Inverse Probability Weights for Quasi-Continuous Ordinal Exposures with a Binary Outcome: Method Comparison and Case Study

Abstract

Abstract Inverse probability weighting (IPW), a well-established method to control for confounding in observational studies with binary exposures, has been extended to analyses with continuous exposures. Methods developed for continuous exposures may not apply when the exposure is quasi-continuous due to irregular exposure distributions that violate key assumptions. We used simulations and cluster-randomized clinical trial data to assess four approaches developed for continuous exposures – ordinary least squares (OLS), covariate balancing generalized propensity scores (CBGPS), non-parametric covariate balancing generalized propensity scores (npCBGPS), and quantile binning (QB) – and a novel method – a cumulative probability model (CPM) – in quasi-continuous exposure settings. We compared IPW stability, covariate balance, bias, mean squared error, and standard error estimation across 3000 simulations with six different quasi-continuous exposures, varying in skewness and granularity. In general, CBGPS and npCBGPS resulted in excellent covariate balance, and npCBGPS was the least biased but most variable. The QB and CPM approaches had the lowest mean squared error, particularly with marginally skewed exposures. We then successfully applied the IPW approaches, together with missing-data techniques, to assess how session attendance (out of 15) in a partners-based clustered intervention among pregnant couples living with HIV in Mozambique influenced postpartum contraceptive uptake.