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.

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.

SOAP Analogy

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…

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

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

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

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.

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).

What We Want Measure in a Study Exposure Outcome ?

Approaches to the Third Variable

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

Confounders

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

Confounding: An Example Age Cigarette Exposure Death ?

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…

Mediators

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

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?

Effect Modification

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

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

Returning to the SOAP Analogy

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.

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

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.

Questions Thank you.