Data and Information Integration for Risk Prediction in the Era of Big Data

摘要 在精准医疗和精准疾病预防方面，该提案的总体目标是 开发创新的统计方法，以准确预测风险。我们应对三大挑战， 关于候选风险预测因素的价值的瘟疫研究，这些预测因素增加了已建立的预测因素， 预测准确性：通常缺乏独立的验证数据， 研究样本和预测的目标人群往往不同，没有统计方法 目前可用于使用个体匹配的病例对照数据开发风险预测模型， 缺乏统计方法来帮助评估研究的可行性， 用于检验预测因子-结果关联的计算。另一方面，数据和信息， 研究的外部因素很可能存在，可以用来缓解这些挑战。例如是 只有标准预测因子的模型经常存在并得到验证，标准预测因子的分布 在预测的目标人群的风险预测往往是可用的。我们建议外部数据和 可以利用这些信息来解决候选预测器的上述挑战 评估，并制定创新的统计方法，使这一想法的成果。考虑 预测的二元结果，我们提出了一种新的方法来建立逻辑预测模型， 保证在目标人群中校准良好，这是一种风险预测的创新方法， 个体匹配的病例对照数据，以及一种将候选预测因子的附加值投影到 帮助评估研究的可行性。我们的方法，加上用户友好的软件，将有助于降低成本， 有效和及时的预测因子评估，用于预测二元结局。我们的方法的动机是 并将应用于陈丕的几项合作研究。

Abstract Toward precision medicine and precision disease prevention, the overarching goal of this proposal is to develop innovative statistical methods for accurate risk prediction. We address three challenges that plague studies on the value of candidate risk predictors that adds to established predictors for improved predictive accuracy: there is often a lack of independent validation data, the source population for the study sample and the target population of prediction are often different, no statistical methods are currently available for developing risk prediction models using individually-matched case-control data, and there is a lack of statistical methods for helping assess study feasibility beyond standard power calculation for testing predictor-outcome association. On the other hand, data and information that are external to the study may well exist and can be exploited to alleviate these challenges. For example, a model with only standard predictors often exists and has been validated, and the distribution of standard risk predictors in the target population of prediction is often available. We propose that external data and information can be exploited to address the above-mentioned challenges for candidate predictor evaluation, and develop innovative statistical methods to bring this idea to fruition. Considering prediction of a binary outcome, we propose a novel method to building logistic prediction models that are guaranteed to calibrate well in the target population, an innovative method for risk prediction with individually matched case-control data, and a method to project the added value of candidate predictors to help assess study feasibility. Our methods, accompanied by user-friendly software, will facilitate cost effective and timely predictor evaluation for predicting binary outcomes. Our methods were motivated by and will be applied to several PI Chen's collaborative studies.