Cholesterol-lowering effect of statin therapy in a clinical HIV cohort: an application of double propensity score adjustment

Abstract

Purpose: Propensity score matching (PSM) is often used to estimate the average treatment effect among the treated (ATT) using observational data. We demonstrate how the use of "double propensity score adjustment" can reduce residual confounding and avoid bias due to incomplete matching compared with traditional PSM methods.

Methods: The DC Cohort is an observational clinical HIV cohort in Washington, DC. We compared the mean percent change in non-high-density lipoprotein cholesterol (non-HDL-C) concentration after 3-12 months between participants treated and participants not treated with statin therapy between 2011 and 2018. We conducted traditional PSM procedures (optimal, nearest neighbor, and nearest neighbor caliper matching) and double propensity score adjustment.

Results: Among 202 treated and 1252 untreated participants, the ATT was -14.5% (95% CI: -18.4, -10.6) after optimal matching (202 matched pairs; 15/22 covariates balanced), -14.9% (-18.9, -11.0) after nearest neighbor matching (202 matched pairs; 17/22 covariates balanced), and -12.0% (-16.5, -7.5) after nearest neighbor caliper matching (153 matched pairs; 21/22 covariates balanced). After double propensity score adjustment, the ATT was -13.0% (-16.0, -10.1).

Conclusions: In PSM analyses, double propensity score adjustment is a readily accessible alternative approach for estimating ATTs when sufficient covariate balance between treatment groups cannot be achieved without excluding treated participants.

Keywords: Causality; Cholesterol; Confounding factors (epidemiology); HIV; Hydroxymethylglutaryl-CoA reductase inhibitors; Matched-pair analysis; Observational study; Propensity score; Research design.