ArticleJournal of applied statistics2018
Untangle the Structural and Random Zeros in Statistical Modelings.
Article in Journal of applied statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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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.
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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.
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Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Endometrial resection and ablation versus hysterectomy for heavy menstrual bleeding.The Cochrane database of systematic reviews · 2021Pooled it
- Progestogens or progestogen-releasing intrauterine systems for uterine fibroids (other than preoperative medical therapy).The Cochrane database of systematic reviews · 2020Pooled it
- Prediction Modeling With Many Correlated and Zero-Inflated Predictors: Assessing the Nonnegative Garrote Approach.Statistics in medicine · 2025Article
- A GEE-type approach to untangle structural and random zeros in predictors.Statistical methods in medical research · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Count data with structural zeros are common in public health applications. There are considerable researches focusing on zero-inflated models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) models for such zero-inflated count data when used as response variable. However, when such variables are used as predictors, the difference between structural and random zeros is often ignored and may result in biased estimates. One remedy is to include an indicator of the structural zero in the model as a predictor if observed. However, structural zeros are often not observed in practice, in which case no statistical method is available to address the bias issue. This paper is aimed to fill this methodological gap by developing parametric methods to model zero-inflated count data when used as predictors based on the maximum likelihood approach. The response variable can be any type of data including continuous, binary, count or even zero-inflated count responses. Simulation studies are performed to assess the numerical performance of this new approach when sample size is small to moderate. A real data example is also used to demonstrate the application of this method.
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Registered trials
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