SynthesisStatistics in medicine2021
A bias correction method in meta-analysis of randomized clinical trials with no adjustments for zero-inflated outcomes.
Synthesis in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Brief Alcohol Interventions are Effective through 6 Months: Findings from Marginalized Zero-inflated Poisson and Negative Binomial Models in a Two-step IPD Meta-analysis.Prevention science : the official journal of the Society for Prevention Research · 2023Pooled it
- A bias correction method in meta-analysis of randomized clinical trials with no adjustments for zero-inflated outcomes.Statistics in medicine · 2021Pooled it
- Which is Better for Individual Participant Data Meta-Analysis of Zero-Inflated Count Outcomes, One-Step or Two-Step Analysis? A Simulation Study.Multivariate behavioral researchPooled it
- Pain when it "counts": hurdle analysis of clinical pain ratings improves data model performance.Pain reports · 2025Article
- A simulation study of the performance of statistical models for count outcomes with excessive zeros.Statistics in medicine · 2024Article
- The selection of statistical models for reporting count outcomes and intervention effects in brief alcohol intervention trials: A review and recommendations.Alcohol, clinical & experimental research · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.