Statistical models of publication basis in meta-analysis.
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Objectives: To review, apply and compare existing publication bias methodology. To
extend the selection model methods that adjust combined estimates and to develop
models to adjust for publication bias and heterogeneity simultaneously. '
Methods: Methodologies that test for the existence of publication bias, estimate the
number of missing studies, and adjust combined estimates for publication bias are
reviewed. Parametric weighted distribution methodology is developed further. The
existing family of distributions is extended to include a logistic function. Weight
functions previously limited to modelling selection based on two-tailed p-values have
been restructured for one-tailed p-values. The selection mechanism model has been
developed to incorporate both p-values and precision. The model for effect size has
been developed to incorporate linear predictors, so heterogeneity and publication bias
can be modelled simultaneously.
Data: Two systematic reviews taken from the Cochrane Library and simulation studies.
Results: Methods that test for the existence of publication bias or estimate the number
of missing studies are limited by the strength of their assumptions and low power.
Weighted distributions offer the only way to directly assess the impact of publication
bias. In data sets in which there is heterogeneity or the true treatment effect is null,
modelling the selection mechanism on p-values only can lead to over-adjusted estimates
and considerable variability between estimates with wide confidence intervals.
Extending the selection model to include precision reduces this. It is then possible to
include other covariates such as study quality or type. The effect-size model can be
extended in a similar way to include linear predictors. Combination of these two models
allows simultaneous consideration of the influence of publication bias and
heterogeneity.
Conclusions: Weighted distributions offer a flexible approach to modelling publication
bias. Inclusion of precision in the selection model reduces sensitivity of the model to the
shape of the selection model improving consistency of results. No selection model
should be used on its own but in conjunction with others to allow a sensitivity approach
Authors
Preston, Carrol LesleyCollections
- Theses [3702]