Statistical methods for assessing the influence of study characteristics on treatment effects in 'meta-epidemiological' research.
Biases in systematic reviews and meta-analyses may be examined in 'meta-epidemiological' studies, in which the influence of trial characteristics such as measures of study quality on treatment effect estimates is explored. Published studies to date have analysed data from collections of meta-analyses with binary outcomes, using logistic regression models that assume that there is no between- or within-meta-analysis heterogeneity. Using data from a study of publication bias (39 meta-analyses, 394 published and 88 unpublished trials) and language bias (29 meta-analyses, 297 English language trials and 52 non-English language trials), we compare results from logistic regression models, with and without robust standard errors to allow for clustering on meta-analysis, with results using a 'meta-meta-analytic' approach that can allow for between- and within-meta-analysis heterogeneity. We also consider how to allow for the confounding effects of different trial characteristics. We show that both within- and between meta-analysis heterogeneity may be of importance in the analysis of meta-epidemiological studies, and that confounding exists between the effects of publication status and trial quality.
MRC Health Services Research Collaboration, Department of Social Medicine, University of Bristol, UK. jonathan.sterne@bristol.ac.uk
Jonathan A C
JA
Sterne
Peter
P
Jüni
Kenneth F
KF
Schulz
Douglas G
DG
Altman
Christopher
C
Bartlett
Matthias
M
Egger
D015982:Bias; D004812:Epidemiologic Methods; D006801:Humans; D016015:Logistic Models; D015201:Meta-Analysis as Topic; D012107:Research Design / Q000592:standards*; D013223:Statistics as Topic / Q000379:methods* / Q000592:standards; D016896:Treatment Outcome