Propensity score model overfitting led to inflated variance of estimated odds ratios.

ABSTRACT:

Objective

Simulation studies suggest that the ratio of the number of events to the number of estimated parameters in a logistic regression model should be not less than 10 or 20 to 1 to achieve reliable effect estimates. Applications of propensity score approaches for confounding control in practice, however, do often not consider these recommendations.

Study design and setting

We conducted extensive Monte Carlo and plasmode simulation studies to investigate the impact of propensity score model overfitting on the performance in estimating conditional and marginal odds ratios using different established propensity score inference approaches. We assessed estimate accuracy and precision as well as associated type I error and type II error rates in testing the null hypothesis of

SUBMITTER: Schuster T 

PROVIDER: S-EPMC5756087 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature