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

Assumptions of Survival Analysis01:15

Assumptions 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
Censoring Survival Data01:09

Censoring 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
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

1.2K
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
1.2K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

742
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,...
742
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

711
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...
711

You might also read

Related Articles

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

Sort by
Same author

Long-chain PUFA and painful temporomandibular disorder in the Hispanic Community Health Study/Study of Latinos.

Public health nutrition·2025
Same author

A mouse model of chronic primary pain that integrates clinically relevant genetic vulnerability, stress, and minor injury.

Science translational medicine·2024
Same author

Effect of bottled fluoridated water to prevent dental caries in primary teeth: study protocol for a phase 2 parallel-group 3.5-year randomized controlled clinical trial (waterBEST).

Trials·2024
Same author

Effect of bottled fluoridated water to prevent dental caries in primary teeth: study protocol for a phase 2 parallel group 3.5-year randomized controlled clinical trial (waterBEST).

Research square·2024
Same author

Diagnostic Accuracy of a Temporomandibular Disorder Pain Screener in Patients Seeking Endodontic Treatment for Tooth Pain.

Journal of endodontics·2024
Same author

Whole-genome methylation profiling reveals regions associated with painful temporomandibular disorders and active recovery processes.

Pain·2023
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Apr 6, 2026

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

2.8K

Parameter estimation in Cox models with missing failure indicators and the OPPERA study.

Naomi C Brownstein1,2, Jianwen Cai3, Gary D Slade4

  • 1Ion Cyclotron Resonance Facility, National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, U.S.A.

Statistics in Medicine
|August 6, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method to accurately estimate disease incidence in large cohort studies when gold-standard diagnostic examinations are missing. The approach uses multiple imputation to handle missing data, improving reliability for temporomandibular disorder (TMD) research.

Keywords:
Cox regressionPoisson regressionmissing datamultiple imputationsurvival analysis

More Related Videos

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

11.0K
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

775

Related Experiment Videos

Last Updated: Apr 6, 2026

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

2.8K
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

11.0K
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

775

Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Research Methodology

Background:

  • Prospective cohort studies often face challenges with prohibitively expensive diagnostic procedures.
  • Screening questionnaires are used, but missing gold-standard examinations due to participant attrition lead to missing failure indicators in survival analysis.
  • The Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study highlights the need for methods to address missing data in temporomandibular disorder (TMD) research.

Purpose of the Study:

  • To propose and validate a novel statistical method for parameter estimation in survival models with missing failure indicators.
  • To accurately estimate disease incidence and regression coefficients in the presence of incomplete diagnostic data.
  • To provide a robust solution for large-scale epidemiological studies, exemplified by TMD research.

Main Methods:

  • Utilizing logistic regression to estimate the probability of being an incident case for participants with missing gold-standard examinations.
  • Employing multiple imputation techniques to generate plausible case status for missing data.
  • Integrating imputed data with observed data in Cox proportional hazard and Poisson regression models for analysis.

Main Results:

  • The proposed multiple imputation method effectively handles missing failure indicators in survival analysis.
  • Simulations demonstrate that the method performs comparably to or better than existing approaches.
  • The method successfully applied to real-world data from the OPPERA study.

Conclusions:

  • The developed method offers a statistically sound approach to address missing failure indicators in prospective cohort studies.
  • This technique enhances the accuracy of incidence rate and regression coefficient estimation, particularly in large-scale epidemiological research.
  • The findings have significant implications for the analysis of data from studies like the OPPERA, improving the understanding of TMD and other conditions.