Meta-analysis of individual patient data versus aggregate data from longitudinal clinical trials
Meta-analysis of individual patient data versus aggregate data from longitudinal clinical trials
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DOI：
10.1177/1740774508100984
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发表时间：
2009-02-01
期刊：
影响因子：
2.7
通讯作者：
Whitehead, Anne
中科院分区：
文献类型：
--
作者：
Jones, Ashley P.;Riley, Richard D.;Whitehead, Anne
背景在临床试验中，在一段时间内跟踪个体，可以在试验过程中的多个时间点进行相同的评估。我们对Cochrane系统评价中处理纵向数据的当前实践的回顾表明，最常用的方法是忽略重复观察之间的相关性，并在多个时间点的每个时间点进行单独的荟萃分析。本文的目的是展示用于聚合数据的重复测量模型和当个体患者数据(IPD)可用时使用的重复测量模型之间的联系，并根据可用的数据类型为从业者可能用于聚合数据元分析的方法提供指导。方法我们讨论当IPD可用时纵向连续结果数据的荟萃分析模型。在这些模型中，时间要么作为一个因素，要么作为一个连续变量，并考虑了重复观测之间的相关性。可采用一步法或两步法进行IPD的荟萃分析：后者涉及在每项研究中单独分析IPD，然后结合研究估计数，同时考虑到其协方差结构。我们讨论了用于聚合数据的模型和两步IPD方法之间的联系，以及当只有聚合数据可用时出现的问题。结果集合数据的荟萃分析的两个主要问题是缺乏关于相关系数的信息和缺失数据在患者水平的影响。对阿尔茨海默病数据集的应用表明，忽略相关性可能导致对治疗差异及其标准误差的不同合并估计。此外，患者水平的缺失数据的数量可能会影响这些估计。限制模型假设在研究中固定的治疗效果，并且任何缺失的数据都是随机缺失的，无论是在患者水平还是在研究水平。结论最好从所有研究中获得IPD，以正确地解释重复观察之间的相关性。当IPD不可用时，理想的汇总数据是基于模型的治疗差异估计及其方差和协方差估计。如果没有协方差估计，应进行敏感性分析，以调查结果对不同相关量的稳健性。临床试验2009；6：16-27。Http://ctj.sagepub.com
Background In clinical trials following individuals over a period of time, the same assessment may be made at a number of time points during the course of the trial. Our review of current practice for handling longitudinal data in Cochrane systematic reviews shows that the most frequently used approach is to ignore the correlation between repeated observations and to conduct separate meta-analyses at each of a number of time points.Purpose The purpose of this paper is to show the link between repeated measurement models used with aggregate data and those used when individual patient data (IPD) are available, and provide guidance on the methods that practitioners might use for aggregate data meta-analyses, depending on the type of data available.Methods We discuss models for the meta-analysis of longitudinal continuous outcome data when IPD are available. In these models time is included either as a factor or as a continuous variable, and account is taken of the correlation between repeated observations. The meta-analysis of IPD can be conducted using either a one-step or a two-step approach: the latter involves analysing the IPD separately in each study and then combining the study estimates taking into account their covariance structure. We discuss the link between models for use with aggregate data and the two-step IPD approach, and the problems which arise when only aggregate data are available. The methods are applied to IPD from 5 trials in Alzheimer's disease.Results Two major issues for the meta-analysis of aggregate data are the lack of information about correlation coefficients and the effect of missing data at the patient-level. Application to the Alzheimer's disease data set shows that ignoring correlation can lead to different pooled estimates of the treatment difference and their standard errors. Furthermore, the amount of missing data at the patient level can affect these estimates.Limitations The models assume fixed treatment effects across studies, and that any missing data is missing at random, both at the patient-level and the study level.Conclusions It is preferable to obtain IPD from all studies to correctly account for the correlation between repeated observations. When IPD are not available, the ideal aggregate data are model-based estimates of treatment difference and their variance and covariance estimates. If covariance estimates are not available, sensitivity analyses should be undertaken to investigate the robustness of the results to different amounts of correlation. Clinical Trials 2009; 6: 16-27. http://ctj.sagepub.com