Performance of the Trim and Fill Method in Adjusting for the Publication Bias in Meta-Analysis of Continuous Data

Abstract

Publication bias in meta-analysis is a serious issue as it may lead to biased estimates which appear to be precise. A popular method for detecting and adjusting the publication bias is the trim and fill method. This study uses simulated meta-analysis to quantify the effects of publication bias on the overall meta-analysis estimates of continuous data where the absolute mean difference was utilized as the measure of effect. It additionally evaluates the performance of the trim and fills method for adjusting the publication bias in terms of statistical bias, the standard errors and the coverage probability. The results demonstrate that if the publication bias is not adjusted it could lead to up to 40% biased in treatment effect estimates. Utilization of the trim and fill method has reduced the bias in the overall effect estimate by more than half. It is optimum in presence of moderate underlying bias but has minimal effects in presence of low and severe bias. Additionally, the trim and fill method improves the coverage probability by more than half when subjected to the same level of publication bias as those of the unadjusted data. However, the method tends to produce false positive results. A sensitivity analysis suggests that the trim and fill method will incorrectly adjust the data for publication bias between 10-45% of the time (for the 5% nominal level). Although the data was incorrectly adjusted, it was found that the Percentage Relative Bias (PRB) introduced into the estimates due to this adjustment is minimal (min: 0.007%, max: 0.109%) and coverage probability for estimates based on this incorrectly adjusted data is not significantly different from those of which is correctly not adjusted. Therefore the trim and fill method is recommended be routinely used when conducting meta-analysis.