STATISTICAL CONSIDERATIONS IN POSTMARKETING SAFETY EVALUATION
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STATISTICAL CONSIDERATIONS IN POSTMARKETING SAFETY EVALUATION A. Lawrence Gould Merck Research Laboratories West Point, PA [[email protected]] FDA/Industry Workshop 29 September 2006 Washington, DC. OVERVIEW. Spontaneous AE Reports.

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STATISTICAL CONSIDERATIONS IN POSTMARKETING SAFETY EVALUATIONA. Lawrence GouldMerck Research LaboratoriesWest Point, PA [[email protected]]FDA/Industry Workshop29 September 2006Washington, DC


Overview
OVERVIEW EVALUATION


Spontaneous ae reports
Spontaneous AE Reports EVALUATION

  • Clinical trial safety information is limited & relatively short duration

  • Safety data collection continues after drug approval

    • Detect rare adverse events

    • Obtain tolerability information in a broader population

  • Large amount of low-quality data collected

    • Not usable for trt comparisons or risk assessment

    • Unknown sensitivity & specificity

  • Evaluation by skilled clinicians & epidemiologists

  • Long history of research on issue


Information available postmarketing
Information Available Postmarketing EVALUATION

  • Previously undetected adverse and beneficial effects that may be uncommon or delayed, i.e., emerging only after extended treatment

  • Patterns of drug utilization

  • Effect of drug overdoses

  • Clinical experience with study drugs in their “natural” environment


The pharmacovigilance process
The Pharmacovigilance Process EVALUATION

Traditional

Methods

Data

Mining

Detect Signals

Generate Hypotheses

Insight from

Outliers

Public Health

Impact, Benefit/Risk

Refute/Verify

Type A

(Mechanism-based)

Estimate

Incidence

Act

Inform

Type B

(Idiosyncratic)

Restrict use/

withdraw

Change Label


Considerations issues an incomplete list
Considerations & Issues (An Incomplete List!) EVALUATION

  • Incomplete reports of events, not reactions

  • Bias & noise in system

  • Difficult to estimate incidence because no. of pats at risk, pat-yrs of exposure seldom reliable

  • Significant under reporting (esp. OTC)

  • Synonyms for drugs & events → sensitivity loss

  • Duplicate reporting

  • No certainty that a drug caused the reaction reported

  • Cannot use accumulated reports to calculate incidence, estimate drug risk, or compare drugs


Data mining
DATA MINING EVALUATION


Data mining is a part of pharmacovigilance
Data Mining is a Part of Pharmacovigilance EVALUATION

  • Identify subtle associations (e.g., drug+drug+event) and complex relationships not apparent by simple summary

  • Identify potential toxicity early

  • Finding ‘real’ D-E associations similar to finding potential active compounds or expressed genes – not exactly the same (no H0) – more like model selection

  • Still need initial case review

    respond to reports involving severe, potential life-threatening events eg., Stevens-Johnson syndrome, agranulocytosis, anaphylactic shock

  • Clinical/biological/epidemiological verification of apparent associations is essential


Typical data display
Typical Data Display EVALUATION

Basic idea:

Flag when

R = a/E(a) is “large”

  • Some possibilities

  • Reporting Ratio: E(a) = nTD nTA/n

  • Proportional Reporting Ratio: E(a) = nTD c/nOD

  • Odds Ratio: E(a) = b  c/d

  • Need to accommodate uncertainty, especially if a is small

  • Bayesian approaches provide a way to do this


  • Currently used bayesian approaches
    Currently Used Bayesian Approaches EVALUATION

    • Empirical Bayes (DuMouchel, 1998) & WHO (Bate, 1998)

    • Both use ratio nij / Eij where

      nij = no. of reports mentioning both drug i & event j

      Eij = expected no. of reports of drug i & event j

    • Both report features of posterior dist’n of ‘information criterion’

      ICij = log2 nij / Eij = PRRij

    • Eij usually computed assuming drug i & event j are mentioned independently

    • Ratio > 1 (IC > 0)  combination mentioned more often than expected if independent


    Comparative example dumouchel 1998
    Comparative Example (DuMouchel, 1998) EVALUATION

    • No. Reports = 4,864,480, Mentioning drug = 85,304



    Result from 6 years of reports on lisinopril
    Result From 6 Years of Reports on Lisinopril EVALUATION

    Events w/Lower 5% RR Bnd > 2 (Bold  N  100)



    Accumulating information over time
    Accumulating Information over Time EVALUATION

    • Lower 5% quantiles of RR stabilized fairly soon


    Time sliced evolution of risk ratios
    Time-Sliced Evolution of Risk Ratios EVALUATION

    • See how values of criteria change over time within time intervals of fixed length

