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

Censoring Survival Data01:09

Censoring Survival Data

539
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...
539
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

27.6K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
27.6K
Confidence Intervals01:21

Confidence Intervals

10.6K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
10.6K
Normal Stress01:19

Normal Stress

1.4K
Normal stress is a type of stress that occurs when forces act perpendicular, or normal, to a material's cross-sectional area. This stress often arises in structures when subjected to axial loading, which is the application of force along the axis of an object. A practical example of this can be found in bridge truss members.
When a rod is under axial loading, the internal forces and corresponding stress are normal to the plane of the section, so it is termed normal stress. It's important to...
1.4K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

11.6K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
11.6K

You might also read

Related Articles

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

Sort by
Same author

Ginsenoside Rb2 modulates the skin barrier by targeting Src to regulate PI3K/Akt signaling in HaCaT cells.

Journal of ginseng research·2026
Same author

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same author

Two new meroterpenoids with 6/10/5/3 tetracyclic skeleton from fungus <i>Ampulloclitocybe clavipes</i>.

Natural product research·2026
Same author

The role of cellular senescence in immune-metabolic features and prognosis of ovarian cancer: an integrated analysis based on single-cell sequencing and multi-omics data.

GeroScience·2026
Same author

Near-infrared photovoltaic gating enables polarity-reconfigurable WSe<sub>2</sub> phototransistors for in-sensor computing.

Nanoscale·2026
Same author

Gallic acid-driven core-shell nanovehicles enable intestinal adhesion and adipose retention for enhanced oral bioavailability of cholecalciferol.

Drug delivery·2026

Related Experiment Video

Updated: Jan 28, 2026

Frailty Assessment in an Aging Mouse Model
06:58

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

507

Normal frailty probit model for clustered interval-censored failure time data.

Haifeng Wu1, Lianming Wang2

  • 1Central Pacific Bank, Honolulu, Hawaii.

Biometrical Journal. Biometrische Zeitschrift
|March 7, 2019
PubMed
Summary

This study introduces a new statistical model for clustered, interval-censored data common in biomedical research. The model effectively analyzes covariate effects on failure times while accounting for within-cluster correlations.

Keywords:
clustered datainterval-censored datamonotone splinesprobit modelsemiparametric regression

More Related Videos

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
05:53

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

17.0K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.9K

Related Experiment Videos

Last Updated: Jan 28, 2026

Frailty Assessment in an Aging Mouse Model
06:58

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

507
Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
05:53

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

17.0K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.9K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Clustered interval-censored data are prevalent in biomedical studies.
  • Intracluster dependence complicates failure time analysis.
  • Existing models may not adequately address these complexities.

Purpose of the Study:

  • To propose a novel semiparametric frailty probit regression model.
  • To analyze covariate effects on failure times in clustered interval-censored data.
  • To account for intracluster dependence.

Main Methods:

  • Developed a normal frailty probit model.
  • Employed a fully Bayesian estimation approach using monotone splines.
  • Utilized data augmentation with normal latent variables and a Gibbs sampler.

Main Results:

  • The model provides interpretable regression parameters for conditional and marginal covariate effects.
  • Intracluster association is summarized by two nonparametric measures.
  • The proposed method demonstrates strong performance in parameter and association estimation.
  • The method is robust to misspecification of the frailty distribution.

Conclusions:

  • The new semiparametric frailty probit model effectively handles clustered interval-censored data.
  • The Bayesian approach with Gibbs sampling is practical for implementation.
  • The model offers a robust framework for analyzing complex biomedical data.