Presentation on theme: "What Is the Role for Analyses of Administrative Data in Assessing Drug Safety in Post-Market Commitment (PMC) Studies? Cathy W. Critchlow, PhD Global Epidemiology,"— Presentation transcript:
1 What Is the Role for Analyses of Administrative Data in Assessing Drug Safety in Post-Market Commitment (PMC) Studies?Cathy W. Critchlow, PhDGlobal Epidemiology, Amgen, Inc.September 29, 2006
2 OutlineWhy should we consider additional approaches (e.g., analyses of administrative data) to post-market commitment studies?What are situations where analyses of administrative data can be used to supplement, or even replace, clinical post-market commitment studies?Study design issuesStrengths and limitations of administrative data analyses as a component of post-market surveillance
3 Post-Market Surveillance – Continuing Assessments of Safety or Efficacy Post-market commitment studiesRoutine surveillanceSpontaneous reportsUnexpected or rare AEsRegistries or studies conducted to ‘complete’ pre-market assessmentsObservational studiesRate of expected/unexpected AEsRelative rate of ‘hard’ endpointsDynamic response to emerging issues, hypothesesMany examples???Can analyses using automated databases help meet these needs?
4 Need for Reevaluation of Post-Market Assessment Strategies Withdrawal of Cox-2 inhibitors after several years and several million patient exposures contributes to “perception of crisis that has compromised the credibility of FDA and the pharmaceutical industry”*Public’s loss of faith in the ability of industry to deliver safe and effective drugs†13% think pharmaceutical companies are “generally honest and trustworthy”60% not confident that drug companies will publicly disclose safety data in a timely manner*IOM Report: The Future of Drug Safety: Promoting and Protecting the Health of the Public†Harris Poll, 2004
5 Need for Reevaluation of Post-Market Assessments Strategies (2) Reliance on regulation alone to demonstrate long-term safety has not workedUnmet phase 4 commitments114 (9.6%) of 1,191 open PMCs met*Confirmation of efficacy using hard endpoints in phase 4 commitment studies for drugs receiving “fast-track” approval based on surrogate measuresFDA Critical Path InitiativeUse of database registries and electronic medical record systems to compare outcomes among relevant patient groups in post-market drug evaluations* Federal Register 2005;70:
6 Crux of the Issue….. Post-Approval Drug Safety Typically, patients exposed to drug in phase 3 testingDrug effects detectable with an incidence ~1–6 per 1000To quantify the risk of an event with incidence of 2 per 10,000/year (precision ±1 per 10,000) with 95% probability, need ~80,000 subjects followed for 1 yearDifficult to conduct studies this large in a timely fashion
7 OpportunitiesConsider additional or alternative strategies to demonstrate long-term drug safety or efficacyAnalyses of administrative data* in post-market surveillanceCollaboratively establish high standards for the conduct of observational data analyses conducted as part of post-market commitmentsDemonstrate safety and effectiveness of drugs in ‘real-world’ settings*Claims data (commercial insurance, Medicare, Medicaid), medical record data, national databases
8 Qualities of the Ideal Database ComprehensiveInpatient & outpatient care; ER visits; lab & radiological tests; prescribed & OTC drugs; mental health care; alternative therapyLarge, stable populationUnique identifiers for linkageRegular, frequent updatesVerifiable, reliableCapacity for chart review or patient interviewsConfounder dataCompliance
9 But, few databases are ideal….(some are better than others) Problematic situationsIllnesses that do not reliably come to medical attentionInpatient drug exposuresOutcomes poorly defined by ICD-9 codingWhen necessary confounder data cannot be obtainedVery long latency eventsNeed to understand the limitations of any databasePurpose for which database was createdData quality, validity, completenessAvailability of confounder dataPatient follow-upAccess to source information
10 Issues to Consider in the Design of PMCs What is the objective?Hypothesis generation (descriptive or exploratory) vs. confirmation?Evaluating expected vs. unexpected eventsApriori specification of events of interestTiming of events of interestShort-term vs. long latency outcomes
11 Most PMCs are Observational….. Issues of Study Validity BiasSelection biasInformation biasMisclassification of covariates, exposure, outcomeData validityConfoundingBy disease severity, treatment indication, comorbid conditionsUnmeasured covariatesTime-dependent confoundingPhysician prescription patterns (‘channeling’)Dosing variability according to patient responsivenessWhat are appropriate comparator groups?
