title: Analyzing Gensini Score as a Semi-Continuous Outcome.
abstract: Background:Investigators frequently encounter continuous outcomes with plenty of values clumped at zero called semi-continuous outcomes. The Gensini score, one of the most widely used scoring systems for expressing coronary angiographic results, is of this type. The aim of this study was to apply two statistical approaches based on the categorization and original scale of the Gensini score to simultaneously assess the association between covariates and the presence and severity of coronary artery disease (CAD).Methods:We considered the data on 1594 individuals admitted to Tehran Heart Center with CAD symptoms from July 2004 to February 2008. The participants' baseline demographic and clinical characteristics were collected, and their coronary angiographic results were expressed through the Gensini score. The generalized ordinal threshold and two-part models were applied for the statistical analyses.Results:Totally, 320 (20.1%) individuals had a Gensini score of zero. The results of neither the two-part model nor the generalized ordinal threshold model showed a significant association between Factor V Leiden and the occurrence of CAD. However, based on the two-part model, Factor V Leiden was associated with the severity of CAD, such that the Gensini score increased by moving from a wild genotype to a heterozygote (β = 0.44; 95% CI: 0.20-0.69 in logarithm scale) or a homozygote mutant (β = 0.70; 95% CI: 0.28- 1.12 in logarithm scale). The proportional odds assumption was not met in our data ([Formula: see text]= 54.26; p value < 0.001); however, a trend toward severe CAD was also observed at each category of the Gensini score using the generalized ordinal threshold model.Conclusion:We conclude that besides loss of information by sorting a semi-continuous outcome, violation from the proportional odds assumption complicates the final decision, especially for clinicians. Therefore, more straightforward models such as the two-part model should receive more attention while analyzing such outcomes.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Homa
authors.initials: H
authors.lastname: Kashani
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Hojjat
authors.initials: H
authors.lastname: Zeraati
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Kazem
authors.initials: K
authors.lastname: Mohammad
authors.affiliation: Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Hamidreza
authors.initials: H
authors.lastname: Goodarzynejad
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Mahmood
authors.initials: M
authors.lastname: Mahmoudi
authors.affiliation: Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Saeed
authors.initials: S
authors.lastname: Sadeghian
authors.affiliation: Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Mohammadali
authors.initials: M
authors.lastname: Boroumand
keywords: Coronary angiography; Coronary artery disease; Data interpretation, statistical