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Full-Text Articles in Medicine and Health Sciences
Biologic Interaction And Their Identification, Tyler J. Vanderweele, James Robins
Biologic Interaction And Their Identification, Tyler J. Vanderweele, James Robins
COBRA Preprint Series
The definitions of a biologic interaction and causal interdependence are reconsidered in light of a sufficient-component cause framework. Various conditions and statistical tests are derived for the presence of biologic interactions. The conditions derived are sufficient but not necessary for the presence of a biologic interaction. Through a series of examples it is made evident that in the context of monotonic effects, but not in general, the conditions which are derived are closely related but not identical to effect modification on the risk difference scale.
A Theory Of Sufficient Cause Interactions, Tyler J. Vanderweele, James M. Robins
A Theory Of Sufficient Cause Interactions, Tyler J. Vanderweele, James M. Robins
COBRA Preprint Series
Sufficient-component causes are discussed within the potential outcome framework so as to formalize notions of sufficient causes, synergism and sufficient cause interactions. Doing so allows for the derivation of counterfactual conditions and statistical tests for detecting the presence of sufficient cause interactions. Under the assumption of monotonic effects, more powerful statistical tests for sufficient cause interactions can be derived. The statistical tests derived for sufficient cause interactions are compared with and contrasted to interaction terms in standard statistical models.
Causal Comparisons In Randomized Trials Of Two Active Treatments: The Effect Of Supervised Exercise To Promote Smoking Cessation, Jason Roy, Joseph W. Hogan
Causal Comparisons In Randomized Trials Of Two Active Treatments: The Effect Of Supervised Exercise To Promote Smoking Cessation, Jason Roy, Joseph W. Hogan
COBRA Preprint Series
In behavioral medicine trials, such as smoking cessation trials, two or more active treatments are often compared. Noncompliance by some subjects with their assigned treatment poses a challenge to the data analyst. Causal parameters of interest might include those defined by subpopulations based on their potential compliance status under each assignment, using the principal stratification framework (e.g., causal effect of new therapy compared to standard therapy among subjects that would comply with either intervention). Even if subjects in one arm do not have access to the other treatment(s), the causal effect of each treatment typically can only be identified from …