Abstract Number:
2463
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Shintaro Hirano (1), Takayuki Abe (2)
Institutions:
(1) A2 Healthcare Corp., Graduate school of Yokohama City University, Tokyo, Japan, (2) Kyoto Women’s University, School of Data Science, Kyoto, Japan
Co-Author:
Takayuki Abe
Kyoto Women’s University, School of Data Science
First Author:
Shintaro Hirano
A2 Healthcare Corp., Graduate school of Yokohama City University
Presenting Author:
Abstract Text:
Meta-analysis is a statistical method for quantitatively synthesizing primary study evidence. Meta-regression is used to assess whether study-level predictors can explain a part of heterogeneity in the results of primary studies. The problem of missing data can arise when primary studies do not provide candidate predictors for meta-regression, but there is insufficient research on how to deal with that. It is implied that the majority of meta-regressions using a single covariate might be due to the fact that increasing the number of covariates makes it more difficult to deal with the problem of missing data. Multiple imputation is well established and practical method for missing data, while it is known that its use in the context of missing covariates and the use of weights in the imputation model should be carefully considered. In this presentation, we will discuss how to deal with missing covariates in meta-regression using multiple imputation methods. Especially, the algorithms and models of the imputation methods, including weighted regression, will be compared under the settings of various missing mechanisms via simulation studies.
Keywords:
meta-analysis|meta-regression|missing data|multiple imputation| |
Sponsors:
Biopharmaceutical Section
Tracks:
Missing Data
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