METHODS FOR EPIDEMIOLOGIC DATA WITH MISSING VALUES

具有缺失值的流行病学数据的方法

基本信息

批准号：
6633329
负责人：
Ralph B. DAgostino
金额：
$ 11.79万
依托单位：
WAKE FOREST UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
1999
资助国家：
美国
起止时间：
1999-08-01 至 2006-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6633329
关键词：
cardiovascular disorder clinical research computer data analysis computer program /software computer simulation data collection methodology /evaluation diabetes mellitus disease /disorder proneness /risk epidemiology human data mathematical model model design /development

项目摘要

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.

该研究在第一个奖励提案的重新提交中的长期目标是为调查人员提供处理研究应用程序中缺少数据的新方法。第一个奖项的目的是1）当预测因子包含丢失的数据和2）将此方法应用于各种应用数据集时，开发和扩展统计方法的倾向得分估计。为了解决这些目标，提出了几个目标。其中包括：开发和扩展方法，用于估计倾向得分时，当预测因子包含丢失的数据时；开发允许预测变量包含可能不会“随机丢失”的数据；开发诊断以评估竞争模型的有效性和合适性；开发用于实施此方法的用户友好软件；并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Framingham心脏研究中的数据来估计风险评估功能，以预测诸如缺失风险因素的个体的心血管疾病或中风之类的结果； 2）使用来自糖尿病注册表的数据，包括120,000多名成员，由北加州Kaiser Permanente的研究部提供，以开发有助于识别糖尿病患者的技术，这些糖尿病患者在存在缺失风险因素信息的情况下在存在糖尿病相关并发症的高风险中； 3）使用来自绝经后雌激素/孕激素干预（PEPI）临床试验的数据来估算模型，以估算倾向得分，这些倾向得分代表了药物疗效的可能性，有条件地条件是可能包含可能缺失数据的预测因子，然后使用这些预测评分来找到对心血管疾病风险因素的效果的调整后的估计，骨骼矿物质矿物矿物质和其他症状，以及其他症状，以及其他症状，以及其他症状，以及其他症状，以及其他症状，以及; 4）使用来自腺瘤息肉研究的遗传流行病学数据来拟合估计特定基因与结果存在的结果的关系的模型。这项研究的结果将为医学，流行病学和统计研究做出重要贡献。预计方法论和应用出版物将被开发和扩展有关缺失值的统计方法。此外，将使用此新方法对所提供的应用数据集回答实质性的医学和流行病学问题。