M2 Medical Epidemiology

1. Clinical trials M2 Medical Epidemiology

2. Clinical Trials A clinical trial is • A cohort study • A prospective study • An interventional study • An experiment • A controlled study

3. The Structure of a Clinical Trial

4. Various Aspects Are Standardized and Protocol-based • Subject selection • Subject assignment • H & P data • Therapeutic intervention • Lab calibration • Outcome evaluation

5. Subject Selection • Adequate number of subjects • Adequate number of expected endpoints • Easy to follow-up • Willing to participate (give consent) • Eligibility (criteria)

6. Efficacy Versus Effectiveness • Internal Validity (validity) versus External Validity (generalizability)

8. Types of Control Groups • Historical • Contemporaneous • Concurrent • Randomized

9. Allocating Treatment • If Concurrent Controls are best, what is the best way of assigning patients to receive a new treatment or serve as controls. • Complete (Simple) randomization • Restricted randomization

10. Complete Randomization • Patients assigned by Identical chance process (but not necessarily in equal numbers) • Mechanics • Insures process fairness • Does not insure balance, especially in small studies.Therefore, may still need statistical adjustment

11. Randomization Contd. • Has nothing to do with sampling bias. • Randomization (random allocation) versus random sample. • Does NOT deal with “chance” as a possible explanation of the difference. To the contrary. • Can be used to create groups of unequal size. • Baseline characteristics (table 1).

13. Allocation Concealment • The allocation sequence is concealed from those enrolling participants until assignment is complete. • Prevents enrollers from (subconsciously or otherwise) influencing which participants are assigned to a particular treatment group.

14. Allocation Concealment To assess concealment, raters of trials look for: • Central (phone) randomization • Sequentially numbered, opaque, sealed envelopes • Sealed envelopes from a closed bag • Numbered or coded bottles or containers

15. Restricted Randomization • Stratification • Blocking (Permuted Block Design) • Minimization (Dynamic Balancing)

16. Stratified Randomization • When an important prognostic factor (risk factor for the outcome being studied) exists. • Subjects are stratified according to that factor prior to randomization. • To the benefits of complete randomization adds assurance of balance on factors used to form strata. • May still need adjustment on other factors.

17. Scheme of stratified randomization

18. Blocking • Ensures close balance of the numbers in each group at all times during trial. • Example: For every six patients 3 will be allocated. • Problem If block size is discovered. • Remedy: more blinding, varying block size, larger blocks. • Basic, Stratified,Randomized (random-sized)

19. Minimization (Dynamic Balancing) • Ensures balance of several factors . • No list in advance. First patient is truly randomly allocated. • For each subsequent patient the treatment allocation is identified which minimizes the imbalance. Then a choice is made at random with weighting in favor of it. • Has to be done away from enrollment point and enrollers blind to process.

20. Variations in Prognosis by Hospital

21. Problems With Contemporaneous Comparisons • Regional population differences. • Regional practice differences. • Diagnostic variations. • Referral pattern biases. • Variations in data collections.

22. 1 Year Mortality in Trials of Medical Vs. Surgical Treatment of Coronary Artery Disease

24. Why Do Controls in a Randomized Trial Do So Well ?! • Volunteerism • Eligibility • Placebo effect • Hawthorne effect • Regression towards the mean

25. Volunteerism People who agree to participate in clinical trials are an “elite” group of patients with extremely good prognosis

26. Eligibility Patients have to meet stringent eligibility criteria before randomization, or they would be excluded

27. Placebo Effect • Placebo can do just about anything (prolong life, cure cancer). • Placebo can also cause side effects. • Placebo effect is very useful in medicine but in epidemiology it causes problems, so we try to equalize it between the 2 groups.

28. Hawthorne Effect • Hawthorne works of the Western Electric Co. Chicago, IL • People who know they are being studied modify their behavior and do better than the average patient

29. Regression Towards the Mean • Weather game • Individuals with initially abnormal results tend on average to have more normal (closer to the mean) results later. • Lab tests, BP etc. • Recheck before randomization. Run-in period. • Sophomore slump, medical school, Airforce landing feedback.

30. Why Does Prognosis Improve Over Time ? 1. Initial reports come from referral centers 2. Publicity brings in more patients 3. Physicians’ awareness increases diagnosis

31. 4. Development of a Diagnostic Test • Allows diagnosis of atypical cases. • Is an incentive for physicians. It’s more challenging to diagnose difficult (atypical cases) • Physicians with zero diagnostic skill can now diagnose this disease. • Allows diagnosis of non-cases (false positives) • Allows population based studies

32. Prognosis Improves Over Time. Contd. 5. Publicity that a disease is very common relieves clinician from worrying that they may be overdiagnosing it. 6. Placebo effect increases over time. Why? 7. Safer treatment (laparoscopic cholecystectomy) lowers the threshold for diagnosing “symptomatic gall stones”

35. Stage Migration BiasWill Rogers Effect 8.Improved staging tests cause an apparent improvement of prognosis in every stage.

36. Stage Migration BiasWill Rogers Effect

37. Stage Migration BiasWill Rogers Effect

38. “BEFORE-AFTER” STUDYMortality by severity level

39. “BEFORE-AFTER” STUDYSeverity distribution

40. Will Rogers Effect • Will Rogers: “ When Okies moved from Oklahoma to California, they improved the IQ in both states”

41. Exclusion Criteria • Excluded patients are “ineligible” • So Why the separate category? More informative • Usually very large number. • Usually underestimates, real number even bigger. Why?

42. Exclusion Criteria Usually there is a table What to watch for • Patient preference • Clinician preference • no reason given

43. Studies within a Clinical Trial • Study of ASA in MI to prevent long term mortality • Can study other predictors of mortality. For example smokers versus non smokers (adjusting for ASA) • Can study predictors of other outcomes for which we have data. For example onset of CHF. Again study exposed versus unexposed (to LVH for example) adjusting for ASA (Why?)

44. Studies within a Clinical Trial • Can study if another drug is associated with the main outcome or secondary outcomes. • Beware bias (non randomized cohort). • If exposure info not available on whole population (e.g. serum samples) then: Nested Case-control. Case-cohort.

45. Loss to follow up Differential vs. Random • Compare their baseline variables with the rest of the subjects. • Chase a subgroup. • Worst case scenario

46. Objectives of Subgroup Analysis • Support the main finding • Check the consistency of main finding • Address specific concerns re efficacy or safety in specific subgroup • Generate hypotheses for future studies

48. Inappropriate Uses of Subgroup Analysis • Rescue a negative trial • Rescue a harmful trial • Data dredging: find interesting results without a prespecified plan or hypothesis

49. To Avoid Inappropriate Uses of Subgroup Analysis • Prespecify analysis plan • Prespecify hypotheses to be tested based on prior evidence • Plan adequate power in the subgroups • Avoid the previous pitfalls.

50. Problems with Subgroup Analysis • Low power • Multiplicity • Test for interaction • Comparability of the treatment groups maybe compromized • Over interpretation