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Statistics in Drug Regulation: The Next 10 Years. Thomas Permutt Director, Division of Biometrics II Center for Drug Evaluation and Research. The views expressed are those of the speaker and not necessarily of FDA. Statutory Standards. Substantial evidence of efficacy

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Statistics in drug regulation the next 10 years

Statistics in Drug Regulation:The Next 10 Years

Thomas Permutt

Director, Division of Biometrics II

Center for Drug Evaluation and Research

The views expressed are those of the speaker and not necessarily of FDA.


Statutory standards
Statutory Standards

  • Substantial evidence of efficacy

  • All tests reasonably applicable for safety

  • Balance not explicit, but history clear


Risk benefit
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


What s new
What’s New?

  • Rofecoxib

  • Rosiglitazone

  • LABA


Rofecoxib
Rofecoxib

  • Heart attacks

  • Large outcome trial

    • which was trial in new indication

  • Now need outcome studies for COX-2 and maybe nonselective


Rosiglitazone
Rosiglitazone

  • Nissin meta-analysis

  • We do meta-analysis

  • You do meta-analysis

  • You do outcome trial, maybe


Meta analysis
Meta-analysis

  • Hard

  • Nonstatistical

  • Statistical

  • Both different in regulatory setting


Meta analysis nonstatistical
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


Meta analysis statistical
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”


What triggers this
What triggers this?

  • “Signal”

    • Class effects

    • Someone else’s meta-analysis

  • For diabetes, everything

  • For COX-2, probably everything

    • other COX?


Statistics in drug regulation the next 10 years
LABA

  • Believed to cause death

    • not “side effect,” death from asthma

  • Effect mostly “seen” without steroid

  • So, with steroid?


With steroid show what
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


Noninferiority margins
Noninferiority Margins

  • Not “1.3”

    • COX-2

    • diabetes

    • asthma!

  • Risk-benefit

    • for direct measures

    • for surrogates


Surrogate
Surrogate

  • Everyone likes “hard” endpoints but …

  • They mostly don’t measure benefit

  • They are correlated with benefit


Correlation with benefit
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


Patient reported outcomes
Patient-Reported Outcomes

  • Hard endpoints are “nice” but they don’t measure utility

  • PRO are squishy but relevant

  • Psychometrics is not evil (now)


Linking risk and benefit
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


It isn t linear
It Isn’t Linear

  • For surrogates

  • For PROs


Utility calculations example
Utility Calculations: Example

  • 50% symptom-free

  • 50% intolerable adverse events

  • Good or bad?

    • How bad were symptoms?

    • How bad were adverse events?


Two drugs

Women have efficacy

Men have adverse events

Women have efficacy

Women have adverse events

Men have nothing

Two Drugs


Two drugs1

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

Two Drugs

“Expected utility” does not distinguish!


Why doesn t expectation work
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!