When considering variable is discrete, the regression coefficient estimated for it is then the average difference in the dependent variable between the category of the independent variable representing the reference group and the other categories.
Relationships of baseline characteristics with incidence of LEA were explored using a proportional hazards approach with discrete linear regression modeling.
In this article, we propose a discrete mixture regression model that synergizes with potential heterogeneity in time-to-event data.
GP models were developed to predict the probability of ICU discharge the day after surgery (classification task), and to predict the day of ICU discharge as a discrete variable (regression task).
Five studies utilised Markov models, with others using discrete event, regression-based simulation, and decision tree approaches.
This paper draws upon a series of discrete choice and regression models to analyze drivers' response to a set of such hypothetical policies in Jakarta, Indonesia.
For discrete variables, logistic regression analyses were used, and adjustments were made for differences in background factors, using SC as the reference.
Due to this over-dispersion of data and the fact that the data are discrete, negative binomial regression methods were used to evaluate predictors of health care utilization.
For time-to-DN analysis, we tested the association between EPO polymorphism and time to the development of DN using discrete time Cox regression in WESDR and DCCT/EDIC before combining the results in a fixed-effect meta-analysis.
Separate discrete conditional logistic regression models were estimated for each of the individual process of care measures, and for 30-day mortality [ 16] The PSI score and race were entered as independent variables into the models.
