  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 sometimes ignored in analyses of clinical trials. This leads to
biased estimates of study-level intervention effect 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 inflated zeros in the outcome
distribution of a randomized clinical trial. This correction method only
requires summary information from individual studies to correct intervention
effect estimates as if they were appropriately estimated using the
zero-inflated Poisson regression model, thus it is attractive for meta-analysis
when individual participant-level data are not available in some studies.
Simulation studies and real data analyses showed that the ZIBC method performed
well in correcting zero-inflation bias in most situations.
