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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,

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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, PhD Global Epidemiology, Amgen, Inc. September 29, 2006

2 Outline Why 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 issues Study design issues Strengths and limitations of administrative data analyses as a component of post-market surveillance Strengths 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 surveillance Post-market commitment studiesRoutine surveillance Spontaneous reports Unexpected or rare AEs Observational studies Registries or studies conducted to complete pre-market assessments Rate of expected/unexpected AEs Relative rate of hard endpoints Dynamic response to emerging issues, hypotheses Can analyses using automated databases help meet these needs? Many examples ???

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* Publics loss of faith in the ability of industry to deliver safe and effective drugs Publics loss of faith in the ability of industry to deliver safe and effective drugs 13% think pharmaceutical companies are generally honest and trustworthy 13% think pharmaceutical companies are generally honest and trustworthy 60% not confident that drug companies will publicly disclose safety data in a timely manner 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 worked Unmet phase 4 commitments Unmet phase 4 commitments 114 (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 measures Confirmation of efficacy using hard endpoints in phase 4 commitment studies for drugs receiving fast-track approval based on surrogate measures FDA Critical Path Initiative Use of database registries and electronic medical record systems to compare outcomes among relevant patient groups in post-market drug evaluations Use 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 testing Drug effects detectable with an incidence ~1–6 per 1000 Drug effects detectable with an incidence ~1–6 per 1000 To 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 year Difficult to conduct studies this large in a timely fashion

7 Opportunities Consider additional or alternative strategies to demonstrate long-term drug safety or efficacy Analyses of administrative data* in post-market surveillance Analyses of administrative data* in post-market surveillance Collaboratively establish high standards for the conduct of observational data analyses conducted as part of post-market commitments Demonstrate 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 Comprehensive Inpatient & outpatient care; ER visits; lab & radiological tests; prescribed & OTC drugs; mental health care; alternative therapy Inpatient & outpatient care; ER visits; lab & radiological tests; prescribed & OTC drugs; mental health care; alternative therapy Large, stable population Unique identifiers for linkage Regular, frequent updates Verifiable, reliable Capacity for chart review or patient interviews Confounder data Confounder data Compliance Compliance

9 But, few databases are ideal….(some are better than others) Problematic situations Illnesses that do not reliably come to medical attention Illnesses that do not reliably come to medical attention Inpatient drug exposures Inpatient drug exposures Outcomes poorly defined by ICD-9 coding Outcomes poorly defined by ICD-9 coding When necessary confounder data cannot be obtained When necessary confounder data cannot be obtained Very long latency events Very long latency events Need to understand the limitations of any database Need to understand the limitations of any database Purpose for which database was created Purpose for which database was created Data quality, validity, completeness Data quality, validity, completeness Availability of confounder data Availability of confounder data Patient follow-up Patient follow-up Access to source information Access to source information

10 Issues to Consider in the Design of PMCs What is the objective? Hypothesis generation (descriptive or exploratory) vs. confirmation? Hypothesis generation (descriptive or exploratory) vs. confirmation? Evaluating expected vs. unexpected events Apriori specification of events of interest Apriori specification of events of interest Timing of events of interest Short-term vs. long latency outcomes Short-term vs. long latency outcomes

11 Most PMCs are Observational….. Issues of Study Validity Bias Selection bias Selection bias Information bias Information bias Misclassification of covariates, exposure, outcome Data validity Data validityConfounding By disease severity, treatment indication, comorbid conditions By disease severity, treatment indication, comorbid conditions Unmeasured covariates Unmeasured covariates Time-dependent confounding Time-dependent confounding Physician prescription patterns (channeling) Dosing variability according to patient responsiveness What are appropriate comparator groups?

12 Other Considerations…… Urgency of need for data Drug first in class or are other relevant data available? Drug first in class or are other relevant data available? Risk vs. benefit profile Numbers of persons to be exposed Expected AE incidence rate Signal 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 drug Incidence of various adverse events Incidence of various adverse events Drug utilization in the real-world Special populations, e.g., children Special populations, e.g., children Effect of comorbid conditions Effect of comorbid conditions Drug-drug interactions Drug-drug interactions

