Many clinical endpoint measures, such as the number of standard drinks
consumed per week or the number of days that patients stayed in the hospital,
are count data with excessive zeros. However, the zero-inflated nature of such
outcomes is often ignored in analyses, which leads to biased estimates and,
consequently, a biased estimate of the overall intervention effect in a
meta-analysis. The current study proposes a novel statistical approach, the
Zero-inflation Bias Correction (ZIBC) method, that can account for the bias
introduced when using the Poisson regression model despite a high rate of zeros
in the outcome distribution for randomized clinical trials. This correction
method utilizes summary information from individual studies to correct
intervention effect estimates as if they were appropriately estimated in
zero-inflated Poisson regression models. Simulation studies and real data
analyses show that the ZIBC method has good performance in correcting
zero-inflation bias in many situations. This method provides a methodological
solution in improving the accuracy of meta-analysis results, which is important
to evidence-based medicine