Speaker
Description
The IQTIG measures, compares and evaluates hospital quality using quality indicators. These usually consist of a population and a binary outcome of interest, such as whether complications have occurred after elective knee replacement. For a fair assessment and comparison of hospital quality, we need to adjust for the hospital’s case mix, i.e., for the patient-specific risk factors such as age or previous surgeries on the same knee. We jointly model the effect of these risk factors and the hospital effect on the outcome probability in a regression model based on individual patient data. Since usually there are hospitals with low caseloads and possibly even no outcomes of interest, it is often necessary to penalize the hospital effect to avoid separation issues.
To this end, the hospital effect is often modeled as a random intercept, but Firth regression has also been proposed. Clearly, the choice of penalization influences the estimated hospital effect. Moreover, it also influences the estimates of other effects, such as patient-specific risk factors. We study the effects of the choice of penalty in two applications: In the first application, we want to estimate how the treatment quality depends on the hospital's caseload ("volume-outcome relationship"). In the second application, we aim to quantify the heterogeneity of hospital effects to estimate the potential for improvement when quality improvement measures decrease heterogeneity in treatment quality. While in the latter application, we are primarily interested in the hospital effects themselves, in the former the main interest lies in the (smooth) effect of the caseload.
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