Designing Clinical Research

1. Designing Clinical Research Anita S. Kablinger, M.D. Associate Professor Departments of Psychiatry and Psychopharmacology

2. Chapter 2: Conceiving the Research Question Research question: Addresses uncertainty Resolved by measurements

3. Successful Research Questions Require:scholarship, experience, mentor • Mastering the Literature -Published literature -Attend meetings -Relationships with professionals • Knowledge of New Ideas and Techniques • Imagination

4. FINER Research Questions • Feasible Number of subjects, technical expertise, affordable in time and money, manageable • Interesting to the investigator • Novel Confirms/ refutes/ extends previous findings, provides new findings • Ethical No unacceptable physical risks or invasion of privacy • Relevant Scientific knowledge, clinical and health policy, future research

5. Early Stage Outline • Researcher clarifies plan • Researcher discovers specific problems -Is research question finer ? • Allows colleagues’ reactions

6. Chapter 3: Choosing the Study Subjects • Study sample must be affordable in time and money • Study sample must represent the target population

7. Inclusion Criteria • Includes main characteristics of target population • What factors are important to the research question? • How should these factors be defined? • Include demographic, clinical, geographic, and temporal characteristics

8. Exclusion Criteria • Characteristics that may interfere with the data or randomization of the study • Excessive exclusion may degeneralize the study

9. Clinical vs. Community Populations • Clinical are often already hospitalized so study subjects are inexpensive, available, and easy to recruit BUT • Their condition may be severe or extraordinary, so the data may be distorted and not representative of the population

10. Convenience Samples • Subjects meet entry criteria • Subjects are accessible • Samples often selected consecutively • Minimizes voluntarism • Volunteers are often healthier than the general population, so data may be distorted

11. Probability Samples • Guarantee that every member in the population has a specific chance of selection for the study • Types: -Simple random -Stratified random -Cluster -Systematic

12. 2 Goals in Recruiting Study Subjects • Representative Response rate affects validity of study Non-response subjects are often different than those that respond • Disease may be the cause of non-response • Size Monitor recruitment progress Note when/ why potential subjects are lost

13. Chapter 7Designing an Observational Study: Cohort Studies • Cohort studies take place over time • Descriptive vs. Analytic • Prospective vs. Retrospective

14. Prospective Cohort Studies • Strengths • Measures predictor before outcome occurs (Less bias, more accurate) • Weaknesses • Expensive and inefficient for studying rare outcomes • Conditions present with symptoms before diagnosis

15. Retrospective Cohort Studies • Strengths • Measures predictor before outcome occurs (Less bias, more accurate) • Less costly and time-consuming than perspective studies • Weaknesses • Investigator has limited control over sampling design or data collection

16. Nested Case-Control Study • Used for predictor variables that are expensive to measure and can be assessed at end of study • Subjects from completed cohort study -Cases: Part of cohort that developed outcome -Controls: Part of cohort without outcome *Matching is optional but an unmatched design is preferable

17. Nested Case-Cohort Studies • Random sample selected from original cohort regardless of outcomes • Advantages: -Same control group for different studies -Information on risk factor prevalence

18. Nested Studies • Strengths • Costly measurements are available • Variables are collected before outcome • Reduces bias from fatal cases and use of various populations • Weaknesses • Observed association from confounded variable • Silent preclinical disease

19. Multiple Cohort Studies • 2 different subject samples based on level of predictor variable • May compare cohort study outcomes with census data • Strengths • Feasible approach for studying rare exposure or potential hazards • Weaknesses • Cohorts may differ • Data may be imprecise, incomplete, nonexistent

20. **Following the Entire Cohort • Exclude those planning to move/ difficult to reach • Obtain information for difficult follow up -Physician, close friends, SSN, Medicare # • Periodic contact and repeated follow-up efforts

21. Chapter 8: Designing an Observational Study: Cross-sectional and Case-control Studies • Cross-sectional • Case-control • Bias

22. Cross Sectional Study • Simultaneous measurements • No follow up • Cause and effect inferred • Predictor and outcome designated

23. Case Control Study • One sample from cases (With outcome) • One sample from controls (No outcome) • Compares levels of predictor in cases vs. controls

24. Prevalence vs. Incidence • Cross sectional studies provide prevalence (at given time) • Relative prevalence: Outcome prevalence by level of predictor • Cohort studies provide incidence • Over period of time

25. Cross Sectional Studies • Strengths -Fast and inexpensive -No loss from follow-up • Weaknesses -Difficult to determine cause and effect

26. Serial Survey • Series of cross sectional studies • Inferences can be drawn but a single group is not followed over time • May be used in cohort study to prevent learning effect on data

