Application and investigation of a bound for outcome reporting bias

A method for calculating the maximum bias in a meta-analysis due to publication bias is adapted for the setting where within-study selective non-reporting of outcomes is suspected, and compared to the alternative approach of missing data imputation. The properties of both methods are investigated in realistic small sample situations.The results suggest that the adapted Copas and Jackson approach is the preferred method for reviewers to apply as an initial assessment of robustness to within-study selective non-reporting.The Copas and Jackson approach is a useful method for systematic reviewers to apply to assess robustness to outcome reporting bias.Publication bias, whereby an entire study is either published or not depending on its results, is recognised as a potential threat to the validity of any meta-analysis. Empirical research suggests that published work is more than twice as likely to be statistically significant (p < 0.05) than unpublished research [1]. Several different methods for the identification of and adjustment for this source of bias are available [2]. More recently, asymptotic results have been presented that allow the meta-analyst to estimate the maximum bias possible in the treatment effect estimate assuming a certain number of trials have not been published [3]. Although the method is proposed as a sensitivity analysis, by varying the number of trials assumed to be missing, rather than a correction for bias, it is still important to investigate how well the result holds since meta-analyses typically include only 5–10 studies. Copas and Jackson suggest bootstrapping as a method for doing this however this is unlikely to be implemented by systematic reviewers unless made available in meta-analysis software.Within-study selective reporting, or outcome reporting bias, has been defined as 'the selection of a subset of the original variables recorded for inclusion in publication of trials' [4]. This may include the situation where only one of several