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

628
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...
628
Survival Tree01:19

Survival Tree

463
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
463
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

680
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
680
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

473
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.
473
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
Prediction Intervals01:03

Prediction Intervals

3.5K
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. 
3.5K

You might also read

Related Articles

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

Sort by
Same author

Resistant dextrin promotes beneficial fecal bacteria in high and low fiber diet populations: a randomized, double-blinded, controlled pilot study.

Frontiers in nutrition·2026
Same author

Development of a fermented quinoa beverage with autochthonous lactic acid bacteria.

Frontiers in microbiology·2026
Same author

Inter- and intra-observer agreement in ultrasound diagnosis of steatotic liver disease: implications for screening in resource-limited settings.

Scientific reports·2025
Same author

The Effect of Probiotics on Health in Pregnancy and Infants: A Randomized, Double-Blind, Placebo-Controlled Trial.

Nutrients·2025
Same author

A bootstrap-assisted methodology for the estimation of prediction uncertainty in multilayer perceptron-based calibration.

Analytica chimica acta·2025
Same author

Characterization of mammographic markers of inflammatory breast cancer (IBC).

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2024
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Mar 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Sufficient dimension reduction for censored predictors.

Diego Tomassi1, Liliana Forzani1, Efstathia Bura2

  • 1Facultad de Ingenieria Quimica, Universidad Nacional del Litoral, Researcher of CONICET, Santa Fe, Argentina.

Biometrics
|August 11, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces new statistical methods to analyze censored inflammatory markers for lung cancer risk. Accounting for censoring improves efficiency and prediction accuracy in marker analysis.

Keywords:
Informative missingnessLimits of detectionMissing dataPenalized likelihoodShrinkage

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
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Related Experiment Videos

Last Updated: Mar 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
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
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Cancer Research

Background:

  • Lung cancer risk is associated with inflammatory markers.
  • Analyzing multiple correlated markers with detection limits presents statistical challenges.
  • Existing methods may not fully account for censored data in marker analysis.

Purpose of the Study:

  • To develop and evaluate likelihood-based sufficient dimension reduction (SDR) methods for analyzing censored, correlated inflammatory markers.
  • To extend SDR by directly accommodating censored predictors and incorporating variable selection.
  • To improve the efficiency and prediction accuracy of lung cancer risk models using marker data.

Main Methods:

  • Utilized likelihood-based sufficient dimension reduction (SDR) frameworks.
  • Developed methods to directly incorporate censored predictors into the likelihood function.
  • Integrated variable selection into the SDR approach.
  • Applied methods to a lung cancer study and simulations.

Main Results:

  • Identified linear combinations of markers that capture all relevant information for outcome prediction, accounting for censoring.
  • Demonstrated that explicitly handling censoring in SDR methods enhances efficiency and prediction accuracy.
  • Showed proposed methods outperform multiple imputation combined with standard SDR.

Conclusions:

  • Likelihood-based SDR methods effectively analyze censored, correlated inflammatory markers for lung cancer risk.
  • Directly accounting for censoring in SDR models is crucial for optimal performance.
  • The developed methods offer robust tools for biomarker analysis in complex datasets.