Meta-analysis techniques in medical research : a statistical perspective.
Hardy, Rebecca Jane;
(1995)
Meta-analysis techniques in medical research : a statistical perspective.
PhD thesis, London School of Hygiene & Tropical Medicine.
DOI: https://doi.org/10.17037/PUBS.00682268
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Meta-analysis is now commonly used in medical research. However there are statistical issues relating to the subject that require investigation and some are considered
here, from both a methodological and a practical perspective.
Each of the fixed effect and the random effects models for meta-analysis are
based on certain assumptions and the validity of these is investigated. A formal test of
the homogeneity assumption made in the fixed effect model may be performed. Since
the test has low power, simulation was used to investigate the power under various
conditions. The random effects model incorporates a between-study component of
variance into the model. A likelihood based method was used to obtain a confidence
interval for this variance and also to provide an interval for the overall treatment
effect which takes into account the fact that the between-study variance is estimated,
rather than assuming it to be known.
In order to obtain confidence intervals for the treatment effect for both the
fixed effect and the random effects models, distributional assumptions of normality
are usually made. Such assumptions may be checked using q-q plots of the residuals obtained for each trial in the meta-analysis. In both meta-analysis models it is
assumed that the weight allocated to each study is known, when in fact it must be
estimated from the data. The effect of estimating the weights on the overall treatment effect estimate, its confidence intervals, the between-study variance estimate
and the test statistic for homogeneity, is investigated by both analytic and simulation
methods.
It is shown how meta-analysis methods may be used to analyse multicentre
trials of a paired cluster randomised design. Meta-analysis techniques are found to
be preferable to previously published methods specifically developed for the analysis
of such designs, which produce biased and potentially misleading results when a large
treatment effect is present.