Meta-Analysis of a Continuous Outcome Combining Individual Patient Data and Aggregate Data: A Method Based on Simulated Individual Patient Data
In: Research Synthesis Methods, Jg. 5 (2014-12-01), Heft 4, S. 322-351
Online
academicJournal
Zugriff:
When some trials provide individual patient data (IPD) and the others provide only aggregate data (AD), meta-analysis methods for combining IPD and AD are required. We propose a method that reconstructs the missing IPD for AD trials by a Bayesian sampling procedure and then applies an IPD meta-analysis model to the mixture of simulated IPD and collected IPD. The method is applicable when a treatment effect can be assumed fixed across trials. We focus on situations of a single continuous outcome and covariate and aim to estimate treatment-covariate interactions separated into within-trial and across-trial effect. An illustration with hypertension data which has similar mean covariates across trials indicates that the method substantially reduces mean square error of the pooled within-trial interaction estimate in comparison with existing approaches. A simulation study supposing there exists one IPD trial and nine AD trials suggests that the method has suitable type I error rate and approximately zero bias as long as the available IPD contains at least 10% of total patients, where the average gain in mean square error is up to about 40%. However, the method is currently restricted by the fixed effect assumption, and extension to random effects to allow heterogeneity is required.
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Meta-Analysis of a Continuous Outcome Combining Individual Patient Data and Aggregate Data: A Method Based on Simulated Individual Patient Data
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Autor/in / Beteiligte Person: | Yamaguchi, Yusuke ; Sakamoto, Wataru ; Goto, Masashi |
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Zeitschrift: | Research Synthesis Methods, Jg. 5 (2014-12-01), Heft 4, S. 322-351 |
Veröffentlichung: | 2014 |
Medientyp: | academicJournal |
ISSN: | 1759-2879 (print) |
DOI: | 10.1002/jrsm.1119 |
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