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Where performance comparison of healthcare providers is of interest,characteristics of both patients and the health condition of interest must bebalanced across providers for a fair comparison. This is unlikely to befeasible within observational data, as patient population characteristics mayvary geographically and patient care may vary by characteristics of the healthcondition. We simulated data for patients and providers, based on a previouslyutilized real-world dataset, and separately considered both binary andcontinuous 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. TheMLC model recovered a range of simulated Trust-level effects. Median recoveredvalues were almost identical to simulated values for the binary Trust-levelcovariate, and we observed successful recovery of the continuous Trust-levelcovariate with at least 3 latent Trust classes. Credible intervals widen as theerror variance increases. The MLC approach successfully partitioned modellingfor prediction and for causal inference, addressing the potential conflictbetween these two distinct analytical strategies. This improves upon strategieswhich only adjust for differential selection. Patient-level variation andmeasurement uncertainty are accommodated within the latent classes.

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