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

个体参与者数据荟萃分析中处理缺失数据和协变量测量误差的方法

基本信息

批准号：
MR/K02180X/1
负责人：
Jonathan Bartlett
金额：
$ 35.45万
依托单位：
London School of Hygiene & Tropical Medicine
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FK02180X%2F1
关键词：
Methods handling missing data covariate

项目摘要

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.

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最好的证据做出的。确定什么是关于特定临床或公共卫生问题的最佳可用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，合成证据的主要工具是荟萃分析，它涉及获取先前研究的结果并将其组合以给出感兴趣数量的单一汇总估计。荟萃分析的黄金标准方法涉及整理所有先前进行的相关研究的个体参与者数据（IPD）并分析所得的组合数据集。与传统的荟萃分析方法相比，汇集个人水平的数据具有许多优势，传统的荟萃分析方法涉及合并研究的总体结果（而不是分析其原始的个人水平数据）。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，以及能够调查变量之间关系的形状。其中最主要的是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中的所有参与者都是缺失的。第二种情况发生时，由于各种原因，一些参与者有缺失的值，尽管研究旨在收集变量。缺失数据会导致结果不太精确，并可能存在偏倚。测量误差发生在感兴趣的变量只能不精确地测量时。如果忽略测量误差，测量误差也会导致结果的偏差，本研究试图开发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差的估计，从而为重要的临床和公共卫生问题提供更准确的答案。新方法将在科学期刊上发表，并将这些方法纳入统计软件包，供研究人员使用。这将有助于医务人员和公共卫生专家根据现有的最佳证据作出决定和制定政策，从而改善病人和广大民众的健康状况。