12 Other Considerations…… Urgency of need for dataDrug first in class or are other relevant data available?Risk vs. benefit profileNumbers of persons to be exposedExpected AE incidence rateSignal detection – what constitutes a safety signal?Implications for study design, sample size, comparators, interim analyses, scientific rigor required
13 What are situations where analyses of administrative data can be used to supplement, or even replace, clinical PMC studies?
14 PMC Scenario 1: Single-Arm Prospective, Observational Clinical Registry Characterize long-term safety profile of approved drugIncidence of various adverse eventsDrug utilization in the ‘real-world’Special populations, e.g., childrenEffect of comorbid conditionsDrug-drug interactions
15 PMC Scenario 1: Single-Arm Prospective, Observational Registry Objective: Characterize drug safety post-approvalClinical RegistryHypothesis generatingPre-specified outcomes; unexpected eventsModest sample sizeWill not observe rare eventsEffect measureAbsolute incidence rateSIR (external comparator)Prospective data collectionDifficult to assess long latency eventsVirtual (Database) RegistryHypothesis confirmationPost-hoc analyses of unexpected eventsLarge sample sizeStudy rare eventsEffect measureAbsolute risk in select populationRelative risk (internal comparator)Retrospective study designPotential for answers sooner
16 PMC Scenario 1: Single-Arm Prospective, Observational Registry (2) Objective: Characterize drug safety post-drug approvalObjective: Characterize drug safety post-drug approvalClinical RegistryData qualityOutcome adjudicationCovariate data can be obtainedRegulatory definitions of AEsSources of bias, confoundingPotential selection, recall or information biasDifferential loss-to-followup with respect to risk of outcomes?Virtual (Database) RegistryData qualityValidity of algorithms assessing drug exposure, disease severity, outcomesComparable ascertainment of data from exposed and comparator groupsData from all medical care providersSources of bias, confoundingRelevant covariate data available?Confounding by indication for treatment, comorbiditiesStability of population
17 Objective: Characterize drug safety post-drug approval When/What Could Analyses of Administrative Data Contribute to this PMC Scenario?Objective: Characterize drug safety post-drug approvalWhen…Large sample size needed to assess rare eventsEvents specified aprioriObjective lab-driven diagnosesConfounder data availableDenominator needed to calculate population incidence ratesWhat…..Background incidence ratesAttributable risk of events
18 PMC Scenario 2: Controlled Studies Further Assessing Efficacy Obtain additional data regarding meaningful clinical endpointsConfirm estimates of efficacy of drugs receiving “fast-track” approval based on surrogate measuresHead-to-head comparisonsNew vs. existing drug
19 PMC Scenario 2: Controlled Studies Further Assessing Efficacy Objective: Compare incidence of clinical (efficacy) endpoints among exposed and unexposed groupsClinical StudyModest sample sizeExpense of large study of infrequent outcomesEffect measureRelative risk compared to placebo or standard of careProspective data collectionPotential ethical issues in randomized trial with placebo control and/or long follow-upDatabase StudyLarge sample sizeStudy rare eventsEffect measureRelative riskMore easily do ‘head-to-head’ comparisonsRetrospective cohort studyPotential for answers sooner
20 PMC Scenario 2: Controlled Studies Further Assessing Efficacy (2) Objective: Compare incidence of clinical (efficacy) endpoints among exposed and unexposed groupsClinical StudyData qualityOutcome adjudicationCovariate data can be obtainedSources of bias, confoundingPotential selection, recall or information biasDifferential loss-to-followup with respect to risk of outcome?Database StudyData qualityValidity of algorithms assessing drug exposure, disease severity, outcomesSources of bias, confoundingUnmeasured confounders?Confounding by indication for treatment, comorbiditiesStability of population
21 When/What Could Analyses of Administrative Data Contribute to this PMC Scenario? Objective: Compare incidence of clinical (efficacy) endpoints among exposed and unexposed groupsWhen…Large sample size needed to assess rare outcomes, long-latency outcomesValid ascertainment of outcomeShort-term effectsRecall or interviewer bias could effect associationWhat…..Timely validation of surrogate markersHead-to-head comparison of outcome incidence
22 Opportunities - Revisited Potential role for analyses of administrative data in post-market surveillanceCollaborative establishment of regulatory thresholds for the conduct, analysis and interpretation of observational data analyses conducted as part of post-market commitmentsDemonstrate safety and effectiveness of drugs in timely fashion