Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric 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...

Kaplan-Meier Approach

Kaplan-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,...

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

Introduction To Survival Analysis

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model 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...

Hazard Rate

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Agricultural pesticide use and Alzheimer's disease dementia prevalence across US counties in a mixed supervised-unsupervised analysis.

Scientific reports·2026

Same author

Assessing the impact of neighborhood structures in Bayesian disease mapping.

Journal of applied statistics·2026

Same author

Bayesian multi-cell type models for the analysis of complex immune cell populations with application to ovarian cancer.

Briefings in bioinformatics·2026

Same author

Retrotransposon methylation profiles and survival in Black women with high-grade serous ovarian carcinoma.

Clinical epigenetics·2025

Same author

Cigarette smoking in relation to survival in Black women with ovarian cancer: Evidence from the African American Cancer Epidemiology Study (AACES).

Annals of epidemiology·2025

Same author

Comparative evaluation of spatiotemporal methods for effective dengue cluster detection with a case study of national surveillance data in Thailand.

Scientific reports·2024

Search research articles

Related Experiment Video

Updated: May 17, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

A Bayesian normal mixture accelerated failure time spatial model and its application to prostate cancer.

Songfeng Wang¹, Jiajia Zhang², Andrew B Lawson³

¹Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA songfeng@gmail.com.

Statistical Methods in Medical Research

|November 3, 2012

Summary

This study introduces a new spatial model to analyze prostate cancer trends in Louisiana. The model reveals significant geographical patterns and racial disparities in prostate cancer incidence among African Americans.

Keywords:

Accelerated failure time spatial model Log pseudo marginal likelihood conditional autoregressive model normal mixture

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: May 17, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Epidemiology
Biostatistics
Spatial Analysis

Background:

Prostate cancer is a leading cause of cancer death in US males, with higher incidence in African Americans.
Geographical factors may influence prostate cancer occurrence and progression, particularly in diverse populations.
Understanding spatial patterns and racial disparities is crucial for targeted public health interventions.

Purpose of the Study:

To investigate spatial effects and racial disparities in prostate cancer within Louisiana.
To propose and validate a flexible statistical model for analyzing complex epidemiological data.
To apply the model to real-world prostate cancer data to identify significant patterns.

Main Methods:

Development of a normal mixture accelerated failure time spatial model, accommodating multi-model error distributions.
Utilized a Bayesian approach for model estimation, compatible with WinBUGS software.
Validated the model's flexibility and performance through extensive simulations.

Main Results:

The proposed spatial model successfully identified potential geographical patterns of prostate cancer in Louisiana.
The analysis highlighted significant racial disparities in prostate cancer incidence and progression.
The model demonstrated flexibility in handling various parametric error distributions.

Conclusions:

The novel spatial model provides a robust framework for analyzing prostate cancer epidemiology.
Findings underscore the importance of considering geographical and racial factors in prostate cancer research and prevention.
The study provides valuable insights for public health strategies targeting prostate cancer in Louisiana.