ArticleGastroenterology and hepatology from bed to bench2013
Statistical count models for prognosis the risk factors of hepatitis C.
Article in Gastroenterology and hepatology from bed to bench, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Exploring the Relationship Between Surgical Capacity and Output in Ghana: Current Capacity Assessments May Not Tell the Whole Story.World journal of surgery · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThe aim of this study was to compare alternatives methods for analysis of zero inflated count data and compare them with simple count models that are used by researchers frequently for such zero inflated data.
backgroundAnalysis of viral load and risk factors could predict likelihood of achieving sustain virological response (SVR). This information is useful to protect a person from acquiring Hepatitis C virus (HCV) infection. The distribution of viral load contains a large proportion of excess zeros (HCV-RNA under 100), that can lead to over-dispersion. PATIENTS AND
methodsThis data belonged to a longitudinal study conducted between 2005 and 2010. The response variable was the viral load of each HCV patient 6 months after the end of treatment. Poisson regression (PR), negative binomial regression (NB), zero inflated Poisson regression (ZIP) and zero inflated negative binomial regression (ZINB) models were carried out to the data respectively. Log likelihood, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to compare performance of the models.
resultsAccording to all criterions, ZINB was the best model for analyzing this data. Age, having risk factors genotype 3 and protocol of treatment were being significant.
conclusionZero inflated negative binomial regression models fit the viral load data better than the Poisson, negative binomial and zero inflated Poisson models.
Indexed as
Identifiers
24834244PMC4017489What 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.