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Clinical Trials Advanced

Clinical Trials Advanced. Lecture 3: What’s the Question? What’s the Response? What Population?. David L. DeMets, Ph.D. Dept. of Biostatistics & Medical Informatics 600 Highland Avenue, Room K6/446 demets@biostat.wisc.edu (608) 263-1706. Lecture Series Topics. Background

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Clinical Trials Advanced

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  1. Clinical Trials Advanced Lecture 3: What’s the Question? What’s the Response? What Population? David L. DeMets, Ph.D. Dept. of Biostatistics & Medical Informatics 600 Highland Avenue, Room K6/446 demets@biostat.wisc.edu (608) 263-1706

  2. Lecture Series Topics • Background • Why clinical Trials • Defining the Question • Basic Trial Designs • Sample Size Estimation • Trial monitoring for safety & benefit • Issues in data analysis • Trial reporting

  3. Primary vs. Secondary Question • Primary • most important, central question • ideally, only one • stated in advance • basis for design and sample size • Secondary • related to primary • stated in advance • limited in number

  4. Examples (1) • Physicians Health Study (PHS) • aspirin vs placebo • primary: total mortality • secondary: fatal + nonfatal myocardial infarction (MI)

  5. Examples (2) • Eastern Cooperative Oncology Group (ECOG - 1178) • tamoxifen vs placebo • primary: tumor recurrence/relapse, disease-free survival • secondary: total mortality

  6. Examples (3) • Chronic Study of Intermittent Positive Pressure Breathing (IPPB) • long-term intermittent positive pressure breathing vs. nebulizer • primary: forced expiratory volume (FEV1) • secondary: quality of life

  7. A-HEFT • Ref: NEJM, Nov 11,2004 • 1050 African Americans with Class III-IV CHF • Isosorbide Dinitrate + Hydrolyzine vs Plbo • Composite outcome (death, HF hospitalizations, change in QoL) • DMC terminated trial early • Unanticipated mortality benefit – not prespecified

  8. A-HEFT

  9. A-HEFT

  10. 2. Subgroup Questions • Questions about effect of therapy in a sub-population of subjects entered into the trial • Assess internal consistency of results • Confirm previous hypothesis • Generate new hypotheses

  11. Subgroup Analyses Examples: Breast Cancer: Does the benefit of treatment depend on: menopausal status, stage of disease, age, etc. AIDS: Does the benefit of treatment depend on: gender, age, initial CD4 counts, race, etc. Analyses of a trial by subgroup results in a separate statistical test for each subgroup. As a result the probability of false positive conclusions arising in the analysis of a trial will increase.

  12. False Positive Rates The greater the number of subgroups analyzed separately, the larger the probability of making false positive conclusions. No. of SubgroupsAt Least One False Positive 1.05 2.097 3.143 4.186 5.226

  13. Example - Subgroup Concern • Second International Study of Infarct Survival (ISIS 2) • 2 x 2 factorial design (aspirin vs. placebo and streptokinase vs. placebo) • vascular and total mortality in patients with an acute myocardial infarction (MI) • Gemini or Libra astrological birth signs did somewhat worse on aspirin while all other signs and overall results impressive and highly significant benefit from aspirin

  14. Subgroup Considerations • Rules for Subgroups 1.Stated in advance (in protocol) 2.Limited in number 3.Interpreted cautiously, qualitatively 4.Look for consistency of results • May be used to 1.Confirm or answer specific questions generated in aprevious trial (e.g. Metroprolol <65 vs. >65 age total mortality 2.Generate new hypothesis to be tested in some future trial 3.Consistency of primary outcomes

  15. MERIT-HF Study Design • Chronic heart failure patients • Randomized placebo controlled • Metoprolol vs. placebo • Two-week placebo run in (compliance) • Entered 3991 patients • Terminated early • Mean follow-up approximately one year The International Steering Committee on Behalf of the MERIT-HF Study Group, Am J Cardiol 1997; 80(9B):54J-58J. The MERIT-HF Study Group, ACC, March 1999.

  16. MERIT Total Mortality

  17. MERIT

  18. MERIT(AHJ, 2001)

  19. Interaction Tests Not Unique • Model Choice • Cox • Logistic • Test Statistic • Wald (Regression co-efficient) • Score (likelihood) • Definition of Subgroups • US vs. World • All Countries Separately

  20. Subgroup x TreatmentInteraction • Qualitative Interaction Treatment effect is different in direction in two subgroups • Quantitative Interaction Treatment effect is of same direction but of different magnitude • Statistical tests for interaction not very powerful • Even if statistically significant, must be cautious in interpretation (PRAISE)

  21. Praise IRef: NEJM, 1996 • Amlodipine vs. placebo • NYHA class II-III • Randomized double-blind • Mortality/hospitalization outcomes • Stratified by etiology (ischemic/non-ischemic) • 1153 patients

  22. PRAISE I

  23. PRAISE I - Interaction • Overall P = 0.07 • Etiology by Trt Interaction P = 0.004 • Ischemic P = NS • Non-Ischemic P < 0.001

  24. PRAISE I - Ischemic

  25. PRAISE I – Non- Ischemic

  26. PRAISE II • Repeated non-ischemic strata • Amlodipine vs. placebo • Randomized double-blind • 1653 patients • Mortality outcome • RR  1.0

  27. Event Probabilities Over Time P1 P2

  28. Three Views: • Ignore subgroups and analyze only by treatment groups. • Plan for subgroup analyses in advance. Do not “mine” data. • Do subgroup analyses --- However view all results with caution.

