  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.
