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Chapter 10 Data Monitoring, Monitoring Committee Function & Statistical Methods
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Chapter 10 Data Monitoring, Monitoring Committee Function & Statistical Methods

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  1. Chapter 10Data Monitoring, Monitoring Committee Function & Statistical Methods

  2. Some References • Texts/Chapters 1. Friedman, Furberg & DeMets (1998) 3rd edition, Fundamentals of Clinical Trials, Springer-Verlag, NY, NY 2. Pocock (1983) Clinical Trials, Wiley. 3. Ellenberg S, Fleming T and DeMets D: Data Monitoring Committees in Clinical Trials: A Practical Perspective. John Wiley & Sons, Ltd., West Sussex, England, 2002. 4. Jennison C and Turnbull B (2000) Group Sequential Methods with Application to Cinical Trials. Chapman & Hall, NY. 5. DeMets DL (1998) Data and Safety Monitoring Boards. In: Encyclopedia of Biostatistics. John Wiley and Sons, West Sussex, England, Vol. 2, pp. 1067-71. 6. DeMets and Lan. The alpha spending function approach to interim data analysis. In, Recent Advances in Clinical Trials Design and Analysis. Kluwer Academic Publishers, Boston, MA, 1995.

  3. Some References • Review Papers 1. Greenberg Report:Organization, review, and administration of cooperative studies. Controlled Clinical Trials 9:137-148, 1988. 2. DeMets and Lan: (1994) Interim analyses: The alpha spending function approach. Statistics in Medicine, 13(13/14):1341-52, 1994. 3. Lan and Wittes. The B-value: A tool for monitoring data. Biometrics 44:579-585, 1988. 4. Task Force of the Working Group on Arrhythmias of the European Society of Cardiology: The early termination of clinical trials: causes, consequences, and control. Circulation 89(6):2892-2907, 1994. 5. Fleming and DeMets: Monitoring of clinical trials: issues and recommendations. Controlled Clin Trials 14:183-97, 1993. 6. DeMets, Ellenberg, Fleming, Childress, et al: The Data and Safety Monitoring Board and AIDS clinical trials. Controlled Clin Trials 16:408-21, 1995. 7. Armstrong and Furberg: Clinical trial data and safety monitoring boards: The search for a constitution. Circulation 1, Sess:6, 1994.

  4. Data MonitoringRationale 1. Ethical 2. Scientific 3. Economic

  5. A 40-year history Greenberg Report (1967) Coronary Drug Project (1968) NIH Experience and Guidelines Industry and ICH Guidelines Department of Health & Human Services Policy (Shalala, 2000) A Brief History

  6. Greenberg Report Recommendations • Develop a mechanism to terminate early if • Question already answered • Trial can’t achieve its goals • Unusual circumstances • Hypothesis no longer relevant • Sponsor decision to terminate should be based on advice of external committee

  7. Coronary Drug Project (CDP) • References • Design (Circulation, 1973) • Monitoring Experience (CCT, 1981) • Major Outcome (JAMA 1970, 1972, 1973, 1975) • Tested several lipid lowering drugs in post MI patients • Multicenter study • Mortality as primary outcome • Began recruitment in 1965

  8. Coronary Drug Project • First trial to benefit from Greenberg Report • Policy Advisory Board • Senior Investigators, External Experts, NIH • Initially reviewed interim data • Data Coordinating and Statistical Center • Safety Monitoring Committee formed (1968), after trial was underway

  9. Early NHLBI CT Model Funding Agency Policy Advisory Board Data and Safety Monitoring Board Steering Committee • Multiple • Clinics Central Lab(s) Working Committees Data Coordinating Center Data Management Statistical Analysis

  10. NHLBI CT Model Funding Agency Data Monitoring Committee Steering Committee Coordinating Data Center • Clinics Central Lab(s) Working Committees

  11. NIH DMC Activity • Ref: Statistics in Medicine (1993) • CDP (Coronary Drug Project) became model for National Heart, Lung, and Blood Institute (NHLBI) • heart, lung, blood disease trials • National Eye Institute (NEI) (1972) • Diabetic Retinopathy Study • National Institute Diabetes, Digestive and Kidney (NIDDK) • Diabetes Complication and Control Trial (1980) • National Cancer Institute (NCI) • Prevention Trials, Cooperative Group Therapeutic Trials • National Institute Allergy and Infectious Disease (NIAID) • AIDS Clinical Trial Group (ACTG) (1986)

