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1. A bias correction method in meta‐analysis of randomized clinical trials with no adjustments for zero‐inflated outcomes

   https://doi.org/10.1002/sim.9161  

   Zhou, Zhengyang; Xie, Minge; Huh, David; Mun, Eun‐Young (September 2021, Statistics in Medicine)

   Summary 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. 

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2. A Comparison of Zero-Inflated Models for Modern Biomedical Data

   https://doi.org/10.33697/ajur.2025.141  

   Beveridge, Max; Goldstein, Zach; Cheol_Chung, Hee (June 2025, American Journal of Undergraduate Research)

   There has been a growing number of datasets exhibiting an excess of zero values that cannot be adequately modeled using standard probability distributions. For example, microbiome data and single-cell RNA sequencing data consist of count measurements in which the proportion of zeros exceeds what can be captured by standard distributions such as the Poisson or negative binomial, while also requiring appropriate modeling of the nonzero counts. Several models have been proposed to address zero-inflated datasets including the zero-inflated negative binomial, hurdle negative binomial model, and the truncated latent Gaussian copula model. This study aims to compare various models and determine which one performs optimally under different conditions using both simulation studies and real data analyses. We are particularly interested in investigating how dependence among the variables, level of zeroinflation or deflation, and variance of the data affects model selection. KEYWORDS: Zero-InflatedModels; HurdleModels; Truncated Latent Gaussian CopulaModel; Microbiome Data; Gene-Sequencing Data; Zero-Inflation, Negative Binomial; Zero-Deflation 

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3. Generalized fiducial inference on the mean of zero-inflated Poisson and Poisson hurdle models

   https://doi.org/10.1186/s40488-021-00117-0  

   Zou, Yixuan; Hannig, Jan; Young, Derek S. (December 2021, Journal of Statistical Distributions and Applications)

   null (Ed.)

   Abstract Zero-inflated and hurdle models are widely applied to count data possessing excess zeros, where they can simultaneously model the process from how the zeros were generated and potentially help mitigate the effects of overdispersion relative to the assumed count distribution. Which model to use depends on how the zeros are generated: zero-inflated models add an additional probability mass on zero, while hurdle models are two-part models comprised of a degenerate distribution for the zeros and a zero-truncated distribution. Developing confidence intervals for such models is challenging since no closed-form function is available to calculate the mean. In this study, generalized fiducial inference is used to construct confidence intervals for the means of zero-inflated Poisson and Poisson hurdle models. The proposed methods are assessed by an intensive simulation study. An illustrative example demonstrates the inference methods. 

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4. scMODD: A model-driven algorithm for doublet identification in single-cell RNA-sequencing data

   https://doi.org/10.3389/fsysb.2022.1082309  

   Zhao, Xinye; Du, Alexander; Qiu, Peng (January 2023, Frontiers in Systems Biology)

   Single-cell RNA sequencing (scRNA-seq) data often contain doublets, where a doublet manifests as 1 cell barcode that corresponds to combined gene expression of two or more cells. Existence of doublets can lead to spurious biological interpretations. Here, we present s ingle- c ell MO del-driven D oublet D etection ( scMODD ), a model-driven algorithm to detect doublets in scRNA-seq data. ScMODD achieved similar performance compared to existing doublet detection algorithms which are primarily data-driven, showing the promise of model-driven approach for doublet detection. When implementing scMODD in simulated and real scRNA-seq data, we tested both the negative binomial (NB) model and the zero-inflated negative binomial (ZINB) model to serve as the underlying statistical model for scRNA-seq count data, and observed that incorporating zero inflation did not improve detection performance, suggesting that consideration of zero inflation is not necessary in the context of doublet detection in scRNA-seq. 

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5. Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data

   https://doi.org/10.1186/s12864-020-06938-8  

   Dadaneh, Siamak Zamani; de Figueiredo, Paul; Sze, Sing-Hoi; Zhou, Mingyuan; Qian, Xiaoning (September 2020, BMC Genomics)

   null (Ed.)

   Abstract Background Single-cell RNA sequencing (scRNA-seq) is a powerful profiling technique at the single-cell resolution. Appropriate analysis of scRNA-seq data can characterize molecular heterogeneity and shed light into the underlying cellular process to better understand development and disease mechanisms. The unique analytic challenge is to appropriately model highly over-dispersed scRNA-seq count data with prevalent dropouts (zero counts), making zero-inflated dimensionality reduction techniques popular for scRNA-seq data analyses. Employing zero-inflated distributions, however, may place extra emphasis on zero counts, leading to potential bias when identifying the latent structure of the data. Results In this paper, we propose a fully generative hierarchical gamma-negative binomial (hGNB) model of scRNA-seq data, obviating the need for explicitly modeling zero inflation. At the same time, hGNB can naturally account for covariate effects at both the gene and cell levels to identify complex latent representations of scRNA-seq data, without the need for commonly adopted pre-processing steps such as normalization. Efficient Bayesian model inference is derived by exploiting conditional conjugacy via novel data augmentation techniques. Conclusion Experimental results on both simulated data and several real-world scRNA-seq datasets suggest that hGNB is a powerful tool for cell cluster discovery as well as cell lineage inference. 

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