Related Experiment Video
Updated: Jun 17, 2026
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Semiparametric proportional means model for marker data contingent on recurrent event
Jianwen Cai1, Donglin Zeng, Wenqin Pan
1Department of Biostatistics, University of North Carolina at Chapel Hill, Campus Box 7420, Chapel Hill, NC, 27599-7420, USA. cai@bios.unc.edu
Lifetime Data Analysis
|December 17, 2009
Summary
This study introduces a new statistical model for analyzing biomedical data where markers are only measured upon recurrent events, like hospitalizations. The method effectively models both the event occurrence and the marker data, showing reliable estimation properties.
Area of Science:
- Biostatistics
- Epidemiology
- Health Economics
Background:
- Biomedical studies often involve recurrent events (e.g., hospitalizations) where associated markers (e.g., medical costs) are only recorded upon event occurrence.
- This creates challenges in statistical modeling due to the contingent nature of marker data on recurrent events.
Purpose of the Study:
- To develop and validate a statistical framework for analyzing marker data that are contingent on recurrent events.
- To simultaneously model recurrent event processes and associated marker data using observed covariates.
Main Methods:
- A proportional means model was proposed for marker data conditional on recurrent events.
- A marginal rate model was used for the recurrent event process.
- Estimating equations were derived for parameter estimation, with asymptotic normality established.
Main Results:
- The proposed estimators demonstrated good finite-sample properties in simulation studies.
- The methodology was successfully applied to real-world data from the Vitamin A Community Trial.
Conclusions:
- The developed statistical models provide a robust approach for analyzing complex biomedical data with event-contingent markers.
- This method enhances the understanding of relationships between recurrent events and their associated markers in health research.
Related Concept Videos
01:09Censoring Survival Data
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:15Assumptions of Survival Analysis
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
01:14Parametric Survival Analysis: Weibull and Exponential Methods
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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...
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...
01:24Kaplan-Meier Approach
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...