  12. Industry/FDA/ICH • Industry sponsorship of RCTs expanded dramatically since 1990 in several disease areas (e.g. cardiology, cancer, AIDS) • Industry use of DMCs growing as well • FDA 1989 guidelines very brief mention of data monitoring and DMCs • International Conference on Harmonization (ICH) • ICH/E9 • Section 4.5 Interim Analyses • Section 4.6 Independent DMCs • ICH/E6

  13. Independent DMCsWhen are they Needed? • Department of Health and Human Services Policy • Shalala (NEJM, 2000): All NIH FDA trials must have a monitoring plan, for some a DMC may be required • NIH policy (1998) • all sponsored trials must have a monitoring system • safety, efficacy and validity • DMC for Phase III trials • FDA guidelines (Nov 2001)

  14. Need for Independent DMCs • Phase I Trials (dose) • Monitoring usually at local level • Phase II Trials (activity) • Most monitoring at local level • Some randomized, blinded, multicenter Phase II trials may need IDMC • Phase III & IV (effectiveness, risk, benefit) • Most frequent user of IDMC • Structure of monitoring depends on risk (e.g. Phase I-IV)

  15. Data Monitoring Committee • FDA suggests a need for an Independent DSMB for • Pivotal Phase IIIs • Mortality or irreversible morbidity outcome

  16. Industry-Modified NIH Model Steering Committee Pharmaceutical Industry Sponsor Regulatory Agencies Independent Data Monitoring Committee (IDMC) Statistical Analysis Center Data Management Center (Sponsor or CRO) Central Units (Labs, …) Clinical Centers Institutional Review Board Patients

  17. DMC Relationshipsand Responsibilities • Patients • Study Investigators • Sponsor • Local IRBs • Regulatory Agencies

  18. Early Administrative AnalysisDMC and Executive Committee 1. Recruitment/Entry Criteria 2. Baseline Comparisons 3. Design Assumptions a. Control only b. Combined groups

  19. Design Modifications 1. Entry Criteria 2. Treatment Dose 3. Sample Size Adjustment 4. Frequency of Measurements

  20. DMC Data ReviewInterim Analysis 1. Recruitment 2. Baseline Variables -Eligibility -Comparability 3. Outcome Measures -Primary -Secondary 4. Toxicity/Adverse Effects 5. Compliance 6. Specified Subgroups

  21. DMC Recommendations 1. Continue Trial / Protocol Unmodified 2. Modify Protocol 3. Terminate Trial

  22. Reasons for Early Termination 1. Serious toxicity 2. Established benefit 3. Futility or no trend of interest 4. Design, logistical issues too serious to fix

  23. DMC Decision Making Process Complex (1) • Recruitment Goals • Baseline risk and comparability • Compliance • Primary and secondary outcomes • Safety

  24. DMC Decision Making Process Complex (2) • Internal consistency • External consistency • Benefit/Risk • Current vs future patients • Clinical/Public impact • Statistical issues

  25. DMC Decision Making Role • DMC makes recommendations, not final decisions • Independent review provides basis for DMC recommendations • DMC makes recommendations to • Executive Committee who recommends to sponsor, or • Sponsor • DMC may, if requested, debrief Executive Committee and/or sponsor • Rarely are DMC recommendations rejected

  26. DMC Meeting Format • Open Session • Progress, blinded data • Sponsor, Executive Committee, DMC, SAC • Closed Session • Unblinded data • DMC, SAC • Sponsor Rep? (Not recommended) • Executive Session • DMC only • Debriefing Session • DMC Chair, Sponsor Rep, Executive Committee Rep

  27. DMC Relationships • Regulatory Agencies (e.g. FDA) • Could perhaps brief DMC about specific concerns at Open Session • Should not participate in DMC Closed Sessions • Should be briefed about DMC recommendations/decisions ASAP following Executive Committee

  28. DMC Membership • Monitoring is complex decision process and requires a variety of expertise • Needed expertise • Clinical • Basic science • Clinical trial methodology • Biostatistics • Epidemiology • Medical ethics • Helpful expertise • Regulatory • Some experience essential

  29. DMC Confidentiality • In general, interim data must remain confidential • DMC may rarely release specific/limited interim data (e.g. safety issue) • Members must not share interim data with anyone outside DMC • Leaks can affect • Patient Recruitment • Protocol Compliance • Outcome Assessment • Trial Support

  30. DMC Liability • Recent events (eg Cox-IIs, Vioxx) have raised the potential for litigation (訴訟)(Vioxx or COX-IIs (painkillers) can raises the risk of heart attack, stroke and death and were withdrawn from the market) • Members have been gotten a subpoena (傳票) • DMC Charters (設立) for industry trials now often cover indemnification clauses (賠償條款) • No indemnification yet for NIH trials

