Statistics in Drug Regulation: The Next 10 Years
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Statistics in Drug Regulation:The Next 10 Years
Thomas PermuttDirector, Division of Biometrics II
Center for Drug Evaluation and Research
The views expressed are those of the speaker and not necessarily of FDA.
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Statutory Standards• Substantial evidence of efficacy• All tests reasonably applicable for safety• Balance not explicit, but history clear
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Risk/Benefit• Formerly:
– Very good evidence about direction of mean treatment effect
• Too good? No.– Adverse events:
• Common: statistical but unimportant• Rare: nonstatistical but important
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What’s New?
• Rofecoxib• Rosiglitazone• LABA
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Rofecoxib• Heart attacks• Large outcome trial
– which was trial in new indication• Now need outcome studies for COX-2 and
maybe nonselective
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Rosiglitazone• Nissin meta-analysis• We do meta-analysis• You do meta-analysis• You do outcome trial, maybe
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Meta-analysis• Hard• Nonstatistical• Statistical• Both different in regulatory setting
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Meta-analysis: Nonstatistical• Better information, but …• Doesn’t fit usual protocol-driven regulatory
framework, either• Do it anyway, but …• Nobody will believe you (or us), so … ?
– sensitivity analysis important
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Meta-analysis: Statistical• Fixed vs. random effects
– doesn’t matter much for global null, but– this doesn’t apply to noninferiority
• Attributable vs. relative risk– relative risk “stable” across settings
• different length of study, at least– but attributable risk is what matters– what about zeroes
• Nissin to Congress: “no information”
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What triggers this?• “Signal”
– Class effects– Someone else’s meta-analysis
• For diabetes, everything• For COX-2, probably everything
– other COX?
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LABA• Believed to cause death
– not “side effect,” death from asthma• Effect mostly “seen” without steroid• So, with steroid?
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With Steroid, Show What?• Noninferior to nothing?
– i.e., combination therapy vs. steroid• Noninferior to realistic alternative?
– e.g., increased dose of steroid– why not superior?
• because of benefit
• Interaction with steroid?– i.e., already “know” without steroid: Is with different?– maybe can’t do without steroid anyway
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Noninferiority Margins• Not “1.3”
– COX-2– diabetes– asthma!
• Risk-benefit– for direct measures– for surrogates
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Surrogate• Everyone likes “hard” endpoints but …• They mostly don’t measure benefit• They are correlated with benefit
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Correlation with Benefit• Does drug produce benefit or modify
correlation? (anti-arrythmics, maybe glitazones)
• Qualitative validation hard enough• Quantify benefit very hard
– estimate strength of relationship– and hope it holds
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Patient-Reported Outcomes• Hard endpoints are “nice” but they don’t
measure utility• PRO are squishy but relevant• Psychometrics is not evil (now)
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Linking Risk and Benefit• Expected utility
– mean efficacy outcome– incidence of AE– (mean effect) X (goodness) – (AE rate) X
(badness)• Other formulas are incorrect
– provided utility is linear wrt effect
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It Isn’t Linear• For surrogates• For PROs
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Utility Calculations: Example• 50% symptom-free• 50% intolerable adverse events• Good or bad?
– How bad were symptoms?– How bad were adverse events?
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Two Drugs• Women have efficacy• Men have adverse
events
• Women have efficacy• Women have adverse
events• Men have nothing
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Two Drugs• Women have efficacy• Men have adverse
events• Useful drug
– provided AEs are reversible
• Women have efficacy• Women have adverse
events• Men have nothing• Useless drug
“Expected utility” does not distinguish!
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Why Doesn’t Expectation Work?• Because you don’t really measure benefit
– benefit at timepoint (or average over time) is surrogate for long-term benefit
– don’t get long-term benefit if you drop out– LOCF makes it worse
• “Mixing up” safety and efficacy is …– not illegal– not even stupid– “individualized medicine”
• dropout is good biomarker!