Statistical methodology for meta-analysis of epidemiological studies using individual participant data.

越来越多的因素被认为是慢性疾病的重要预测因素和/或原因，特别是随着能够快速测量大量血液蛋白质和遗传因素的技术的出现。为了能够更全面和更有力地评估这些因素的相关性，通常有必要汇集不同研究的数据。如果这样的研究能够可靠地证明特定因素与某种疾病(如心脏病)相关，那么这可能对疾病的预测和预防(例如测量和修改血液胆固醇值)具有重要意义。我们计划推进统计方法的开发，以便在这种数据汇集方法中使用，方法是对以前从大约100项研究的100万名参与者中整理的多达40,000例心脏病发作的详细信息进行工作。主要目的是开发方法，使人们能够就以下问题得出更可靠的结论：(I)直线(或某种更复杂的关系)是否最好地描述了因素和疾病风险之间的关系，以及(Ii)特定因素与疾病风险的关联是否可能反映因果关系。将开发的方法将适用于许多不同的情况和不同的疾病，随着在大型、协作的多中心研究中继续进行数据共享和汇集的趋势，这些方法将变得越来越重要。

An increasing number of factors are being proposed as important predictors and/or causes of chronic diseases, particularly with the advent of technologies that enable rapid measurement of large numbers of blood proteins and genetic factors. To enable a more comprehensive and powerful evaluation of the relevance of such factors, it is often necessary to pool data from different studies. If such studies can reliably demonstrate that a particular factor is relevant to a condition (such as heart disease), then this could have important implications for the prediction and prevention of disease (exemplified by measurement and modification of blood cholesterol values). We plan to advance the development of statistical methods for use in such data pooling approaches by working on detailed information previously collated on up to 40,000 cases of heart attack among 1 million participants from about 100 studies. The main aim is to develop methods that will enable more reliable conclusions to be drawn about (i) whether a straight line (or some more complicated relationship) best describes the relationship between a factor and the risk of disease, and (ii) whether associations of particular factors with disease risk are likely to reflect cause-and-effect relationships. The methods that will be developed will have applications to many different situations and to different diseases, and will become increasingly important as the trend continues towards data sharing and pooling in large, collaborative multi-centre studies.