1. Introduction to Clinical Research Design Lee E. Morrow, MD, MS Assistant Professor of Medicine Creighton University

2. Clinical Research Designs Descriptive Describe incidence of outcomes over time Describe incidence of outcomes over time Case Reports Case Reports Case Series Case Series Registries Registries Cross Sections Cross Sections Analytic Analyze associations between predictors and outcomes Observational Cohort Studies Case-Control Studies Experimental Clinical Trials

3. Descriptive Studies Often a first step in research Often a first step in research Doesn’t always have a specific hypothesis to be tested Doesn’t always have a specific hypothesis to be tested Causality usually cannot be determined Causality usually cannot be determined Examples: Examples: Case Reports/Series Case Reports/Series Registries Registries Cross Sectional Studies Cross Sectional Studies

4. Case Reports/Series Definition: A single/series of patients with or without a disease or exposure of interest for whom data are collected in any fashion Definition: A single/series of patients with or without a disease or exposure of interest for whom data are collected in any fashion Sources: Clinics, hospitals, disease registries Sources: Clinics, hospitals, disease registries Limitations: Not randomly selected, bias due to selection factors inherent in the source, not representative of the population from which they are selected Limitations: Not randomly selected, bias due to selection factors inherent in the source, not representative of the population from which they are selected

5. Case Reports/Series Benefits: Easy to do, useful for exploring relationships and/or generating hypotheses Benefits: Easy to do, useful for exploring relationships and/or generating hypotheses Key Point: Associations seen in case series are highly likely to be biased and frequently do NOT hold up in more rigorous studies Key Point: Associations seen in case series are highly likely to be biased and frequently do NOT hold up in more rigorous studies

6. Cross-Sectional Studies Definition: A study based on a sample selected at one point or period in time Definition: A study based on a sample selected at one point or period in time Population Risk Factor Present Risk Factor Absent

7. Cross-Sectional Studies Definition: A study based on a sample selected at one point or period in time Definition: A study based on a sample selected at one point or period in time Sample Population Risk Factor Present Risk Factor Absent

8. Cross-Sectional Studies Definition: A study based on a sample selected at one point or period in time Definition: A study based on a sample selected at one point or period in time Sample Population No Disease Disease Disease Risk Factor Present Risk Factor Absent

9. Cross-Sectional Studies If looking at a specified moment in time: point prevalence If looking at a specified moment in time: point prevalence If looking at a specified moment in time plus all new cases during the specified time period: period prevalence If looking at a specified moment in time plus all new cases during the specified time period: period prevalence If looking only at new cases during the specified time period: incidence If looking only at new cases during the specified time period: incidence

10. Cross-Sectional Studies Which cases are included in 7/1/02 point prevalence? Which cases are included in 7/1/02-6/30/03 period prevalence? Which cases are included in incidence? 7/1/026/30/03 1 2 3 4 6 7 8 9 5

11. Cross-Sectional Studies 7/1/02 point prevalence cases: 1, 2, 8 7/1/02-6/30/03 period prevalence cases: 1, 2, 3, 4, 6, 8, 9 incidence cases: 3, 4, 6, 9 7/1/026/30/03 1 2 3 4 6 7 8 9 5

12. Cross-Sectional Studies Assuming N=100, calculate the 7/1/02 point prevalence. Calculate the 7/1/02-6/30/03 period prevalence. Calculate the incidence rate. 7/1/026/30/03 1 2 3 4 6 7 8 9 5

13. Cross-Sectional Studies 7/1/02 point prevalence: 3% 7/1/02-6/30/03 period prevalence: 7% incidence rate: 4% 7/1/026/30/03 1 2 3 4 6 7 8 9 5

14. Cross-Sectional Studies Limitations: Limitations: Exposure and outcome are assessed at the same time by the investigator (no temporality) Exposure and outcome are assessed at the same time by the investigator (no temporality) Sample selection is not based on exposure or outcome Sample selection is not based on exposure or outcome Prevalence estimate is affected by duration of disease: disease with longer duration is more likely to be detected Prevalence estimate is affected by duration of disease: disease with longer duration is more likely to be detected Must consider “at risk” population only Must consider “at risk” population only

15. Cross-Sectional Studies Benefits Benefits Easy Easy Cheap Cheap Gives a “snap-shot” of exposure and outcome Gives a “snap-shot” of exposure and outcome Good for hypothesis generation Good for hypothesis generation

