A mixed effects model for multivariate ordinal response data including correlated discrete failure times with ordinal responses
Related Experiment Video
Updated: Oct 9, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
1Center for Biostatistics and Epidemiology, Hershey Medical Center, Pennsylvania State University, 17033, USA.
Abstract:
The mixed effects model for binary responses due to Conaway (1990, A Random Effects Model for Binary Data) is extended to accommodate ordinal responses in general and discrete time survival data with ordinal responses in particular. Given a multinomial likelihood, cumulative complementary log-log link function, and log-gamma random effects distribution, the resulting marginal likelihood has a closed form. As a result, a Newton-Raphson estimation procedure is feasible without resorting to numerical, approximation-based, or Monte Carlo integration techniques. The parameters in the model have a proportional hazards interpretation in terms of multivariate discrete time data with ordinal responses. Using data from a psychological example, the proposed method is compared with other mixed effects approaches as well as population-averaged models.
Related Concept Videos
Ordinal Level of Measurement
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...
Friedman Two-way Analysis of Variance by Ranks
Assumptions of Survival Analysis
Comparing the Survival Analysis of Two or More Groups
Survival Tree
Building a Survival Tree
Constructing a survival tree begins...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

