Meta-analysis with missing study-level sample variance data.
Stat Med
; 35(17): 3021-32, 2016 07 30.
Article
em En
| MEDLINE
| ID: mdl-26888093
ABSTRACT
We consider a study-level meta-analysis with a normally distributed outcome variable and possibly unequal study-level variances, where the object of inference is the difference in means between a treatment and control group. A common complication in such an analysis is missing sample variances for some studies. A frequently used approach is to impute the weighted (by sample size) mean of the observed variances (mean imputation). Another approach is to include only those studies with variances reported (complete case analysis). Both mean imputation and complete case analysis are only valid under the missing-completely-at-random assumption, and even then the inverse variance weights produced are not necessarily optimal. We propose a multiple imputation method employing gamma meta-regression to impute the missing sample variances. Our method takes advantage of study-level covariates that may be used to provide information about the missing data. Through simulation studies, we show that multiple imputation, when the imputation model is correctly specified, is superior to competing methods in terms of confidence interval coverage probability and type I error probability when testing a specified group difference. Finally, we describe a similar approach to handling missing variances in cross-over studies. Copyright © 2016 John Wiley & Sons, Ltd.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Metanálise como Assunto
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
/
Systematic_reviews
Limite:
Humans
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
Estados Unidos