16. Analytic Studies Involve a specific hypothesis that can be tested using a statistical model Involve a specific hypothesis that can be tested using a statistical model Involve assessing exposures as a predictor of outcomes Involve assessing exposures as a predictor of outcomes Examples: Examples: Observational: Cohort Studies, Case-Control Studies Observational: Cohort Studies, Case-Control Studies Experimental: Clinical Trials Experimental: Clinical Trials

17. Cohort Studies Involve following a group (cohort) of subjects over time Involve following a group (cohort) of subjects over time Usually analytic but may be descriptive Usually analytic but may be descriptive Was a treatment specifically initiated for evaluation? Was a treatment specifically initiated for evaluation? No: Simple Cohort Study No: Simple Cohort Study Yes: Clinical Trial Yes: Clinical Trial Randomized Randomized Non-Randomized Non-Randomized

18. Cohort Studies Prospective Cohort Studies Prospective Cohort Studies Investigator defines sample and predictor variables before any outcomes have occurred Investigator defines sample and predictor variables before any outcomes have occurred Retrospective Cohort Studies Retrospective Cohort Studies Investigator defines sample and collects information about predictor variables after the outcomes have occurred Investigator defines sample and collects information about predictor variables after the outcomes have occurred

19. Prospective Cohort Studies Risk Factor Present Risk Factor Absent The Present Population Is a given Risk Factor associated with a given Disease?

20. Prospective Cohort Studies Risk Factor Present Risk Factor Absent The Present Population Sample Is a given Risk Factor associated with a given Disease?

21. Prospective Cohort Studies Risk Factor Present Risk Factor Absent No Disease Disease Disease The Present The Future Population Sample Is a given Risk Factor associated with a given Disease?

22. Retrospective Cohort Studies No Disease Disease Disease The Present Is a given Risk Factor associated with a given Disease?

23. Retrospective Cohort Studies Risk Factor Present Risk Factor Absent No Disease Disease Disease The Past The Present Population Sample Is a given Risk Factor associated with a given Disease?

24. Cohort Studies in General Strengths Strengths Powerful strategy for directly measuring the incidence of a disease Powerful strategy for directly measuring the incidence of a disease Can examine multiple outcomes and multiple exposures Can examine multiple outcomes and multiple exposures Easier to establish temporal relationship: improves inference for causality Easier to establish temporal relationship: improves inference for causality

25. Cohort Studies in General Weaknesses Weaknesses Attrition of the sample Attrition of the sample Level of exposure may change over time Level of exposure may change over time Inability to identify presence of confounders and effect modifiers Inability to identify presence of confounders and effect modifiers Susceptible to follow-up bias: there may be a difference in the exposure-disease relationship for those who follow-up and those who do not Susceptible to follow-up bias: there may be a difference in the exposure-disease relationship for those who follow-up and those who do not Cost and feasibility vs. representativeness: general population sample vs. restricted cohort sample Cost and feasibility vs. representativeness: general population sample vs. restricted cohort sample

26. Prospective Cohort Studies Strengths Strengths Allows opportunity for complete and accurate measurement of risk factors Allows opportunity for complete and accurate measurement of risk factors Uniquely valuable for studying the antecedents of fatal diseases Uniquely valuable for studying the antecedents of fatal diseases End-point unknown: can take a long time for sufficient number of cases to develop End-point unknown: can take a long time for sufficient number of cases to develop Observer bias Observer bias Weaknesses Weaknesses Expensive and inefficient for rare diseases Expensive and inefficient for rare diseases Observer bias Observer bias

27. Retrospective Cohort Studies Strengths Strengths Much less costly and time consuming Much less costly and time consuming Observer bias Observer bias Weaknesses Weaknesses Less control over the nature and quality of predictor variable data collected Less control over the nature and quality of predictor variable data collected Incomplete data sets Incomplete data sets Observer bias, recall bias Observer bias, recall bias

28. Risk Ratios in Cohort Studies The Risk Ratio (RR) is the ratio of the incidence of disease in exposed persons to the incidence of disease in non-exposed persons The Risk Ratio (RR) is the ratio of the incidence of disease in exposed persons to the incidence of disease in non-exposed persons RR = Cumulative Incidence in Exposed Cumulative Incidence in Non-Exposed

29. Risk Ratios in Cohort Studies RR calculation requires incidence data RR calculation requires incidence data Used in cohort and intervention studies Used in cohort and intervention studies Not used in Case-Control Not used in Case-Control RR = a/(a+b) c/(c+d) ab c d Diseased + - Exposed + -

