1 / 37

Lessons Learned From Recent Safety Meta-Analyses

Lessons Learned From Recent Safety Meta-Analyses. Mark Levenson, Ph.D. Quantitative Safety and Pharmacoepidemiology Group Office of Biostatistics Center for Drug Evaluation and Research, FDA. v. 1 Oct. 2009. Disclaimer.

anoush
Download Presentation

Lessons Learned From Recent Safety Meta-Analyses

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Lessons Learned From Recent Safety Meta-Analyses Mark Levenson, Ph.D. Quantitative Safety and Pharmacoepidemiology GroupOffice of BiostatisticsCenter for Drug Evaluation and Research, FDA v. 1 Oct. 2009

  2. Disclaimer The views expressed in this presentation represent the opinions of the author, and do not necessarily represent the views of the United States Food and Drug Administration

  3. Focus Today • Safety • Regulatory setting • Pre- and post-market • Clinical trials • Access to relevant data • Not: locating, accessing quality of, and extracting data from studies

  4. Outline • Study inclusion criteria • Endpoints • Methodology • Examples • Suicidality meta-analyses • Aprotinin

  5. Meta-Analysis Steps • Define research goals • Research/understand relevant trials • Define analysis plan (Prespecify!) • Research questions • Study inclusion criteria • Analysis set and subgroups • Endpoints • Primary methods • Sensitivity methods • Make request to sponsor(s) / obtain data • Implement analysis plan • Report and interpret findings

  6. Study Inclusion Criteria

  7. Study Inclusion Criteria • Valid comparison groups • Similarity in • Design • Interventions • Study population • Studied indication • Data availability

  8. Survival 1 2 Years Duration: Example • Assume event of interest takes some time to develop (increasing hazard)

  9. Duration: Example (Cont.) • Scenario 1: • 10 trials • 100 patients per trial • 3 month duration • 1,000 patients • 250 person-years • Scenario 2: • 1 trial • 100 patients per trial • 2 year duration • 100 patients • 200 person-years

  10. Endpoints

  11. Endpoints • First choice: prospectively collect and adjudicate endpoints • Second choice: use common post-hoc adjudication procedure across trials • Last choice: make do with existing information from trials

  12. Endpoints: Follow-Up Time Time Randomization End of Treatment Event End of Follow-up

  13. Endpoints: Follow-Up Time • Follow-up should be long enough to capture event of interest • Use common cut-off point across trials when possible • Need to balance lasting effect of drug versus confounding with post-trial therapy and dilution of drug effect (see: NEJM Vioxx APPROVE discussion, 2006)

  14. Data Availability Patient-level data allows more thorough analysis • Time-to-event • Subgroup • Treatment duration effects • Internal validation

  15. Methodology

  16. Methodology • Need to use appropriate methods for problem • Need to justify method • Need to perform sensitivity analyses along several fronts

  17. Methodology Considerations • Number of trials • Number of subjects per trial • Rates of events • Zero-event trials • Heterogeneity of effect

  18. Methods • Inverse variance weighting • Mantel-Haenszel odds ratio or risk ratio • Exact method for odds ratio • Mantel-Haenszel risk difference • Bayesian methods • Encompass fixed- and random-effect models and hierarchical models

  19. Sensitivity Analysis • Consequences of low event rate • Consequences of zero-event trials • Consequences of heterogeneity of trials • Random effects models • Trials with large influence

  20. Sensitivity Strategy • Primary method: Exact method for OR • Sensitivity methods: • Mantel-Haenszel RD • GLMM, qualitatively compare results with primary method

  21. Suicidality Meta-Analyses

  22. Suicidality Meta-Analyses • Concern that drugs may be associated with suicidality • Requested all patient-level data from all placebo-controlled trials from sponsors • Patients retrospectively classified into suicidality outcomes by blinded experts

  23. * * Reanalysisof FDA/Hammad 2004 data

  24. Suicidal Behavior or Ideation Odds Ratio Estimates

  25. Antiepileptic AC Paraphrase • Does committee agree with agency that findings should apply to all 11 drugs? Yes: 18, No: 3, Abstain: 0 • Does committee agree with agency that findings should apply to all approved antiepileptics? • Yes: 15, No: 5, Abstain: 1

  26. Aprotinin

  27. The Aprotinin Story • Aprotinin: used to reduce blood loss and transfusion in patients undergoing coronary artery bypass graft surgery (CABG) with cardiopulmonary bypass • 2006 NEJM Mangano paper raised safety concerns • FDA held 2 Advisory Committee meetings on the safety of aprotinin motivated by 3 observational studies

  28. Disparate Findings • Mangano in-hospital death: no effect relative to no drug • Mangano 5-year death: 1.37 HR, p-value=0.008 relative to no drug • Sponsor Global CABG RCT Database: Death 2.9% aprotinin, 2.5% placebo (9/06 AC) • Meta-analyses Henry et al. Cochran Review 2007: RR=0.90 (0.67, 1.20) relative to control (no drug). No effects relative to other drugs.

  29. BART • Blood Conservation Using Antifibrinolytics in a Randomized Trial (BART) • Compared aprotinin to two active drugs • 30-day death secondary endpoint

  30. Jan. 2007 Second Interim Analysis • Aprotinin 5.0% vs. 3.9% and 4.3% for comparator drugs • DSMB: “did not consider the results of [Mangano Study] convincing.” • DSMB: [Systematic reviews] “less biased than the observational studies” • Four systematic reviews showed no death effect

  31. October 2007 • Aprotinin 6.5% vs. 4.2% and 4.3% for comparator drugs (p-value near nominal significance) • DSBM recommends terminating trial

  32. What Happened to the Meta-Analysis? • Ray Editorial (NEJM) • Trials not designed to collect follow-up mortality information • Limited data on head-to-head comparisons with other drugs • Trial and patient heterogeneity (surgical procedure, patient risk) may hide signal

  33. Conclusions • Prospectively plan trials for pooled analyses, e.g. endpoint definition and ascertainment • Prespecify analysis plan • Select trials with similar and appropriate designs • Consider methodology issues of sparse events and perform sensitivity analyses • Understand the limitations of meta-analysis • Draft FDA Guidance for safety meta-analyses December 2009

  34. References • Guidance for Industry Premarketing Risk Assessment (FDA, 2005) • Much ado about nothing: a comparison of the performance of meta-analytic methods with rare events (Statis. Med., Bradburn et al., 2007) • The Aprotinin Story – Is BART the final chapter? (NEJM, Ray, 2007) • Time-to-Event Analysis for Long-Term Treatments – The APPROVe trial (NEJM, Lagakos, 2006) • Understanding the New Drug Safety Standards: The Emerging Science of Meta-Analysis (The Pink Sheet, 2007)

More Related