Methods for handling missing data and covariate measurement error in individual participant data meta-analysis

近几十年来，人们一直在努力确保医学以证据为基础，这意味着有关患者护理和公共卫生的决定是根据目前可获得的最佳证据做出的。就某一特定临床或公共卫生问题确定可获得的最佳证据的核心是证据合成过程。对于可以用数字量化的临床问题，综合证据的主要工具是荟萃分析，它包括从以前的研究中获取结果，并将它们结合起来，对感兴趣的数量给出一个单一的总结估计。荟萃分析的黄金标准方法包括整理来自所有先前进行的相关研究的个体参与者数据（IPD），并分析由此产生的综合数据集。与传统的荟萃分析方法相比，汇集个人层面的数据具有许多优势，传统的荟萃分析方法涉及结合研究的总体结果（而不是分析原始的个人层面的数据）。这些优势包括能够对一组一致的变量进行统计调整，探索不同患者组之间的治疗效果是否不同，以及调查变量之间关系的形状的能力。然而，有许多问题威胁到IPD荟萃分析的潜力。其中主要是由于数据缺失和测量误差造成的问题。在IPD荟萃分析中，数据缺失有两个原因。第一种是当一些研究没有收集一个或多个感兴趣的变量的数据时，这些变量的值对于这些研究的所有参与者来说都是缺失的。第二种情况是，由于各种原因，一些参与者的值缺失，尽管研究打算收集变量。缺少数据会导致结果不那么精确，并可能存在偏差。当感兴趣的变量只能被不精确地测量时，就会出现测量误差。如果忽略，测量误差也会导致结果偏差。拟议的研究旨在发展新的统计方法来处理这两个问题。通过这样做，他们将使研究人员能够从IPD荟萃分析中获得更精确和更少偏见的估计，从而为重要的临床和公共卫生问题提供更准确的答案。新的方法将发表在科学期刊上，并将其纳入统计软件包，使研究人员能够使用这些方法。这将有助于使医生和公共卫生专家能够根据现有的最佳证据作出决定和制定政策，从而更普遍地改善患者和人口的健康结果。该工作将由奖学金申请人进行。

In recent decades there has been a concerted drive towards ensuring medicine is evidence based, meaning that decisions about patient care and public health are made in light of the current best available evidence. Central to establishing what constitutes the best available evidence in regards to a particular clinical or public health question is the process of evidence synthesis. For clinical questions which can be numerically quantified, the primary tool for synthesizing evidence is meta-analysis, which involves taking the results from previous studies and combining them to give a single summary estimate of the quantity of interest.The gold standard approach to meta-analysis involves collating the individual participant data (IPD) from all of the previously conducted relevant studies and analysing the resulting combined dataset. Pooling the individual level data confers a number of advantages compared to the traditional meta-analysis approach which involves combining the overall results of studies (as opposed to analysing their original, individual level data). These advantages include the ability to make statistical adjustments for a consistent set of variables, exploration of whether treatment effects vary between different groups of patients, and the ability to investigate the shape of relationships between variables.However, there are a number of issues which threaten the potential of IPD meta-analysis. Principal among these are issues caused by missing data and measurement error. Missing data occur for two reasons in IPD meta-analyses. The first is when some studies did not collect data on one or more variables which are of interest, such that the values of these variables are missing for all participants in these studies. The second occurs when, for a variety of reasons, some participants have missing values despite the fact the study intended to collect the variable. Missing data cause results to be less precise and possible biased. Measurement error occurs when variables of interest can only be measured imprecisely. If ignored, measurement error also causes biases in results.The proposed research seeks to develop new statistical methods to deal with these two issues. By doing so, they will enable researchers to obtain more precise and less biased estimates from IPD meta-analyses, thereby giving more accurate answers to important clinical and public health questions. New methods will be published in scientific journals, and methods implemented into statistical software packages to enable them to be used by researchers. This will help enable medical practitioners and public health experts to base their decisions and policies on the best available evidence, thus improving health outcomes for patients and the population more generally.The work will be conducted by the Fellowship applicant.