30. Risk Ratios in Cohort Studies Is a measure of the strength of association between exposure and outcome: does not imply causality… Is a measure of the strength of association between exposure and outcome: does not imply causality…

31. Case-Control Studies Compares people with disease (cases) to people without disease (controls) with respect to history of exposure Compares people with disease (cases) to people without disease (controls) with respect to history of exposure If exposure is different between cases and controls, an association exists between exposure and disease If exposure is different between cases and controls, an association exists between exposure and disease Cases must represent the population of all cases while controls must represent the population of all non-diseased Cases must represent the population of all cases while controls must represent the population of all non-diseased

32. Case-Control Studies Population with Disease Population without Disease The Present

33. Case-Control Studies Population with Disease Population without Disease The Present Risk Factor Present Risk Factor Absent

34. Case-Control Studies Population with Disease Population without Disease The Present Risk Factor Present Risk Factor Absent Select Cases Select Controls

35. Case-Control Studies Population with Disease Population without Disease The Present Risk Factor Present Risk Factor Absent The Past D+/RF+ D+/RF- D+/RF+ D+/RF- D-/RF+ D-/RF-

36. Case-Control Studies Strengths Strengths Shorter study period is possible Shorter study period is possible Rare diseases are more easily studied Rare diseases are more easily studied Less expensive Less expensive Multiple risk factors may be studied Multiple risk factors may be studied Particularly useful for studying new diseases about which little is known Particularly useful for studying new diseases about which little is known

37. Case-Control Studies Weaknesses Weaknesses Choice of appropriate controls is usually very difficult (selection bias) Choice of appropriate controls is usually very difficult (selection bias) Cases and controls do not usually come from the same population (selection bias) Cases and controls do not usually come from the same population (selection bias) May be difficult to assess whether exposure preceded disease (recall bias) May be difficult to assess whether exposure preceded disease (recall bias) Incidence rates cannot be calculated directly Incidence rates cannot be calculated directly

38. Odds Ratios in Case-Control Studies The Odds Ratio (OR) provides an estimate of the Risk Ratio (RR) for Case-Control studies The Odds Ratio (OR) provides an estimate of the Risk Ratio (RR) for Case-Control studies OR is a good estimate of the RR if the disease is “rare” (incidence <10% per year in the population) OR is a good estimate of the RR if the disease is “rare” (incidence <10% per year in the population) Is a measure of the strength of association between exposure and outcome: does not imply causality… Is a measure of the strength of association between exposure and outcome: does not imply causality…

39. Nested Case-Control Studies Select disease cases from within a cohort study Select disease cases from within a cohort study Controls are selected from non-diseased cases within the same cohort, within the same time period as the cases develop Controls are selected from non-diseased cases within the same cohort, within the same time period as the cases develop If controls are randomly selected from within the cohort (i.e.: includes diseased subjects in the case group and the control group) it is a Case-Cohort Study If controls are randomly selected from within the cohort (i.e.: includes diseased subjects in the case group and the control group) it is a Case-Cohort Study

40. A Few Words About Controls The most difficult aspect of Case-Control Studies is selecting appropriate controls The most difficult aspect of Case-Control Studies is selecting appropriate controls Matching is often used to eliminate the effect of potential confounders Matching is often used to eliminate the effect of potential confounders Technically speaking, matching reduces the variance of the OR! Technically speaking, matching reduces the variance of the OR! Matching is difficult to do correctly and may paradoxically worsen analysis problems if done incorrectly Matching is difficult to do correctly and may paradoxically worsen analysis problems if done incorrectly Impossible to match for unknown confounders Impossible to match for unknown confounders

41. Clinical Trials Definition: A clinical trial is a scientific experiment involving human subjects which is designed to evaluate the effects of intervention(s) against a particular disease in order to elucidate the most appropriate care for future subjects Definition: A clinical trial is a scientific experiment involving human subjects which is designed to evaluate the effects of intervention(s) against a particular disease in order to elucidate the most appropriate care for future subjects

42. Clinical Trials Controlled* or Uncontrolled Controlled* or Uncontrolled Is there a concurrent comparison group? Is there a concurrent comparison group? Randomized* or Nonrandomized Randomized* or Nonrandomized Are subjects randomly allocated to the control and experimental groups? Are subjects randomly allocated to the control and experimental groups? Parallel Group or Crossover Parallel Group or Crossover Parallel group implies each subject receives only one of the interventions Parallel group implies each subject receives only one of the interventions Crossover implies each subject receives successively each of the interventions Crossover implies each subject receives successively each of the interventions *Hence the terminology RCT

