Exact inference on the random-effects model for meta-analyses with few studies.

ABSTRACT: We describe an exact, unconditional, non-randomized procedure for producing confidence intervals for the grand mean in a normal-normal random effects meta-analysis. The procedure targets meta-analyses based on too few primary studies, ≤7 , say, to allow for the conventional asymptotic estimators, e.g., DerSimonian and Laird (1986), or non-parametric resampling-based procedures, e.g., Liu et al. (2017). Meta-analyses with such few studies are common, with one recent sample of 22,453 heath-related meta-analyses finding a median of 3 primary studies per meta-analysis (Davey et al., 2011). Reliable and efficient inference procedures are therefore needed to address this setting. The coverage level of the resulting CI is guaranteed to be above the nominal level, up to Monte Carlo error, provided

SUBMITTER: Michael H 

PROVIDER: S-EPMC7045874 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature