Split-sample reliability estimation in health care quality measurement: Once is not enough.

Abstract

To examine the sensitivity of split-sample reliability estimates to the random split of the data and propose alternative methods for improving the stability of the split-sample method. Data were simulated to reflect a variety of real-world quality measure distributions and scenarios. There is no date range to report as the data are simulated. Simulation studies of split-sample reliability estimation were conducted under varying practical scenarios. All data were simulated using functions in R. Single split-sample reliability estimates can be very dependent on the random split of the data, especially in low sample size and low variability settings. Averaging split-sample estimates over many splits of the data can yield a more stable reliability estimate. Measure developers and evaluators using the split-sample reliability method should average a series of reliability estimates calculated from many resamples of the data without replacement to obtain a more stable reliability estimate.