Novel Applications of Risk Reclassification Methods in CVD Risk Prediction

描述（由申请人提供）：风险预测方程广泛用于心血管医学的风险分层，并确定成本效益和适当的治疗过程。新的预测因子是否可以为已建立的模型（如Frachial风险评分）增加临床效用是一个重要的问题。这些研究人员以前的工作已经引入了比较风险预测模型的新方法，包括风险重新分类，评估新模型更准确地将个体分类为较高或较低风险层的能力。该建议提供了这些方法的几个新的扩展，特别是与校准有关的扩展，它直接比较预测的观察到的风险。这种校准对于估计个人风险和计算绝对风险的差异至关重要。虽然可获得改善辨别力的措施，但本申请提出了检查校准改善的综合方法。此外，由于预先指定的风险层，并没有确定所有的应用程序，一个无类别的重新分类校准措施提出。感兴趣的第二个领域是将重新分类校准方法扩展到其他研究设计。虽然生存数据的方法可用于歧视措施，如何以及校准措施，特别是重新分类卡方统计，扩展到生存设置尚不清楚。此外，病例队列研究和配对病例对照研究在心血管研究中被广泛使用，尤其是用于生物标志物评估。目前尚不清楚重新分类措施如何转化为这些设计。最后，为了限制成本，临床医生通常对疾病的多阶段筛查感兴趣。对那些处于中等风险的人进行重新分类是最感兴趣的，并且“临床NRI”已经被 这是为了评估这一群体的重新分类。这种新方法在实践中的效果如何还有待确定。我们建议进行一系列关于预测模型比较的研究项目，特别是重新分类校准，这将进一步发展这些方法及其临床实用性。我们将在来自妇女健康研究的心血管疾病数据、模拟和现有的关于心血管疾病结局的病例对照和病例队列数据中检查新措施的特征。这些新应用的扩展将是高度创新的，并直接和立即适用于心血管疾病的临床风险预测。

DESCRIPTION (provided by applicant): Risk prediction equations are widely used in cardiovascular medicine for risk stratification and to determine cost-effective and appropriate courses of treatment. Whether new predictors can add clinical utility to established models such as the Framingham risk score is an important question. Previous work by these investigators has introduced new methods for comparing risk prediction models, including risk reclassification, which assesses the ability of new models to more accurately classify individuals into higher or lower risk strata. This proposal offers several novel extensions of these methods, particularly related to calibration, which directly compares the predicted to the observed risk. Such calibration is essential for estimating risk for the individual and computing differences in absolute risk. While measures of improvement in discrimination are available, this application proposes an integrated approach examining improvement in calibration. Also, since pre-specified risk strata are not identified for all applications, a category-free measure of reclassification calibration is proposed. A second area of interest is extending methods for reclassification calibration to other study designs. While methods for survival data are available for discrimination measures, how well measures of calibration, particularly the reclassification chi-square statistic, extend to survival settings is not yet known. In addition, case-cohort and matched case-control studies are widely used in cardiovascular research, especially for biomarker assessment. It is not known how well reclassification measures translate to these designs. Finally, to limit costs, clinicians are often interested in multi-stage screening of diseae. Reclassification of those at intermediate risk is of most interest, and the 'clinical NRI' has been introduced to assess reclassification in this group. How well this new method performs in practice remains to be determined. We propose to conduct a portfolio of research projects regarding the comparison of predictive models in general, and reclassification calibration specifically, that will further the development of these methods and their clinical utility. We wil examine the characteristics of the new measures in data on cardiovascular disease from the Women's Health Study, in simulations, and in existing case-control and case-cohort data on CVD outcomes. Extensions to these novel applications would be highly innovative and directly and immediately applicable to clinical risk prediction for cardiovascular disease.