1. Department of O UTCOMES R ESEARCH

2. Daniel I. Sessler, M.D. Professor and Chair Department of O UTCOMES R ESEARCH The Cleveland Clinic Clinical Research Design Types of Clinical Research Sources of Error Study Design Clinical Trials

3. Case-Control Studies Identify cases & matched controls Look back in time and compare on exposure Time Case Group Control Group ExposureExposure

4. Cohort Studies Identify exposed & matched unexposed patients Look forward in time and compare on disease Time Exposed Unexposed DiseaseDisease

5. Timing of Cohort Studies Time Initial exposuresDisease onset or diagnosis PROSPECTIVE COHORT STUDY AMBIDIRECTIONAL COHORT STUDY RETROSPECTIVE COHORT STUDY

6. Sources of Error There is no perfect study All are limited by practical and ethical considerations It is impossible to control all potential confounders Multiple studies required to prove a hypothesis Good design limits risk of false results Statistics at best partially compensate for systematic error Major types of error Selection bias Measurement bias Confounding Reverse causation Chance

7. Statistical Association

8. Selection Bias Non-random selection for inclusion / treatment Or selective loss Subtle forms of disease may be missed When treatment is non-random: Newer treatments assigned to patients most likely to benefit “Better” patients seek out latest treatments “Nice” patients may be given the preferred treatment Compliance may vary as a function of treatment Patients drop out for lack of efficacy or because of side effects Largely prevented by randomization

9. Measurement Bias Quality of measurement varies non-randomly Quality of records generally poor Not necessarily randomly so Patients given new treatments watched more closely Subjects with disease may better remember exposures When treatment is unblinded Benefit may be over-estimated Complications may be under-estimated Largely prevented by blinding

10. Example of Measurement Bias Reported parental historyArthritis (%)No arthritis (%) Neither parent2750 One parent5842 Both parents158 From Schull & Cobb, J Chronic Dis, 1969 P = 0.003

11. Measurement & Selection Bias

12. Confounding Association between two factors caused by third factor For example, mortality greater in Florida than Alaska But average age is much higher in Florida Increased mortality results from age, rather than geography of FL For example, transfusions associated with mortality But patients having larger, longer operations require more blood Increased mortality may be a consequence of larger operations Largely prevented by randomization

13. External Threats to Validity Population of interest Eligible Subjects Subjects enrolled Selection bias Measurement bias Confounding Chance Conclusion Internal validityExternal validity ? ??

14. Study Design Life is short; do things that matter! Is the question important? Is it worth years of your life? Concise hypothesis testing of important outcomes Usually only one or two hypotheses per study Beware of studies without a specified hypothesis A priori design Planned comparisons with identified primary outcome Intention-to-treat design Randomization Best prevention for selection bias and confounding Concealed allocation –No a priori knowledge of randomization

15. Subject Selection Appropriate for question Maximize incidence of desired outcome Technically, results apply only to population studied –Broaden enrollment to improve generalizability As restrictive as practical; trade-off between: Variability Ease of recruitment Sample-size estimates Usually from preliminary data Defined stopping points

16. Blinding / Masking Only reliable way to prevent measurement bias Essential for subjective responses –Use for objective responses whenever possible Careful design required to maintain blinding Use double blinding whenever possible Avoid triple blinding! Maintain blinding throughout data analysis Even data-entry errors can be non-random Statisticians are not immune to bias! Placebo effect can be enormous

17. Importance of Blinding Mackey, Personal communication Chronic Pain Analgesic

18. More About Placebo Effect Kaptchuk, PLoS ONE, 2010

19. Conclusion: Good Clinical Studies… Test a specific a priori hypothesis Evaluate clinically important outcomes Use appropriate statistical analysis Make conclusions that follow from the data And acknowledged substantive limitations Randomize treatment & conceal allocation if possible Best protection against selection bias and confounding Blind patients and investigators if possible Best protection against measurement bias

20. Department of O UTCOMES R ESEARCH