27. Case-Control Studies • Used to compare risk factor prevalence • How often do predictor variables lead to disease? • Cases (with disease) vs. controls (without) • Specified number of each, so incidence or prevalence of disease not determined • Retrospective so may contain bias

28. Case-Control Studies • Rare diseases • Diseases with long latency • Retrospective so can look at various predictor variables to determine cause of new outbreak

29. Case-Control Study Bias • Control and case separate sampling -Sampling bias • Retrospective predictor variable measurements -Differential measurement bias

30. Sampling Bias Not all affected are available for study -Undiagnosed, misdiagnosed, dead SOLUTIONS: • Hospital- or clinic-based controls • Matching • Population-based sample • Two+ control groups

31. Differential Measurement Bias Imperfect recall SOLUTIONS: • Data recorded before outcome • Blinding

32. Chapter 10 Designing an Experiment: Clinical Trials I • Treatment applied • Effect on outcome observed • Causality demonstrated • Mature research questions

33. Selecting Participants • Entry criteria: Rate of outcome Treatment effectiveness Recruitable Follow up Generalizability Compliance • Sample size and recruitment planning

34. Measuring Baseline Variables • Fewer variables may be more efficient • Spend time and money wisely • Tracking information • Participant description • Measure risk factor and subgroup defining variables • Material banks • Initial outcome variable

35. Randomizing random assignment to interventions • Treatment assignment must be random • Blocked • Stratified blocked • Guarantees even distribution of strong predictor in small sample

36. Applying Intervention • Blinding -Co-intervention effects -Biased outcome assessment • Choosing the intervention -Effectiveness, safety, blindness, generalizability, combinations • Choosing the control -The control treatment should mimic the active treatment -Ethical co-intervention -Equivalence trials

37. Chapter 11 Designing an Experiment: Clinical Trials II • Maximize follow up and adherence • Measuring outcome • Analyzing results • Monitoring clinical trials • Vulnerable populations • Research misconduct

38. Maximize Follow up and Adherence • Tolerable drug • Daily dosage • Pill counts and drug screening • Easy, convenient, and interesting studies (“participant friendly”)

39. Clinical Trials • Clinical relevance should be balanced with feasibility and cost • Measurable variables: Outcome (Clinical) Risk of outcome (Surrogate)

40. Measuring Outcome • Outcomes should be accurate and precise • Continuous variables preferred over dichotomous • Include outcome measures to detect adverse effects (See FDA website “Good Clinical Practices”)

41. Analyzing Results • Dichotomous: chi squared • Continuous: t test • Intention to treat • Cross-overs covered, prevents bias, may underestimate results (results are conservative) • Preferred over per protocol • Per protocol analyzes only evaluable • May differ from those that drop

42. Subgroup Analysis • Comparing randomized subjects of trials • Randomization measurements should be determined before treatment • Post randomization factors should not be considered • Subgroup size is problematic • May be too small to detect differences • Different findings among subgroups

43. Monitoring Clinical Trials • Prevent harmful intervention • Risks are greater than benefits • Provide beneficial intervention • Discontinue intervention when research question becomes unanswerable • Before study begins, create guidelines and procedures for monitoring • Monitor recruitment, adherence, randomization, blinding, follow up, outcome, adverse effects, potential confounders • Who will monitor and how often • Statistical monitoring methods

44. Committee Monitoring • Intervention and continuation decisions should balance ethical responsibility with advancement of medical knowledge • Committee should be composed of physicians, participant advocates, biostatisticians, and clinical trial experts with no connection to study

45. Options to Randomized Blinded Trials • Factorial Design • Randomization of Matched Pairs • Group or Cluster Randomization

46. Factorial Design • Answers two separate research questions • Efficient and cost effective • Interaction b/t treatments and outcomes • Useful for studying two unrelated questions

47. Randomization of Matched Pairs • Subject pairs with matching factors (ex. age, sex) • Contrast treatment and control in two parts of the same individual at same time (ex. one eye is treated, other serves as control)

48. Group or Cluster Randomization • Assigning by group (ex. family, sports team) • Answers questions about public health programs • More feasible and cost effective than individual assignments • Complicated analysis

49. Within Group Designs • May be used in time-series studies • Time series studies: Participants are their own control for evaluating treatment effects • Disadvantage: Lack of concurrent control group • Efficacy is optimistic b/c learning effects, regression to the mean, secular trends • Repeatedly starting and stopping therapy to establish treatment effects

50. Cross-over Design • Each person is their own control so requires fewer participants • Due to carry over effects: Study duration doubled Complex analysis and interpretation • Carry over effects: Influence of intervention outcome after treatment has stopped • Use washout period to diminish carry over effect • Use when limited number of subjects and unproblematic carry over effects