Regulatory Science Virtual Symposium: “Study Design for Clinical Trials: Types and Trends” Session 2: CTSI Clinical Study Design Types (2023)

1. Objectives
   1. Review of study designs, including clinical trials
   2. Examples of study designs and clinical trials
   3. Alignment of study designs with a research question, data collection, and statistical analysis.
   4. What statistical methods are appropriate for study design and data collected?
2. PICOT Criteria to Develop the Research Question
   1. P = Population
   2. I = Intervention/Exposure
   3. C = Comparison Group
   4. O = Outcome
   5. T = Time
3. Spectrum of Study Designs
4. Observation Study Defined
   1. Clinical studies where participants already belong to certain study groups rather than assigned by the investigators and often exposures or interventions are self-selected
   2. Association between exposures/interventions and outcomes may be biased by characteristics that differ between those that choose exposure vs. no exposure
5. Cohort Study
   1. Involve individuals free of the outcome, including persons with and without exposure
   2. Follow forward in time to determine outcome
   3. Statistical methods: Chi-square, logistic regression, propensity scores
6. Case-Control Study
   1. Select persons with (cases) and without (controls) outcome and then go back to determine their past exposure (before outcome occurred)
   2. Example: VRE in cirrhotic patients
   3. Statistical methods: Chi-square test, T-test or non-parametric Wilcoxon rank sum, and logistic regression
7. Clinical Trial Defined
   1. Clinical study in which participants are assigned to receive intervention (or no intervention) so that researchers can evaluate effects of intervention on biomedical/health-related outcomes
      1. A cohort study where persons are “assigned” to exposures and followed for ascertainment of outcomes
      2. Not feasible when assigning to an exposure/intervention is not ethical
8. Quasi-Experimental Study
   1. Assign groups without randomization
   2. Considered as “natural” experiments
      1. Design takes advantage of what would already happening or a change in the natural setting; usually utilized in healthcare settings
   3. Comparator is the pre-implementation period
   4. Statistical methods: Regression model for counts
9. Clinical Trial Designs: Parallel Group
   1. Parallel group: each participant is assigned to one and only one of the trial interventions
      1. Standard approach for most clinical trials
   2. Statistical methods: Non-parametric Wilcoxon signed rank within group, non-parametric Wilcoxon rank sum between group (major), and intervention effect sizes
10. Crossover
    1. Each participant receives both the experimental and comparator interventions, usually in randomized order, with a wash-out period in between interventions
    2. Each participant acts as their own control, which reduces the variability
    3. Disadvantages: greater likelihood of dropout; must be a stable disease under study; only appropriate for interventions that wash-out and have short-term outcomes
    4. Statistical methods: need to consider the correlated data, Paired t-tests, mixed effects regression model
11. Cluster Randomized
    1. Definition: Unit of randomization is a group of persons rather than a single person
    2. Necessary for complex interventions in primary care, health promotion, community/public health settings i.e., schools
    3. Avoids contamination of intervention effects however requires more subjects and blinding is not possible
    4. Statistical methods: mixed effects linear regression models
12. Clinical Trial Designs: Equivalence or Non-Inferiority Trials
    1. Trials that hypothesize that new intervention groups will demonstrate trial outcomes that are the same as (equivalency) or no worse than (non-inferiority) a currently standard intervention
    2. Appropriate if there is a standard and effective intervention available
    3. Allows the testing of the efficacy of interventions but requires the naming of an “equivalence or non-inferiority margin” that should have a clear clinical rationale
13. Adaptive Intervention
    1. Conduct a sequential multiple-assignment randomized trial (SMART)
14. Statistical Analysis Plan
    1. Ties directly back to your research questions, aims, and hypotheses
    2. Must address the following: dependent/outcome variables, independent variables, method and frequency of measurement
15. Testing Differences Among Groups
    1. Certain statistical analyses would be more appropriate for particular comparisons
       1. For categorical data, groups are compared with chi-square tests that incorporate the matching (McNemar’s test for proportions)
       2. For continuous data, groups are compared with parametric or non-parametric tests
16. Survival Time Data
    1. Two components:
       1. Did the subject have the event (death)?
       2. What is the last time the subject was observed?
17. Linear Regression (and Other Regression Models)
    1. Use linear regression to adjust for other variables
18. Other Regression Models
    1. Continuous outcome: linear regression
    2. Dichotomous outcome: logistic regression
    3. Ordinal categorial outcome: ordinal logistic regression
    4. Nominal outcome (not ordered): multinomial logistic regression
    5. Count outcome: Poisson or negative binomial regression
    6. Survival outcome: Cox (proportional hazards) or other “survival” regression
19. Questions?

Accompanying text created by Roxy Terteryan, RKS Project Administrator, SC CTSI (atertery@usc.edu) and Cyan Tan, Student Worker