Evidence map›Paper›PMID 25317011›Full record

ArticleShanghai archives of psychiatry2014

Structural zeroes and zero-inflated models.

Hua He, Wan Tang, Wenjuan Wang, Paul Crits-Christoph

Abstract read
In one paragraph

Article in Shanghai archives of psychiatry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Modelling Count Data in Psychological Research: An Applied Tutorial.International journal of psychology : Journal international de psychologie · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Binary Classification with Imbalanced Data.Entropy (Basel, Switzerland) · 2023
    Article
  16. Article
  17. Model-Based Causal Discovery for Zero-Inflated Count Data.Journal of machine learning research : JMLR · 2023
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hua HeDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA ; Veterans Integrated Service Network, Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA ; Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA.
Wan TangDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
Wenjuan WangDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
Paul Crits-ChristophDepartment of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.

Funding

Analysis of Count Data with Structural Zeros: CTN0018 and CTN0019R33DA027521 · NIDA · UNIVERSITY OF ROCHESTER · PI TANG, WAN · 2010 to 2012
$1.0M
Moving beyond description: Statistical and casual inference for social media dataR01GM108337 · NIGMS · UNIVERSITY OF ROCHESTER · PI HE, HUA, WHITE, ANN MARIE · 2013 to 2015
$617k
Analysis of Count Data with Structural Zeros: CTN0018 and CTN0019R21DA027521 · NIDA · UNIVERSITY OF ROCHESTER · PI TANG, WAN · 2009 to 2009
$323k
NIDA NIH HHS R21 DA027521NIDA NIH HHS R33 DA027521NIGMS NIH HHS R01 GM108337
6 · The paper itself

Abstract

summaryIn psychosocial and behavioral studies count outcomes recording the frequencies of the occurrence of some health or behavior outcomes (such as the number of unprotected sexual behaviors during a period of time) often contain a preponderance of zeroes because of the presence of 'structural zeroes' that occur when some subjects are not at risk for the behavior of interest. Unlike random zeroes (responses that can be greater than zero, but are zero due to sampling variability), structural zeroes are usually very different, both statistically and clinically. False interpretations of results and study findings may result if differences in the two types of zeroes are ignored. However, in practice, the status of the structural zeroes is often not observed and this latent nature complicates the data analysis. In this article, we focus on one model, the zero-inflated Poisson (ZIP) regression model that is commonly used to address zero-inflated data. We first give a brief overview of the issues of structural zeroes and the ZIP model. We then given an illustration of ZIP with data from a study on HIV-risk sexual behaviors among adolescent girls. Sample codes in SAS and Stata are also included to help perform and explain ZIP analyses.

Indexed as

count responserandom zeroesstructural zeroeszero-inflated models

Identifiers

PMID25317011
PMCPMC4194007

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

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.