METHODS FOR EPIDEMIOLOGIC DATA WITH MISSING VALUES

重新提交第一个奖项提案的研究的长期目标是为研究人员提供处理研究应用中缺失数据的新方法。第一个奖项的目的将是：1)开发和推广当预测者包含缺失数据时倾向分数估计的统计方法；2)将这一方法应用于各种应用的数据集。为了实现这些目标，提出了几个目标。这些措施包括：开发和推广在预测者包含缺失数据时估计倾向分数的方法；开发允许预测者包含可能不是“随机缺失”的缺失数据的方法；开发诊断以评估有效性和是否适合竞争模型；开发用户友好的软件来实施这一方法；以及将这些方法应用于四个实际数据应用程序。这些应用包括：1)使用弗雷明翰心脏研究的数据来估计风险评估功能，以预测缺乏风险因素的个人的心血管疾病或中风等结果；2)使用由北加利福尼亚州凯撒永久研究部提供的由120,000多名成员组成的糖尿病登记数据，开发有助于在存在缺少风险因素信息的情况下识别糖尿病相关并发症的高风险的糖尿病人的技术；3)使用来自绝经后雌激素/孕激素干预(PEPI)临床试验的数据来拟合模型，以根据可能包含缺失数据的预测因素来估计表示坚持用药的概率的倾向分数，然后使用这些倾向分数来找到激素治疗对心血管疾病风险因素、骨密度和其他症状的影响的调整估计；以及4)使用来自腺瘤性息肉遗传流行病学研究的数据来拟合估计模型，考虑到缺失风险因素的存在，估计特定基因与结果的关系。这项研究的结果将对医学、流行病学和统计学研究做出重要贡献。随着关于遗漏价值的统计方法的发展和推广，预计将出版方法学和应用出版物。此外，将在所提供的应用数据集上使用这一新方法回答实质性的医学和流行病学问题。

The long term objective of the research in this resubmission of a FIRST Award proposal is to provide investigators with new methodology for handling missing data in research applications. The aims of this FIRST Award will be 1) to develop and extend statistical methodology for propensity score estimation when predictors contain missing data and 2) to apply this methodology to a variety of applied data sets. To address these aims several goals are proposed. These include: developing and extending methodology for estimating propensity scores when predictors contain missing data; developing methodology that allows predictors to contain missing data that may not be "missing at random"; developing diagnostics to assess the validity and fit for competing models; developing user friendly software for implementing this methodology; and applying these methods to four real data applications. These applications include: 1) using data from the Framingham Heart Study to estimate risk appraisal functions for predicting such outcomes as cardiovascular disease or stroke for individuals with missing risk factors; 2) using data from a diabetes registry consisting of over 120,000 members, provided by the Division of Research at Kaiser Permanente, Northern California, to develop techniques that aid in identifying diabetic persons who are at high risk for developing diabetic related complications in the presence of missing risk factor information; 3) using data from the Postmenopausal Estrogen/Progestin Intervention (PEPI) clinical trial to fit models to estimate propensity scores which represent the probability of medication adherence conditional on predictors that may contain missing data, and then use these propensity scores to find adjusted estimates of the effects of hormone therapy on cardiovascular disease risk factors, bone mineral density, and other symptoms and; 4) using data from the Genetic Epidemiology of Adenomatous Polyps study to fit models estimating the relationship of specific genes to outcomes considering the presence of missing risk factors. The results from this research will make important contributions to medical, epidemiological and statistical research. Methodological and applied publications are anticipated as statistical methodology concerning missing values will be developed and extended. In addition, substantive medical and epidemiological questions will be answered using this new methodology on the applied data sets provided.