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Abstract
We propose a constrained generalised method of moments (CGMM) for enhancing the efficiency of estimators in meta-analysis in which some studies do not measure all covariates associated with the response or outcome. Under some assumptions, we show that the proposed CGMM estimators have good asymptotic properties. We also demonstrate the effectiveness of the proposed method through simulation studies with fixed sample sizes.
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No potential conflict of interest was reported by the authors.
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Notes on contributors
Menghao Xu
Menghao Xu is a doctoral candidate in college of statistics, East China Normal University. His main research direction is variable selection, missing data and survival analysis.
Jun Shao
Jun Shao is a professor in department of University of Wisconsin-Madison and in college of statistics, East China Normal University. His research covers a wide range of fields, s.t. the jackknife, bootstrap and other resampling methods; variable selection and inference with high dimensional data; sample surveys (variance estimation, imputation for nonrespondents); missing data (nonignorable missing, dropout, semi-parametric methods); longitudinal data analysis with missing data and/or measurement error; medical statistics (clinical trials, personalized medicine, bioequivalence). He is the author of Mathematical Statistics, which is a wildly used graduate textbook covering topics in statistical theory essential for graduate students preparing for work on a Ph.D. degree in statistics.