  31. DMC Needs “On-Line”Data Management and Analysis • DMC reluctant to make decisions on “old data” • Minimize data delay and event verification • Be prepared from start • Focus on key variables, not complete case reports (delays can be problematic)

  32. Levels of Independence • Totally Inhouse Coordinating Center • Internal DM, Internal SAC, External DMC • Internal DM, External SAC, External DMC • External DM(e.g. CRO), External SAC, External DMC

  33. DMC Summary • NIH Clinical Trial Model - long history of success • Adaptation for industry can be made • SC, DMC, SAC or DM are critical components • Independence of DMC essential • Best way to achieve this goal is for external SAC and external DMC

  34. Data Monitoring Process 1. DMC and the decision process 2. A brief introduction to statistical monitoring methods a. Group Sequential b. Stochastic Curtailment 3. Examples Ref: BHAT, DeMets et al. Controlled Clin Trials,1984

  35. Decision Factors 1. Comparability 2. Bias 3. Compliance 4. Main effect vs. Potential side effects 5. Internal Consistency a. Outcome measures b. Subgroups c. Centers 6. External Consistency 7. Impact 8. Statistical Issues/Repeated Testing

  36. Beta-blocker Heart Attack Trial (BHAT) Preliminary Report. JAMA 246:2073-2074, 1981 Final Report. JAMA 247:1707-1714, 1982 Design Features Mortality Outcome 3,837 patients Randomized Men and women Double-blind 30-69 years of age Placebo-controlled 5-21 days post-M.I. Extended follow-up Propranolol-180 or 240 mg/day

  37. BHATAccumulating Survival Data Date Data Monitoring Committee Meeting Propranolol Placebo Z(log rank) May 1979 22/860 34/848 1.68 Oct 1979 29/1080 48/1080 2.24 March 1980 50/1490 76/1486 2.37 Oct 1980 74/1846 103/1841 2.30 April 1981 106/1916 141/1921 2.34 Oct 1981 135/1916 183/1921 2.82* June 1982 * Data Monitoring Committee recommended termination

  38. Beta-Blocker Heart Attack Trial October 1, 1981LIFE-TABLE CUMULATIVE MORALITY CURVES

  39. Beta-Blocker Heart Attack TrialBaseline Comparisons Propranolol Placebo (N=1,916) (N=1,921) Average Age (yrs.) 55.2 55.4 Male (%) 83.8 85.2 White (%) 89.3 88.4 Systolic B.P. 112.3 111.7 Diastolic B.P. 72.6 72.3 Heart rate 76.2 75.7 Cholesterol 212.7 213.6 Current smoker (%) 57.3 56.8

  40. Beta-Blocker Heart Attack TrialTotal Mortality(Average 24-Month Follow-Up) Propranolol Placebo Age 30-59 5.9% 7.1% 60-69 9.6% 14.4% Sex Male 7.0% 9.3% Female 7.1% 10.9% Race White 6.7% 9.0% Black 11.0% 15.2%

  41. Beta-Blocker Heart Attack TrialTotal Mortality(Average 24-Month Follow-Up) Propranolol Placebo Risk Group I 13.5% 16.9% Risk Group II 7.8% 11.4% Risk Group III 5.2% 7.1%

  42. DMC Interim Analysis • Ethical, scientific and financial reasons • Repeated analysis of accumulating data causes a statistical problem

  43. Data Monitoring

  44. Classical Sequential Analysis • Observations are taken sequentially • After each observation • Decide whether to stop sampling (one group is significantly better, or worse, than the other) • Or take another observation • Originally developed by Wald (1947) • Applied to the clinical trial by Armitage (1975)

  45. Why Sequential Analysis? (Armitage, 1975) • Data reduction • Estimation with desired precision • Medical ethics

  46. Repeated Significance Tests • Assume X 1 , X 2 ,  ~ N(, 1) • Let S n = X 1 +  +X n • N is the maximum sample size • Testing H 0 :  = 0 vs H A : 0 • Nominal significance level is 0.05

  47. Repeated Significance Tests • For each nN , we assess if | Sn |  1.96 n • Stop sampling and reject H0 at the first nN , if any, such that | Sn |  1.96 n • Otherwise, stop sampling at N and do not reject H0

  48. Probability of Type I Error • *N = P {| Sn |  1.96 n for some nN |H0 } • By the law of the iterated logarithm, eventually reject H0 when in fact it is true • *N might be large for some N

  49. The Type I Error Probability when the Maximum Number of Observations is N

  50. The Required Critical Values and Nominal Level Giving a Type I Error Probability 0.05 for Various Values of N