  29. 3. Adverse Effects • Any intervention should do more benefit than harm • Not always easy to specify in advance - many variables will be measured (clinical, laboratory) • Usually not willing or interested in demonstrating an intervention to be harmful • May be known adverse effects from earlier trials

  30. Serious Adverse Events (SAEs) • Death • Irreversible event • Requires hospitalization

  31. Serious Adverse Events (SAEs) • Must be reported to regulatory agencies and IRBs

  32. Adverse Events • Challenges • Short term vs longer term • Longer term follow-up in face of early benefit • Rare AEs may be seen only with very large numbers of exposed patients and long term follow-up • Recent Examples • Immediate pain reduction vs longer term increase in cardiovascular risk • Viox & Celebrex

  33. Coronary Drug Project Cumulative morality rate % Month of Follow-up Life-table cumulative mortality rates, Coronary Drug Research Project Group

  34. Coronary Drug ProjectResearch Group z values for clofibrate-placebo differences in proportion of deaths by calendar month since beginning of study (Month 0 = March 1966, Month 100 = July 1974)

  35. Small rate of SAEs • Most SAEs are not so frequent • Hard to interpret small numbers of events • CDP mortality comparisons not stable with small numbers • Might easily have claimed harm, incorrrectly • Need to accumulate larger numbers to be sure

  36. Outcome AssessmentWhat is the response variable?

  37. What’s the Response Variable? • Used to answer primary/secondary questions • Characteristics for primary/secondary outcomes 1.Well defined & stable 2.Ascertained in all subjects 3.Unbiased 4.Reproducible 5.Specificity to question

  38. Response Variable • Examples 1.MILIS Infarct size measurement? - Enzymes (area under curve or peaks) - Radionuclide imaging - EKG Issues of definition, ascertainment, reproducible 2.NOTT Quality of Life? - POMS (Profile of Mood) - SIP (Sickness Impact Profile) - Pulmonary Function - Survival

  39. Response Variable 3.Cardiovascular Disease Trials - Total mortality - CHD mortality - Non-fatal MI - PVC’s 4.Diabetes - Mortality - Blindness - Visual impairment - Retinopathy - Microaneurisms

  40. Surrogate Response Variables • Used as alternative to desired or ideal clinical response • Examples • Suppression of arrhythmia (sudden death) • T4 cell counts (AIDS or ARC) • Used often - therapeutic exploratory (Phase I, Phase II) • Use with caution - therapeutic confirmatory (Phase III)

  41. Surrogate Response Variables (2) • Frequent Criticism of Clinical Trials • Too long • Too large • Too expensive • Advantages • Perhaps smaller sample size • Detect earlier effect shorter trial • Easier

  42. Examples of FDA Approval of Drugs Using Surrogates (1) • Lower cholesterol without evidence of survival benefit • Lower blood pressure without evidence of benefit for stroke, MI, congestive heart failure, or survival • Increase bone density without evidence of decreased fractures in osteoporosis

  43. Examples of FDA Approval of Drugs Using Surrogates (2) • Increase cardiac function in congestive heart failure without evidence of survival benefit • Decrease rate of arrhythmias (VPBs) without evidence of survival benefit • Lower blood glucose and glycosylated hemoglobin without evidence about diabetic complications or survival benefit

  44. Surrogate Response Variables (3) • Requirements (Prentice, 1989) T = True clinical endpoint S = Surrogate Z = Treatment • H0: P(T|Z) = P(T) P(S|Z) = P(S) • Sufficient Conditions 1.S is informative about T (predictive) P(T|S) P(T) 2.S fully captures effect of Z on T P(T|S,Z) = P(T|S)

  45. Concerns About Surrogates 1.Relationship between surrogate and true endpoint may not be causal, but coincidental to a third factor 2.Other unfavorable effects of the drug 3.Effect on surrogate may correlate with one clinical endpoint, but not others

  46. Time Intervening on the Surrogate Intervention True Clinical Outcome Surrogate Disease End Point The setting that provides the greatest potential for the surrogate endpoint to be valid. Reprinted from Ann Intern Med 1996; 125:605-13. Figure 2. Reasons for failure of surrogate end points. A. The surrogate is not in the causal pathway of the disease process. B. Of several causal pathways of disease, the intervention affects only the pathway mediated through the surrogate. C. The surrogate is not in the pathway of the intervention’s effect or is insensitive to its effect. D. The intervention has mechanisms for action independent of the disease process. Dotted lines = mechanisms of action that might exist.

  47. Time Other Possibilities Reasons for failure of surrogate end points. A. The surrogate is not in the causal pathway of the disease process. B. Of several causal pathways of disease, the intervention affects only the pathway mediated through the surrogate. C. The surrogate is not in the pathway of the intervention’s effect or is insensitive to its effect. D. The intervention has mechanisms for action independent of the disease process. Dotted lines = mechanisms of action that might exist. Surrogate True Clinical Outcome A End Point Disease Intervention B True Clinical Outcome Surrogate Disease End Point Intervention C True Clinical Outcome Disease Surrogate End Point Intervention D True Clinical Outcome Surrogate End Point Disease

  48. Examples using “Surrogates” • Chronic Obstructive Pulmonary Disease • Cardiac Arrhythmias • Heart Failure • Osteoporosis

  49. Nocturnal Oxygen Therapy Trial (NOTT) • Hypothesis • Is continuous oxygen therapy better than nocturnal oxygen therapy in chronic obstructive lung disease patients? • Surrogates • Survival • Design • 203 patients • Two-sided 0.05 Type I error • Randomized • Multicenter • Sequential data monitoring

  50. Possible NOTT Surrogates • PaO2 • Hematocrit • FEV1 % Predicted • FVC % Predicted • Maximum Workload • Heart Rate • Mean Pulmonary Artery Pressure • Cardiac Index • Pulmonary Vascular Resistance • Neuropsychiatric Impairment • Quality of Life

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