A bias-corrected net reclassification improvement for clinical subgroups.

Abstract

Comparing prediction models using reclassification within subgroups at intermediate risk is often of clinical interest. To demonstrate a method for obtaining an unbiased estimate for the Net Reclassification Improvement (NRI) evaluated only on a subset, the clinical NRI. and Setting. We derived the expected value of the clinical NRI under the null hypothesis using the same principles as the overall NRI. We then conducted a simulation study based on a logistic model with a known predictor and a potential predictor, varying the effects of the known and potential predictors to test the performance of our bias-corrected clinical NRI measure. Finally, data from the Women's Health Study, a prospective cohort of 24 171 female health professionals, were used as an example of the proposed method. Our bias-corrected estimate is shown to have a mean of zero in the null case under a range of simulated parameters and, unlike the naïve estimate, to be unbiased. We also provide 2 methods for obtaining a variance estimate, both with reasonable type 1 errors. Our proposed method is an improvement over currently used methods of calculating the clinical NRI and is recommended to reduce overly optimistic results.