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

Odds Ratio01:09

Odds Ratio

2.0K
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
2.0K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.8K
Censoring Survival Data01:09

Censoring Survival Data

622
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...
622
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

9.4K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
9.4K
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

You might also read

Related Articles

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

Sort by
Same author

The Impacts of HIV-Related Service Interruptions During the COVID-19 Pandemic: Protocol of a Mixed Methodology Longitudinal Study.

AIDS and behavior·2023
Same author

Spatial Distribution of Parvalbumin-Positive Fibers in the Mouse Brain and Their Alterations in Mouse Models of Temporal Lobe Epilepsy and Parkinson's Disease.

Neuroscience bulletin·2023
Same author

The incidence and dynamic risk factors of chronic kidney disease among people with HIV.

AIDS (London, England)·2023
Same author

Association between clusters of antibodies against periodontal microorganisms and Alzheimer disease mortality: Evidence from a nationally representative survey in the USA.

Journal of periodontology·2023
Same author

CC Chemokine 2 Promotes Ovarian Cancer Progression through the MEK/ERK/MAP3K19 Signaling Pathway.

International journal of molecular sciences·2023
Same author

Hybridized Triboelectric-Electromagnetic Aeolian Vibration Generator as a Self-Powered System for Efficient Vibration Energy Harvesting and Vibration Online Monitoring of Transmission Lines.

ACS applied materials & interfaces·2023
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Mar 9, 2026

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

An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.

Jie Zhou1, Jiajia Zhang1, Wenbin Lu2

  • 1Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, U.S.A.

Statistics in Medicine
|December 23, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient Expectation Maximization algorithm for generalized odds-rate models with interval-censored data. The gamma-Poisson data augmentation method simplifies fitting these complex regression models.

Keywords:
EM algorithmdata augmentationgeneralized odds-rate modelsinterval censoring

More Related Videos

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

2.7K
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

706

Related Experiment Videos

Last Updated: Mar 9, 2026

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
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

2.7K
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

706

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Generalized odds-rate models are versatile semiparametric regression tools.
  • Fitting these models with interval-censored data is computationally challenging due to complex likelihood functions.
  • Existing methods for interval-censored data in generalized odds-rate models are limited.

Purpose of the Study:

  • To develop an efficient and easy-to-implement method for fitting generalized odds-rate models to interval-censored data.
  • To address the computational challenges associated with maximizing complex likelihood functions in this context.
  • To provide a practical tool for researchers analyzing interval-censored survival data.

Main Methods:

  • A gamma-Poisson data augmentation approach was employed.
  • An Expectation Maximization (EM) algorithm was developed based on this augmentation.
  • The method was validated using comprehensive simulation studies and real-world datasets.

Main Results:

  • The proposed EM algorithm effectively fits generalized odds-rate models to interval-censored data.
  • The method demonstrates computational efficiency and ease of implementation.
  • Successful application to breast cancer and hemophilia datasets highlights practical utility.

Conclusions:

  • The gamma-Poisson data augmentation EM algorithm offers a robust solution for generalized odds-rate models with interval-censored data.
  • The developed R package 'ICGOR' facilitates practical application of the method.
  • This approach enhances the analysis of survival data where exact event times are unknown.