Comparison of commonly used methods in random effects meta-analysis: application to preclinical data in drug discovery research.
BMJ Open Sci
; 5(1): e100074, 2021.
Article
en En
| MEDLINE
| ID: mdl-35047696
ABSTRACT
BACKGROUND:
Meta-analysis of preclinical data is used to evaluate the consistency of findings and to inform the design and conduct of future studies. Unlike clinical meta-analysis, preclinical data often involve many heterogeneous studies reporting outcomes from a small number of animals. Here, we review the methodological challenges in preclinical meta-analysis in estimating and explaining heterogeneity in treatment effects.METHODS:
Assuming aggregate-level data, we focus on two topics (1) estimation of heterogeneity using commonly used methods in preclinical meta-analysis:
method of moments (DerSimonian and Laird; DL), maximum likelihood (restricted maximum likelihood; REML) and Bayesian approach; (2) comparison of univariate versus multivariable meta-regression for adjusting estimated treatment effects for heterogeneity. Using data from a systematic review on the efficacy of interleukin-1 receptor antagonist in animals with stroke, we compare these methods, and explore the impact of multiple covariates on the treatment effects.RESULTS:
We observed that the three methods for estimating heterogeneity yielded similar estimates for the overall effect, but different estimates for between-study variability. The proportion of heterogeneity explained by a covariate is estimated larger using REML and the Bayesian method as compared with DL. Multivariable meta-regression explains more heterogeneity than univariate meta-regression.CONCLUSIONS:
Our findings highlight the importance of careful selection of the estimation method and the use of multivariable meta-regression to explain heterogeneity. There was no difference between REML and the Bayesian method and both methods are recommended over DL. Multiple meta-regression is worthwhile to explain heterogeneity by more than one variable, reducing more variability than any univariate models and increasing the explained proportion of heterogeneity.
Texto completo:
1
Base de datos:
MEDLINE
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
/
Systematic_reviews
Idioma:
En
Revista:
BMJ Open Sci
Año:
2021
Tipo del documento:
Article
País de afiliación:
Reino Unido