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
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 commitment studies
Routine surveillance
Spontaneous reports
Unexpected or rare AEs
Registries or studies conducted to ‘complete’ pre-market assessments
Observational studies
Rate of expected/unexpected AEs
Relative rate of ‘hard’ endpoints
Dynamic response to emerging issues, hypotheses
Many 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 worked
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
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
* 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
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
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
Large, stable population
Unique identifiers for linkage
Regular, frequent updates
Verifiable, reliable
Capacity for chart review or patient interviews
Confounder data
Compliance

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

11. Most PMCs are Observational….. Issues of Study Validity
Bias
Selection bias
Information bias
Misclassification of covariates, exposure, outcome
Data validity
Confounding
By disease severity, treatment indication, comorbid conditions
Unmeasured covariates
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?
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
Drug utilization in the ‘real-world’
Special populations, e.g., children
Effect of comorbid conditions
Drug-drug interactions

15. PMC Scenario 1: Single-Arm Prospective, Observational Registry
Objective: Characterize drug safety post-approval
Clinical Registry
Hypothesis generating
Pre-specified outcomes; unexpected events
Modest sample size
Will not observe rare events
Effect measure
Absolute incidence rate
SIR (external comparator)
Prospective data collection
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

16. PMC Scenario 1: Single-Arm Prospective, Observational Registry (2)
Objective: Characterize drug safety post-drug approval
Objective: Characterize drug safety post-drug approval
Clinical Registry
Data quality
Outcome adjudication
Covariate data can be obtained
Regulatory definitions of AEs
Sources of bias, confounding
Potential selection, recall or information bias
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

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 approval
When…
Large sample size needed to assess rare events
Events specified apriori
Objective lab-driven diagnoses
Confounder data available
Denominator needed to calculate population incidence rates
What…..
Background incidence rates
Attributable risk of events

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
Head-to-head comparisons
New vs. existing drug

19. PMC Scenario 2: Controlled Studies Further Assessing Efficacy
Objective: Compare incidence of clinical (efficacy) endpoints among exposed and unexposed groups
Clinical Study
Modest sample size
Expense of large study of infrequent outcomes
Effect measure
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
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

20. PMC Scenario 2: Controlled Studies Further Assessing Efficacy (2)
Objective: Compare incidence of clinical (efficacy) endpoints among exposed and unexposed groups
Clinical Study
Data quality
Outcome adjudication
Covariate data can be obtained
Sources of bias, confounding
Potential selection, recall or information bias
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

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

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!