Best (but oft-forgotten) practices: propensity score methods in clinical nutrition research

Abstract

In observational studies, treatment assignment is a nonrandom process and treatment groups may not be comparable in their baseline characteristics, a phenomenon known as confounding. Propensity score (PS) methods can be used to achieve comparability of treated and nontreated groups in terms of their observed covariates and, as such, control for confounding in estimating treatment effects. In this article, we provide a step-by-step guidance on how to use PS methods. For illustrative purposes, we used simulated data based on an observational study of the relation between oral nutritional supplementation and hospital length of stay. We focused on the key aspects of PS analysis, including covariate selection, PS estimation, covariate balance assessment, treatment effect estimation, and reporting. PS matching, stratification, covariate adjustment, and weighting are discussed. R codes and example data are provided to show the different steps in a PS analysis.

Keywords: balance; confounding; matching; model selection; propensity score.