43. Clinical Trials: Randomization Participants are randomly assigned to “Exposure” or “No Exposure” Participants are randomly assigned to “Exposure” or “No Exposure” Randomization refers to assigning subject to an intervention arm without regard for baseline characteristics Randomization refers to assigning subject to an intervention arm without regard for baseline characteristics Goal of randomization is to equalize all other exposures that may confound or bias the association between Treatment and Outcome Goal of randomization is to equalize all other exposures that may confound or bias the association between Treatment and Outcome

44. Clinical Trials: Blinding Single Blinding: examiners do not know treatment assignment Single Blinding: examiners do not know treatment assignment Double Blinding: examiners and subjects do not know treatment assignment Double Blinding: examiners and subjects do not know treatment assignment Triple Blinding: examiners, subjects, and statisticians do not know treatment assignments Triple Blinding: examiners, subjects, and statisticians do not know treatment assignments Blinding is not always possible… Blinding is not always possible…

45. Clinical Trials Advantages Advantages Minimizes confounding and bias through randomization Minimizes confounding and bias through randomization Allows clear assessment of temporal association Allows clear assessment of temporal association Permits a test of causality between exposure and disease Permits a test of causality between exposure and disease

46. Clinical Trials Disadvantages Disadvantages Ethical considerations of treatment or with-holding treatment Ethical considerations of treatment or with-holding treatment Harms (drug side effects, emotional distress) may outweigh benefits Harms (drug side effects, emotional distress) may outweigh benefits Expensive and time-consuming Expensive and time-consuming Loss to follow up Loss to follow up Non-adherence to group assignment Non-adherence to group assignment Possible early termination Possible early termination Cannot always randomize an exposure Cannot always randomize an exposure

47. Quasi-Experimentation This is essentially a clinical trial without randomization This is essentially a clinical trial without randomization Not possible to randomize: patients being enrolled in a rare disease trial at a site which does not have access to a given intervention Not possible to randomize: patients being enrolled in a rare disease trial at a site which does not have access to a given intervention Not ethical to randomize: patients with cancer who have already failed the chemo in one arm of a trial cannot ethically be randomized to that arm Not ethical to randomize: patients with cancer who have already failed the chemo in one arm of a trial cannot ethically be randomized to that arm Uses statistical deductive processes to rule out threats to plausibility Uses statistical deductive processes to rule out threats to plausibility Causal inference is less strong Causal inference is less strong

48. Factorial Designs This is essentially an attempt to evaluate multiple interventions concurrently This is essentially an attempt to evaluate multiple interventions concurrently Given costs and inconvenience of recruiting, this is particularly appealing Given costs and inconvenience of recruiting, this is particularly appealing Not a valid model if interaction, adds complexity, potential for polypharmacy, reviewer skepticism Not a valid model if interaction, adds complexity, potential for polypharmacy, reviewer skepticism ab c d Intervention X - Intervention Y - CellIntervention aX + Y bY + Placebo cX + Placebo dPlacebo

49. Example 1: Design Type? Investigators obtained lists of RNs age 25-42 in the 11 most populous U.S. states Investigators obtained lists of RNs age 25-42 in the 11 most populous U.S. states They mailed baseline questionnaires about diet and other risk factors They mailed baseline questionnaires about diet and other risk factors Follow-up questionnaires were sent every 2 years for 20 years assessing additional risk factors and the development of disease outcomes Follow-up questionnaires were sent every 2 years for 20 years assessing additional risk factors and the development of disease outcomes

50. Example 1: Nurses’ Health Study Prospective Cohort Study Prospective Cohort Study Assembled a cohort Assembled a cohort Assessed baseline risk factors Assessed baseline risk factors In the future assessed disease outcomes In the future assessed disease outcomes Repeated Cross Sectional Study Repeated Cross Sectional Study Described changes over time in characteristics of the same study population Described changes over time in characteristics of the same study population

51. Example 2: Design Type? Investigators reported a 12-year old boy with adrenomyeloneuropathy Investigators reported a 12-year old boy with adrenomyeloneuropathy Disease progression was markedly attenuated by treatment with a combination of oleic and erucic acids Disease progression was markedly attenuated by treatment with a combination of oleic and erucic acids

52. Example 2: Lorenzo’s Oil Case Report Case Report A single patient A single patient Descriptive Descriptive No specific hypothesis being tested No specific hypothesis being tested Often not representative of the population at large… Often not representative of the population at large…

53. Example 3: Design Type? Investigators assembled 23 patients with adrenomyeloneuropathy from a national data base Investigators assembled 23 patients with adrenomyeloneuropathy from a national data base Randomized to two-years of treatment with oleic and erucic acids vs. placebo Randomized to two-years of treatment with oleic and erucic acids vs. placebo No statistically significant difference in disease progression, survival, etc. No statistically significant difference in disease progression, survival, etc.

