Generalization of trim and fill for application in meta-regression.

Trim and fill is a popular method of accounting for publication bias in meta-analysis. However, the use of trim and fill is limited to the setting in which all meta-analyzed studies represent a true common effect. In many practical settings, within-study effect estimates are a function of some covariate. Because methods of accounting for publication bias in meta-regression have received little attention, we propose here a generalization of trim and fill for application in meta-regression. The proposed algorithm preserves the computational features of trim and fill and adds only an assumption of symmetry in the hypothesized distribution of the measured covariate. By simulation, we evaluate properties (mean bias, root mean squared error, and coverage probability) of meta-regression parameter estimates and corresponding confidence intervals with application of the proposed algorithm in a range of scenarios, including violation of the aforementioned assumption of symmetry. We also evaluate the performance of common estimators of the number of suppressed studies. In general, we show that the proposed algorithm is successful in identifying suppression of studies and reducing the bias in regression parameters derived from the analysis of the augmented set of studies. We apply the proposed algorithm to an analysis of the effect of cognitive-behavioral therapy on the risk of recidivism.