A Bias-Corrected Net Reclassification Improvement for Clinical Subgroups

Background. Comparing prediction models using reclassification within subgroups at intermediate risk is often of clinical interest. Objective. To demonstrate a method for obtaining an unbiased estimate for the Net Reclassification Improvement (NRI) evaluated only on a subset, the clinical NRI. Study Design 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. Results. 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. Conclusion. Our proposed method is an improvement over currently used methods of calculating the clinical NRI and is recommended to reduce overly optimistic results.