    Change in ICij for reports of selected events on A2A from 1995 to 2000

    tension =

    hypotension

    failure =

    heart failure

    kalemia =

    hyperkalemia

    edema =

    angioedema


    Masking of ae drug relationships 1
    Masking of AE-Drug Relationships (1) EVALUATION

    • Company databases smaller than regulatory databases, more loaded with ‘similar’ drugs

      eg, Drug A is 2nd generation version of Drug B, similar mechanism of action, many reports with B

    • Elevated reporting frequency on Drug B could mask effect of Drug A

    • May be useful to provide results when reports mentioning Drug B are omitted



    Example 2 vaccine vaccine interaction
    Example 2: Vaccine-Vaccine Interaction EVALUATION

    • From FDA VAERS database, reports from 1990-2002

    • Intussusception is a serious intestinal malady observed to affect infants vaccinated against rotavirus

    • Look at reports of intussusception that mention rotavirus vaccine (RV) and DTAP vaccine

    • DTAP is a benign combination vaccine commonly administered to infants

    • Demonstration question: Intussusception very commonly reported with RV – but does the reporting rate depend on whether DTAP was co-administered?

    • Not easy to address using standard pharmacovigilance procedures


    Outline of analysis
    Outline of Analysis EVALUATION

    • Standard tools provide intussusception reporting rate for pairs of vaccines, and for vaccines singly

    • Result is a 3-way count table (corresponding to RV + or -, DTAP + or -, and intussusception + or -)

    • Use log-linear model to see if intussusception is mentioned with the two vaccines together more often than the separate vaccine-intussusception reporting associations would predict

    • Turns out that there is an association – Likelihood ratio chi-square is 17.41, 1 df, highly significant



    Comments
    Comments EVALUATION

    • Intussusception seems to be reported more often than expected when RV and DTAP are given together than when RV is given without DTAP, after adjusting for individual vaccine-intussusception associations

    • Reports of intussception without RV are very rare, about 4.5/10,000 reports if RV is not mentioned

    • The joint effect of RV and DTAP on intussusception reporting is small, but does reach statistical significance

    • Not clear that apparent association means anything -- actual synergy between RV and DTAP seems unlikely, but explanation requires clinical knowledge


    A new bayesian approach gould biometrical journal 2006 to appear
    A NEW BAYESIAN APPROACH EVALUATION(Gould, Biometrical Journal 2006, to appear)


    Model for process generating observations
    Model for Process Generating Observations EVALUATION

    • ni = no. of reports mentioning i-th drug-event pair ~ Poisson (true for EB approach as well)

      f(ni | Ei, i) = fPois(ni ; iEi)

    • i drawn from a gamma(a0, b0) distribution or from a gamma(a1, b1) distribution

      • A model selection problem

      • Dist’ns reflect physician/epidemiologist’s judgment as to what range of  values corresponds to ‘signals’, and what does not

    Expected count

    under independence

    Association measure


    Prior model density of
    Prior/Model Density of EVALUATION

    • Bayes approach starts with a random mixture of gamma densities,

      f0( ; , a0, b0, a1, b1)

      = (1 - )fgam(; a0, b0) + fgam(; a1, b1)

      Use value of Ppost(g = 1) for inference

    • EB approach starts with expectation wrt  given p  nonrandom mixture of gamma densities,

      f0( ; p, a0, b0, a1, b1)

      = pfgam(; a0, b0) + (1-p)fgam(; a1, b1)

      Use quantiles of posterior dist’n of  for inference

    Analyst specifies parameter values

    Data determine parameter values


    Comments1
    Comments EVALUATION

    • Bayes and EB approaches both model strength of drug-event reporting assn as a gamma mixture

    • Diagnostic properties of Bayes method can be determined analytically or by simulation

    • Unknown separation of the true alternative dist’ns for  more important than prior dist’n used for analysis

    • Methods described here can be applied to other models – Scott & Berger (2005) used normal distributions – could also use binomial instead of Poisson, beta instead of gamma distributions to develop screening methods for AEs in clinical trials


    Discussion
    DISCUSSION EVALUATION


    Discussion1
    Discussion EVALUATION

    • Bayesian approaches may be useful for detecting possible emerging signals, especially with few events

    • MCA (UK) currently uses PRR for monitoring emergence of drug-event associations

    • Signal detection combines numerical data screening, statistical interpretation, and clinical judgement

    • Most apparent associations represent known problems

    • ~ 25% may represent signals about previously unknown associations

    • The actual false positive rate is unknown


    What next
    What Next? EVALUATION

    • PhRMA/FDA working group has published a white paper addressing many of these issues

      Drug Safety (2005) 28: 981-1007

    • Further refine methods, look for associations among combinations of drugs and events, timing of reports

    • Data mining is like screening, need to evaluate diagnostic properties of various approaches

    • Need good dictionaries: many synonyms  difficult signal detection

      • Event names: MedDRA may help

      • Drug names: Need a common dictionary of drug names to minimize dilution effect of synonyms



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