Performance Evaluation of Parametric and Nonparametric Methods When Assessing Effect Measure Modification.

ABSTRACT: Effect measure modification is often evaluated using parametric models. These models, although efficient when correctly specified, make strong parametric assumptions. While nonparametric models avoid important functional form assumptions, they often require larger samples to achieve a given accuracy. We conducted a simulation study to evaluate performance tradeoffs between correctly specified parametric and nonparametric models to detect effect modification of a binary exposure by both binary and continuous modifiers. We evaluated generalized linear models and doubly robust (DR) estimators, with and without sample splitting. Continuous modifiers were modeled with cubic splines, fractional polynomials, and nonparametric DR-learner. For binary modifiers, generalized linear models showed the

SUBMITTER: Conzuelo Rodriguez G 

PROVIDER: S-EPMC8897998 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature