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Explanatory studies, such as randomized controlled trials, are targeted toextract the true causal effect of interventions on outcomes and are by designadjusted for covariates through randomization. On the contrary, observationalstudies are a representation of events that occurred without intervention. Bothcan be illustrated using the Structural Causal Model (SCM), and do-calculus canbe employed to estimate the causal effects. Pragmatic clinical trials (PCT)fall between these two ends of the trial design spectra and are thus hard todefine. Due to its pragmatic nature, no standardized representation of PCTthrough SCM has been yet established. In this paper, we approach this problemby proposing a generalized representation of PCT under the rubric of structuralcausal models (SCM). We discuss different analysis techniques commonly employedin PCT using the proposed graphical model, such as intention-to-treat,as-treated, and per-protocol analysis. To show the application of our proposedapproach, we leverage an experimental dataset from a pragmatic clinical trial.Our proposition of SCM through PCT creates a pathway to leveraging do-calculusand related mathematical operations on clinical datasets.

Pragmatic Clinical Trials in the Rubric of Structural Causal Models