Causal Model Building in the Context of Cardiac Rehabilitation: A Systematic Review.

ABSTRACT: Randomization is an effective design option to prevent bias from confounding in the evaluation of the causal effect of interventions on outcomes. However, in some cases, randomization is not possible, making subsequent adjustment for confounders essential to obtain valid results. Several methods exist to adjust for confounding, with multivariable modeling being among the most widely used. The main challenge is to determine which variables should be included in the causal model and to specify appropriate functional relations for continuous variables in the model. While the statistical literature gives a variety of recommendations on how to build multivariable regression models in practice, this guidance is often unknown to applied researchers. We set out to investigate the current practice

SUBMITTER: Akbari N 

PROVIDER: S-EPMC9968189 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature