leads to 2 fold increase in MI etc. • Design: stop early if you reject • Estimated relative risk must be pulled toward 2 • Insight from Joe Heyse

  26. The option • Respect PI: Adjudicate adverse events • Precision: Reclassify, reorganize • Mechanism: Be an empiricist • Dredging: Use sentinel events

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