A Bias-Corrected Net Reclassification Improvement for Clinical Subgroups. (February 2013)

• Record Type: Journal Article
• Authors: Paynter, Nina P.; Cook, Nancy R.
• Is Part Of: Medical decision making. Volume 33:Number 2(2013)
• Page Start: 154
• Page End: 162
• Publication Date: 2013-02
• Subjects: cardiology -- internal medicine -- preventive medicine—screening -- public health; Medical policy -- Periodicals; Clinical medicine -- Decision making -- Periodicals; Medicine -- Periodicals; Médecine clinique -- Prise de décision -- Périodiques; 362.1
• DOI: 10.1177/0272989X12461856
• Languages: English
• ISSNs: 0272-989X

Abstract

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.