15 PMC Scenario 1: Single-Arm Prospective, Observational Registry Clinical Registry Hypothesis generating Pre-specified outcomes; unexpected events Modest sample size Will not observe rare events Will not observe rare events Effect measure Absolute incidence rate Absolute incidence rate SIR (external comparator) SIR (external comparator) Prospective data collection Difficult to assess long latency events Difficult to assess long latency events Virtual (Database) Registry Hypothesis confirmation Post-hoc analyses of unexpected events Large sample size Study rare events Effect measure Absolute risk in select population Relative risk (internal comparator) Retrospective study design Potential for answers sooner Objective: Characterize drug safety post-approval

16 PMC Scenario 1: Single-Arm Prospective, Observational Registry (2) Clinical Registry Data quality Outcome adjudication Outcome adjudication Covariate data can be obtained Covariate data can be obtained Regulatory definitions of AEs Regulatory definitions of AEs Sources of bias, confounding Potential selection, recall or information bias Potential selection, recall or information bias Differential loss-to-followup with respect to risk of outcomes? Differential loss-to-followup with respect to risk of outcomes? Virtual (Database) Registry Data quality Validity of algorithms assessing drug exposure, disease severity, outcomes Comparable ascertainment of data from exposed and comparator groups Data from all medical care providers Sources of bias, confounding Relevant covariate data available? Confounding by indication for treatment, comorbidities Stability of population Objective: Characterize drug safety post-drug approval

17 When/What Could Analyses of Administrative Data Contribute to this PMC Scenario? When… Large sample size needed to assess rare events Large sample size needed to assess rare events Events specified apriori Events specified apriori Objective lab-driven diagnoses Objective lab-driven diagnoses Confounder data available Confounder data available Denominator needed to calculate population incidence rates Denominator needed to calculate population incidence ratesWhat….. Background incidence rates Background incidence rates Attributable risk of events Attributable risk of events Objective: Characterize drug safety post-drug approval

18 PMC Scenario 2: Controlled Studies Further Assessing Efficacy Obtain additional data regarding meaningful clinical endpoints Confirm estimates of efficacy of drugs receiving fast-track approval based on surrogate measures Confirm estimates of efficacy of drugs receiving fast-track approval based on surrogate measures Head-to-head comparisons New vs. existing drug New vs. existing drug

19 PMC Scenario 2: Controlled Studies Further Assessing Efficacy Clinical Study Modest sample size Expense of large study of infrequent outcomes Expense of large study of infrequent outcomes Effect measure Relative risk compared to placebo or standard of care Relative risk compared to placebo or standard of care Prospective data collection Potential ethical issues in randomized trial with placebo control and/or long follow-up Potential ethical issues in randomized trial with placebo control and/or long follow-up Database Study Large sample size Study rare events Effect measure Relative risk More easily do head-to-head comparisons Retrospective cohort study Potential for answers sooner Objective: Compare incidence of clinical (efficacy) endpoints among exposed and unexposed groups

20 PMC Scenario 2: Controlled Studies Further Assessing Efficacy (2) Clinical Study Data quality Outcome adjudication Outcome adjudication Covariate data can be obtained Covariate data can be obtained Sources of bias, confounding Potential selection, recall or information bias Potential selection, recall or information bias Differential loss-to- followup with respect to risk of outcome? Differential loss-to- followup with respect to risk of outcome? Database Study Data quality Validity of algorithms assessing drug exposure, disease severity, outcomes Sources of bias, confounding Unmeasured confounders? Confounding by indication for treatment, comorbidities Stability of population Objective: Compare incidence of clinical (efficacy) endpoints among exposed and unexposed groups

21 When/What Could Analyses of Administrative Data Contribute to this PMC Scenario? When… Large sample size needed to assess rare outcomes, long- latency outcomes Large sample size needed to assess rare outcomes, long- latency outcomes Valid ascertainment of outcome Valid ascertainment of outcome Short-term effects Short-term effects Recall or interviewer bias could effect association Recall or interviewer bias could effect associationWhat….. Timely validation of surrogate markers Timely validation of surrogate markers Head-to-head comparison of outcome incidence Head-to-head comparison of outcome incidence Objective: Compare incidence of clinical (efficacy) endpoints among exposed and unexposed groups

22 Opportunities - Revisited Potential role for analyses of administrative data in post-market surveillance Collaborative establishment of regulatory thresholds for the conduct, analysis and interpretation of observational data analyses conducted as part of post-market commitments Demonstrate safety and effectiveness of drugs in timely fashion

23 Thank you!


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