1. A SOAP Approach to Clinical Research Design and Analysis
Martha Carvour, MD, PhD
April 17, 2018
M. Carvour has no financial disclosures.
Content is by M. Carvour unless otherwise noted.
All figures are intended for conference/classroom purposes only.

2. After Completion of This Session, Participants Should Be Able to…
Explain how approaches to clinical research design and analysis can follow a familiar SOAP approach.
Embrace their critical role in the clinical reasoning behind clinical research design and analysis.
Effectively consult statistical collaborators just as they might consult other providers in the clinical setting.

3. SOAP Analogy

4. SOAP Analogy
Statistical tests, like laboratory tests, are meaningful in context.
Always present stats within their context.
Be wary of stats presented without a context!
Before we order any laboratory tests…
Before we order any statistical tests…

5. Subjective: Elements of a Good History
Before we order any laboratory tests…
Before we order any statistical tests…

6. Objective: Elements of a Good Exam
Before we order any laboratory tests…
Before we order any statistical tests…

7. Objective: Elements of a Good Exam
Descriptive
Analytic
Comparisons across groups
T-test, Wilcoxon-Mann- Whitney, ANOVA
Chi-square, Fisher exact test
Multivariable modeling
Numerical or graphical characteristics of groups
Averages (mean, median, mode)
Frequencies (percentages, distributions)
Depictions of subgroups

8. Objective: A Logical Approach
Approach is similar to a physical exam.
Descriptive stats should be fairly thorough and methodical.
Analytic stats should be focused and hypothesis-directed.
For complex stats, consultation may be appropriate.
Your assessment will help to frame an effective consultation and to interpret the results.

9. Assessment: Clinical Reasoning Counts!
Statistical software and statistical analysts are not equipped with the same knowledge of your topic.
Let’s take the frequently encountered example of the multivariable model….
Image from Pixabay (CC License).

10. What We Want Measure in a Study
Exposure
Outcome ?

11. Approaches to the Third Variable

12. What Else Affects This Measure
Exposure
Outcome ?
Other Important Variables
Covariates
Confounders
Mediators
Effect modifiers
Colliders
Let’s look at a few examples…

13. Confounders

14. Confounding
What our statistics may reflect if we don’t account for confounding
Confounder
Exposure
Outcome ?
What we actually want to measure

15. Confounding: An Example
Age
Cigarette
Exposure
Death ?

16. Confounding and Its Imposters
Confounders must be…
Associated with the exposure of interest and
Associated with the outcome of interest but
NOT on the causal pathway between the exposure and outcome.
Variables that do not fit all of the above criteria should not be treated as confounders.
Is the variable a mediator?
Is the variable an effect modifier?
Let’s look at mediators next…

17. Mediators

18. Returning to the Example
COPD
Cigarette
Exposure
Death
Why might treating COPD as a confounder lead to undesirable consequences?
Let’s review one more important scenario…
COPD=Chronic obstructive pulmonary disease

19. Another Scenario: “It Depends”
Exposure
Outcome ?
What happens when the relationship between the exposure and outcome depends on another variable?
Are exposed women more likely to have an outcome than exposed men?
Will a treatment work in one group of patients in your study but not another?

20. Effect Modification

21. Effect Modification For instance…
In this case, the effect of the exposure differs for men and women (i.e., the effect is modified by sex). Women have a higher risk of disease compared to men.
Exposure
Disease
High Risk
Among Women
Exposure
Disease
Low Risk
Among Men

22. Putting the Pieces Together
How does a variable affect the relationship of the exposure and outcome?
It is associated with both exposure and outcome but not on the causal pathway.
It is on the causal pathway between exposure and outcome.
It depends. The relationship differs depending on the value of the other variable.
Mediator
Confounder
Effect Modifier

23. Returning to the SOAP Analogy

24. Plan: A Systematic Approach
Gather and prioritize the information you have.
Is a consult needed?
Consult as early as possible, even during the study design stage. This can prevent lost time and resources during analysis.
Equip yourself with your initial results and assessment to foster effective dialogue.
Request clarification and rationale from collaborators. You may not need to do everything your consultant suggests.

25. Plan: A Systematic Approach
Disposition: Always have the desired ends in mind and have a plan for reaching those ends.
Publication plans
Future research phases or studies
Prophylaxis: Keep documentation of “must-haves”!
Human subjects approvals
Authorship and acknowledgements

26. Summary
Statistical tests, like laboratory tests, are meaningful in context.
Familiar approaches to clinical reasoning provide a framework for approaching clinical research.
Nothing replaces a good history and exam!
Start with basic descriptive statistics.
Choose complex analytic statistics judiciously.
Collaborate with your stat consultants. You have insights they may lack.

27. Questions
Thank you.