Multilevel latent class (MLC) modelling of healthcare provider causal effects on patient outcomes: Evaluation via simulation

TL;DR

This paper introduces a multilevel latent class modeling approach to evaluate healthcare provider effects on patient outcomes, effectively balancing prediction and causal inference in simulated healthcare data.

Contribution

The study proposes a novel MLC model that separates prediction and causal inference tasks, improving accuracy in estimating provider effects while accounting for patient variation.

Findings

MLC model accurately recovered simulated provider effects.

Successful identification of Trust-level covariates with at least 3 latent classes.

Credible intervals widen with increased error variance.

Abstract

Where performance comparison of healthcare providers is of interest, characteristics of both patients and the health condition of interest must be balanced across providers for a fair comparison. This is unlikely to be feasible within observational data, as patient population characteristics may vary geographically and patient care may vary by characteristics of the health condition. We simulated data for patients and providers, based on a previously utilized real-world dataset, and separately considered both binary and continuous covariate-effects at the upper level. Multilevel latent class (MLC) modelling is proposed to partition a prediction focus at the patient level (accommodating casemix) and a causal inference focus at the provider level. The MLC model recovered a range of simulated Trust-level effects. Median recovered values were almost identical to simulated values for the binary Trust-level covariate, and we observed successful recovery of the continuous Trust-level covariate with at least 3 latent Trust classes. Credible intervals widen as the error variance increases. The MLC approach successfully partitioned modelling for prediction and for causal inference, addressing the potential conflict between these two distinct analytical strategies. This improves upon strategies which only adjust for differential selection. Patient-level variation and measurement uncertainty are accommodated within the latent classes.