New publication in BMC Medical Research Methodology

In our latest publication, we developed a new methodological approach to obtain fair comparisons of survival probabilities, and differences thereof, across study clusters such as hospitals, regions or other hierarchical units. The proposed approach combines posterior prediction of the random effects with regression standardisation to account for differences in the case-mix distribution between the clusters. We also developed an accompanying Stata command, available on our company's GitHub page.

The paper combines regression standardisation with posterior predictions of the random effects in multilevel survival models to produce standardised survival probabilities that allow for fair and interpretable comparisons between hierarchical units. These standardised predictions quantify how the entire study population would have fared under the performance of a specific cluster. By adjusting for and standardising over a common case mix, differences in case-mix are accounted for and predictions for different clusters can be compared fairly.

The method is demonstrated using a three-level dataset with patients nested within surgeons nested within centres, and shows how the methodology could be used to benchmark best, worst, and average providers, compare surgeons within a centre, compare centres directly, and compute contrasts between units, all from a single unified model. This approach could be valuable anywhere you need fair, risk-based comparisons of higher-level units while adjusting for individual case-mix and censoring.