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

Abstract

Objective

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 Sources and Study Setting

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.

Study Design

Simulation studies of split-sample reliability estimation were conducted under varying practical scenarios.

Data Collection/Extraction Methods

All data were simulated using functions in R.

Principal Findings

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.

Conclusions

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.