METHODS FOR EPIDEMIOLOGIC DATA WITH MISSING VALUES

本次重新提交FIRST奖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。第一个奖项的目的是：1）当预测因子包含缺失数据时，开发和扩展倾向评分估计的统计方法; 2）将此方法应用于各种应用数据集。 为实现这些目标，提出了若干目标。 其中包括：开发和扩展用于在预测因子包含缺失数据时估计倾向分数的方法;开发允许预测因子包含可能不是“随机缺失”的缺失数据的方法;开发诊断以评估竞争模型的有效性和适合性;开发用于实施该方法的用户友好软件;以及将这些方法应用于四个真实的数据应用。 这些应用包括：1）使用来自心脏病研究的数据来估计风险评估函数，用于预测缺失风险因素的个体的心血管疾病或中风等结果; 2）使用由北方加州Kaiser Permanente研究部提供的糖尿病登记处的数据，该登记处由超过120，000名成员组成，开发技术，以帮助确定糖尿病患者谁是在高风险发展糖尿病相关并发症的风险存在缺失的风险因素信息; 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估计倾向评分，所述倾向评分代表以可能包含缺失数据的预测因子为条件的药物依从性的概率，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、骨矿物质密度和其他症状的影响的调整估计值; 4）使用腺瘤性息肉遗传流行病学研究的数据来拟合模型，估计特定基因与考虑缺失风险因素存在的结果的关系。本研究结果将对医学、流行病学和统计学研究做出重要贡献。预计将编写方法和应用出版物，因为将制定和推广有关缺失值的统计方法。 此外，还将根据所提供的应用数据集，采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.