The partly Aalen's model for recurrent event data with a dependent terminal event
Chyong-Mei Chen1,2, Pao-Sheng Shen3, Ya-Wen Chuang4
1Department of Statistics and Informatics Science, College of Science, Providence University, Taichung City, 43301, Taiwan.
Statistics in Medicine
|August 13, 2015
Summary
This study introduces a new statistical model for analyzing recurrent events and terminal events in biomedical research. The model accounts for correlations between these events, offering insights for clinical decision-making.
Area of Science:
- Biostatistics
- Survival Analysis
- Longitudinal Data Analysis
Background:
- Recurrent event data are common in biomedical studies, often complicated by a terminal event.
- The correlation between recurrent and terminal events is frequently nonignorable and impacts analysis.
Purpose of the Study:
- To propose a joint statistical model for recurrent and terminal events.
- To analyze the temporal influence of covariates on recurrent event rates.
- To provide tools for physicians in clinical decision-making.
Main Methods:
- A partly Aalen's additive model with multiplicative frailty for recurrent events.
- A Cox frailty model for the terminal event time.
- A shared gamma frailty to link recurrent and terminal events.
- An estimating equation approach for parameter estimation.
Main Results:
- The proposed joint model effectively captures the temporal covariate effects.
- The estimating equation approach provides consistent parameter estimates.
- Simulation studies confirm the model's practical applicability.
Conclusions:
- The developed joint model is a valuable tool for analyzing complex event data in biomedical research.
- This approach enhances understanding of recurrent events in the presence of terminal events.
- The method is illustrated with a real-world peritonitis cohort data set.
Related Concept Videos
01:19Survival Tree
497
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
497
01:29Contingency Table
5.0K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
5.0K
01:09Censoring Survival Data
654
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...
654
01:15Assumptions of Survival Analysis
489
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.
489
01:08Determination of Expected Frequency
2.7K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.7K
01:14Multicompartment Models: Overview
703
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
703