Speaker
Description
Improved predictions of quality of care indicators in the tail of its distribution
Els Goetghebeur, Ghent University
Standard mixed methods have been popular for evaluating performance across care centers in terms of indicators that summarize residents’ outcomes. Their results tend to lack power, however, for the detection of poor performance [1]. This stems from regression to the mean when estimating center performance based on the usual BLUP estimation of the center-specific random effect. To avoid this, one has turned to fixed effects models that may be Firth corrected.
Recently, optimally weighted BLUPS methods' have been proposed [2] that allow for better prediction of extreme (e.g. poor) outcome indicators after fitting the mixed model for continuous outcomes. In this talk, we adapt this approach to find improved standardized QoL measures for diagnosing outlying center performance. We evaluate how these results compare with those ofstandard random effects models' or `adapted fixed effects models' for the purpose of diagnosing centers with poor outcomes. We apply the new method to evaluate causal estimands for quality of life in care centers and discuss extensions that allow for death as an intercurrent event when residents are followed over time.
References
1. M. Varewyck, E. Goetghebeur, M. Eriksson and S. Vansteelandt. (2014) On shrinkage and model extrapolation in the evaluation of clinical center performance. Biostatistics, 15: 651-664
2. C. E. McCulloch and J. M. Neuhaus. (2023) Improving predictions when interest focuses on extreme random effects. Journal of the American Statistical Association, 118(541):504–513.
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