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.
翻译:许多临床终点措施,如每周消费的标准饮料数量或病人在医院停留的天数等,都是以零为单位的计数数据。然而,在临床试验分析中,有时忽略了这类结果的零膨胀性质。这导致对研究一级干预效应的偏差估计,从而在元分析中对总体干预效应的偏差估计。本研究报告提出了一种新的统计方法,即零膨胀比亚校正(ZIBC)方法,这可以说明在使用Poisson回归模型时所引入的偏差,尽管随机临床试验的结果分配结果出现高比例的零膨胀率。这一纠正方法只需要个别研究的简要信息来纠正干预效果估计,如使用零膨胀普瓦森回归模型进行适当估计,因此在某些研究中没有单个参与者一级数据时,对元分析有吸引力。模拟研究和真实数据分析表明,在大多数情况下,ZIBC方法在纠正零通货膨胀偏差方面表现良好。