Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

569
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...
569
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

507
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
507
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

269
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
269
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

377
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
377
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

182
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.
182
Study Design in Statistics01:15

Study Design in Statistics

8.4K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
8.4K

You might also read

Related Articles

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

Sort by
Same author

Information-Based Composite Likelihood Method for Hybrid Meta-Analysis Integrating Individual Participant Data and Aggregated Data.

Statistics in medicine·2026
Same author

Desirability of outcome ranking (DOOR) analysis for multivariate survival outcomes with application to ACTT-1 trial.

Clinical trials (London, England)·2025
Same author

A patient-centric paradigm and tool for clinical research: the DOOR is open.

Antimicrobial agents and chemotherapy·2025
Same author

Multiplicity Control in Oncology Clinical Trials With a Binary Surrogate Endpoint-Based Drop-The-Losers Design.

Statistics in medicine·2025
Same author

A Bayesian approach towards the identification of latent subgroups.

Statistical methods in medical research·2025
Same author

Impact of the COVID-19 Pandemic on Antibiotic Resistant Infection Burden in U.S. Hospitals : Retrospective Cohort Study of Trends and Risk Factors.

Annals of internal medicine·2025

Related Experiment Video

Updated: Sep 2, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Joint semiparametric models for case-cohort designs.

Weibin Zhong1, Guoqing Diao2

  • 1Global Biometrics & Data Sciences, Bristol Myers Squibb, Berkeley Heights, New Jersey, USA.

Biometrics
|August 2, 2022
PubMed
Summary

This study introduces a novel joint semiparametric modeling approach for two-phase study data, efficiently analyzing survival outcomes and expensive exposures. The proposed methods offer robust and effective regression analysis for complex epidemiological data.

Keywords:
density ratio modelnonparametric likelihoodsemiparametric transformation model

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Related Experiment Videos

Last Updated: Sep 2, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Two-phase study designs, including case-cohort and nested case-control, are cost-effective for collecting data on expensive exposures.
  • Efficiently utilizing all available data in regression analyses of two-phase studies presents a significant statistical challenge.
  • Existing methods may not fully leverage the information gathered across both phases of the study.

Purpose of the Study:

  • To propose a novel joint semiparametric modeling framework for analyzing two-phase study data.
  • To develop efficient likelihood-based estimation and inference procedures for this modeling approach.
  • To assess the performance and robustness of the proposed methods through simulations and a real-world application.

Main Methods:

  • Proposed a joint semiparametric modeling strategy combining survival outcomes with expensive exposures.
  • Utilized semiparametric transformation models for survival outcomes (including proportional hazards and odds models).
  • Employed a flexible semiparametric density ratio model for multivariate mixed-type expensive exposures.
  • Developed likelihood-based estimation and inference, establishing large sample properties of estimators.

Main Results:

  • Extensive numerical studies demonstrated that the proposed methods perform well in practical settings.
  • The methods showed reasonable robustness against various model mis-specifications.
  • The approach was successfully applied to data from the National Wilms Tumor Study.

Conclusions:

  • The proposed joint semiparametric modeling provides an efficient and robust approach for analyzing two-phase study data.
  • This method effectively integrates information on survival outcomes and expensive exposures, enhancing analytical power.
  • The findings have significant implications for statistical analysis in epidemiological research utilizing complex sampling designs.