Monitoring response variables 13Feb2014

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Monitoring response variables 13Feb2014

  1. 1. Monitoring Response Variables
  2. 2. What’s The Question? What is the study hypothesis?
  3. 3. What’s The Question? • • • • • • What’s the outcome? What’s the intervention? When and for how long? For whom? How many participants are needed? How can we optimize potential benefit (and what we learn) while minimizing potential harm?
  4. 4. Answering the Question • Response variable selection and measurement • Defining the intervention • Study design • Eligibility criteria • Sample size estimate • Patient management procedures • Monitoring for safety and benefit • Data analysis approaches
  5. 5. Response Variable Selection • “Dose ranging” • Biologic activity • Biomarker – Understand mechanism – Surrogate outcome • Toxicity • Condition/vector/ge ne interaction • Feasibility for larger study • Clinical outcome
  6. 6. Response Variable Criteria • • • • • • Well defined Stable Reproducible Unbiased Ascertainable in all participants Adequately address study hypothesis
  7. 7. Outcome Assessment
  8. 8. 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
  9. 9. Response Variable (1) • 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
  10. 10. Response Variable(2) 3. Cardiovascular Disease Trials - Total mortality - CHD mortality - Non-fatal MI - PVC’s 4. Diabetes - Mortality - Blindness - Visual impairment - Retinopathy - Microaneurysms
  11. 11. 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)
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. 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. 2. S is informative about T (predictive) P(T|S) ≠ P(T) S fully captures effect of Z on T P(T|S,Z) = P(T|S)
  16. 16. 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
  17. 17. Time Intervention Disease Surrogate End Point True Clinical Outcome The setting that provides the greatest potential for the surrogate endpoint to be valid. Reprinted from Ann Intern Med 1996; 125:605-13.
  18. 18. Time 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. A Disease Surrogate End Point True Clinical Outcome Intervention B Disease Surrogate End Point True Clinical Outcome Intervention C Disease Surrogate End Point True Clinical Outcome Intervention D Disease Surrogate End Point True Clinical Outcome
  19. 19. Examples using “Surrogates” • • • • • Chronic Obstructive Pulmonary Disease Cardiac Arrhythmias Heart Failure AIDS Osteoporosis
  20. 20. Nocturnal Oxygen Therapy Trial (NOTT) • Hypothesis – Is continuous oxygen therapy better than nocturnal oxygen therapy in chronic obstructive lung disease patients? • Possible Surrogates • Quality of Life • Survival • Design – – – – – 203 patients Two-sided 0.05 Type I error Randomized Multicenter Sequential data monitoring
  21. 21. Possible NOTT Surrogates PaO2 • Mean Pulmonary Artery Pressure Hematocrit FEV1 % Predicted • Cardiac Index FVC % Predicted • Pulmonary Vascular Maximum Resistance Workload • Heart Rate • • • • •
  22. 22. Concluding Remarks on Surrogates • Surrogates play an important role in the development of Phase I, II, and pilot Phase III studies • Treatments may affect more than one mechanism • “Surrogates” do not reliably predict treatment on clinical outcome • Continued success in a given field is not even guaranteed • Reliance on “surrogates” should be minimized
  23. 23. Composite Outcomes • Defined as having occurred if any one of several components is observed – e.g. death, MI, stroke, change in severity,….. • • • • Should be clinically relevant Each component ascertainable without bias Must be sensitive to intervention Made up of fatal & nonfatal events
  24. 24. Composite Endpoint Rationale • May reduce Sample Size by increasing event rates – Assumes each component sensitive to intervention – Otherwise, power can be lost • Avoids competing risk problem – Death is a competing risk to all other morbid events, probably not independent
  25. 25. Problems with Composite Outcomes • Interpretability if individual components go in different directions – e.g. WHI global index– • Death: similar • Fractures: positive • DVTs, PEs: negative • Relevance of a mixed set of components – Adding softer outcomes • Could have a loss of power • Failure to ascertain components
  26. 26. Data and Safety Monitoring Boards Why? • Participant Safety • Policy Review
  27. 27. Data and Safety Monitoring Boards New Treatment for Lung Cancer • Totally New Compound • Possible Liver Toxicity in Animals
  28. 28. Data and Safety Monitoring Boards New Treatment for Lung Cancer Ten Patients Enrolled – 6 Months FU • Placebo Group Two Dead from Lung Cancer • Treated Group None Dead from Lung Cancer Two Dead from Liver Failure
  29. 29. Data and Safety Monitoring Boards Who? • Independent of Sponsor/Investigators • Appointed by Sponsor • Members Without Conflict of Interest • Different Areas of Expertise
  30. 30. Data and Safety Monitoring Boards What? – Study Monitoring • Review Protocols and Procedures • Review Study Design • Monitor Ongoing Quality • Monitor Patient Accrual and Drop-out • Monitor Clinic and Patient Compliance
  31. 31. Data and Safety Monitoring Boards What? – Data Monitoring • Identify Major Response Variables • Identify Possible Adverse Outcomes • Develop Stopping Guidelines
  32. 32. Sequential Study Monitoring O’Brian - Flemming 5 4 3 2 1 Test 0 Statistic -1 -2 -3 -4 -5 Reject Null Hypothesis Continue Study Accept Null Hypothesis Continue Study Reject Null Hypothesis 0 20 40 60 Number of Events 80
  33. 33. Data and Safety Monitoring Boards Study Outcomes • Planned Termination • Harm – Early Termination • Early Benefit – Early Termination • Futility – Early Termination • Study Extension with Changes

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