Dataset for: Meta-analysis of quantitative individual patient data: two stage or not two stage?

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Morris, TP, Fisher, DJ, Kenward, MG and Carpenter, JR. 2018. Dataset for: Meta-analysis of quantitative individual patient data: two stage or not two stage? [Online]. Figshare. Available from: - https://doi.org/10.6084/m9.figshare.5662267.v1

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Quantitative evidence synthesis through meta-analysis is central to evidence-based medicine. For well-documented reasons, the meta-analysis of individual patient data (IPD) is held in higher regard than aggregate data. With access to IPD, the analysis is not restricted to a ‘two-stage’ approach (combining estimates and standard errors) but can estimate parameters of interest by fitting a single model to all of the data; a so-called ‘one-stage’ analysis. There has been debate about the merits of one- and two-stage analysis. Arguments for one-stage analysis have typically noted that a wider range of models can be fitted and overall estimates may be more precise. The two-stage side has emphasised that the models that can be fitted in two-stages are sufficient to answer the relevant questions, with less scope for mistakes because there are fewer modelling choices to be made in the two-stage approach. Considering Gaussian data, we consider the statistical arguments for flexibility and precision in the small-sample settings. Regarding flexibility, several of the models that can be fitted only in one stage may not be of serious interest to most meta-analysis practitioners. Regarding precision, we consider fixed- and random-effects meta-analysis, and see that, for a model making certain assumptions, the number of stages used to fit this model is irrelevant; the precision will be approximately equal. Meta-analysts should choose modelling assumptions carefully. Sometimes relevant models can only be fitted in one stage. Otherwise, meta-analysts are free to use whichever procedure is most convenient to fit the identified model.

Published in a 3rd party system Date: 19 January 2018
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Aggregation
Data Creators(s): Morris, TP, Fisher, DJ, Kenward, MG and Carpenter, JR
LSHTM Faculty/Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Participating Institutions: London School of Hygiene & Tropical Medicine, London, United Kingdom, London Hub for Trials Methodology Research, MRC Clinical Trials Unit at UCL, London, United Kingdom

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