Evaluating Risk Factors with Regression : A Review and Simulation Study of Current Practices

Sammanfattning: The term ”risk factor” is used synonymously with both predictor and causal factor, and causal aims of explanatory analyses are rarely stated explicitly. Consequently, the concepts of explaining and predicting are conflated in risk factor research. This thesis reviews the current practice of evaluating risk factors with regression in three medical journals and identifies three common covariate selection strategies: adjusting for a pre-specified set, univariable pre-filtering, and stepwise selection. The implication of ”risk factor” varies in the reviewed articles and many authors make implicit causal definitions of the term. In the articles, logistic regression is the most frequently used model, and effect estimates are often reported as conditional odds ratios. The thesis compares current practices to estimating a marginal odds ratio in a simulation study mimicking data from Louapre et al. (2020). The marginal odds ratio is estimated with a regression imputation estimator and an Augmented Inverse Probability Weighting estimator. The simulation study illustrates the difference between conditional and marginal odds ratios and examines the performance of estimators under correctly specified and misspecified models. From the simulation, it is concluded that the estimators of the marginal odds ratio are consistent and robust against certain model misspecifications.