Related Experiment Video
Updated: Oct 28, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Joint linear and nonlinear mixed effects model with random change points for left-censored longitudinal data:
Binod Manandhar1, Hongbin Zhang2
1Department of Mathematical Sciences, Clark Atlanta University, Atlanta, Georgia, USA.
Abstract:
A change-point model is useful for longitudinal data to infer the time to an event that induces a change of trajectory within the study period. We present a random change-point model that fits the linear and non-linear mixed effects for pre- and post-change-points, respectively. We address the left-censored observations in the model. The stochastic approximation expectation maximization (SAEM) with the Metropolis Hastings sampler is used to fit the random change-point non-linear mixed effects model. We apply our method to the viral load (VL) longitudinal data reported to the HIV surveillance registry in New York City. We evaluate the model with a data simulation.
Related Concept Videos
Statistical Methods for Analyzing Epidemiological Data
Censoring Survival Data
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...
Assumptions of Survival Analysis
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time until a...
Comparing the Survival Analysis of Two or More Groups