54. Example 3: Lorenzo’s Oil II Randomized, Placebo Controlled Clinical Trial Randomized, Placebo Controlled Clinical Trial Prospective Cohort with an Intervention Prospective Cohort with an Intervention Cohort of subjects selected based on presence of disease of interest Cohort of subjects selected based on presence of disease of interest Followed prospectively over time Followed prospectively over time A treatment was specifically initiated for evaluation A treatment was specifically initiated for evaluation Treatment was allocated randomly Treatment was allocated randomly Treatment was compared to placebo Treatment was compared to placebo

55. Example 4: Design Type? Investigators were interested in determining the prevalence of various pathology subtypes of inoperable lung cancer Investigators were interested in determining the prevalence of various pathology subtypes of inoperable lung cancer Through an institutional registry identified all patients diagnosed with a new lung cancer in the prior year (476 patients) Through an institutional registry identified all patients diagnosed with a new lung cancer in the prior year (476 patients) Found that 38% were epidermoid, 28% were small cell, 18% were adenocarcinoma, 13% were large cell, and 3% were other types Found that 38% were epidermoid, 28% were small cell, 18% were adenocarcinoma, 13% were large cell, and 3% were other types

56. Example 4 Cross-Sectional Study Cross-Sectional Study A “snapshot” in time A “snapshot” in time Subjects are included based on the designated point/period in time of interest Subjects are included based on the designated point/period in time of interest Purely descriptive Purely descriptive

57. Example 5: Design Type? Investigators recorded the smoking histories of 1357 men with and 1357 age-matched men without lung cancer Investigators recorded the smoking histories of 1357 men with and 1357 age-matched men without lung cancer Risk of lung cancer is estimated to be 3.4 times greater for the smokers than for the non- smokers Risk of lung cancer is estimated to be 3.4 times greater for the smokers than for the non- smokers Doesn’t imply causality: replace “smoked” with “carried lighter” Doesn’t imply causality: replace “smoked” with “carried lighter” 857457 500900 Lung Cancer + - Smoked + -

58. Example 5 Case-Control Study Case-Control Study Investigator selects people with disease (cases) Investigator selects people with disease (cases) Investigator selects people without disease (controls) Investigator selects people without disease (controls) Matching is often used is case-control studies Matching is often used is case-control studies The cases and controls are then compared with respect to a history of exposure The cases and controls are then compared with respect to a history of exposure

59. Example 6: Design Type? Investigators followed 40,000 British MDs for 10 years Investigators followed 40,000 British MDs for 10 years Stratified by the number of cigarettes smoked each day at the start of the study Stratified by the number of cigarettes smoked each day at the start of the study Assessed annual death rate from lung cancer Assessed annual death rate from lung cancer Found that the risk of death from lung cancer was 32 times greater for heavy smokers as for non-smokers Found that the risk of death from lung cancer was 32 times greater for heavy smokers as for non-smokers

60. Example 6: British Physician Study Prospective Cohort Study Prospective Cohort Study Investigator selects sample (cohort) and predictor variables before any outcomes have occurred Investigator selects sample (cohort) and predictor variables before any outcomes have occurred The sample is then followed over time The sample is then followed over time

61. Example 7: Design Type? Goal was to describe the natural history of thoracic aortic aneurysms and risk factors for rupture Goal was to describe the natural history of thoracic aortic aneurysms and risk factors for rupture Investigators identified 133 patients diagnosed with aortic aneurysms Investigators identified 133 patients diagnosed with aortic aneurysms Reviewed records to collect data on gender, age, size of aneurysm, risk factors for CV disease, rupture of aneurysms, and surgical repair of aneurysms Reviewed records to collect data on gender, age, size of aneurysm, risk factors for CV disease, rupture of aneurysms, and surgical repair of aneurysms 31% of aneurysms >6 cm ruptured, 0% 6 cm ruptured, 0% <4 cm ruptured

62. Example 7 Retrospective Cohort Study Retrospective Cohort Study Identify a cohort based on past data Identify a cohort based on past data Collect data on predictors from past data Collect data on predictors from past data Collect data on outcomes from past and/or present data Collect data on outcomes from past and/or present data