A Bias Correction Method in Meta-analysis of Randomized Clinical Trials with no Adjustments for Zero-inflated Outcomes

TL;DR

This paper introduces the Zero-inflation Bias Correction (ZIBC) method to adjust for bias in meta-analyses of clinical trials with zero-inflated count data, improving effect estimates when individual data are unavailable.

Contribution

The study proposes a novel ZIBC approach that corrects bias in meta-analysis of zero-inflated outcomes using only summary data, applicable when individual data are missing.

Findings

ZIBC effectively reduces bias in simulated scenarios.

Real data analysis confirms ZIBC's practical utility.

Method performs well across various zero-inflation levels.

Abstract

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.