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

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

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

You might also read

Related Articles

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

Sort by
Same author

Impact of Trait Measurement Error on Quantitative Genetic Analysis of Computer Vision-Derived Traits.

Genes·2026
Same author

Evaluating the Impact of Scanning Factors on Ultrasound Imaging for Predicting Semen Quality in Boars.

Animals : an open access journal from MDPI·2026
Same author

Effect of early to mid-gestation heat stress exposure on mammary development and milk traits in F1 gilts divergently selected for thermotolerance.

Journal of animal science·2026
Same author

Combining Serum Prostate Health Index With Urinary PCA3 and TMPRSS2:ERG RNA Testing Improves Detection of Clinically Significant Prostate Cancer.

JU open plus·2026
Same author

Association of Pre-Fontan Hemodynamics With Long-Term Outcomes After Fontan Palliation: A Study From the Pediatric Cardiac Care Consortium.

Circulation·2026
Same author

Genetic background of behavior traits in lactating sows under heat-stress conditions and their relationship with heat tolerance and maternal performance traits.

Frontiers in genetics·2025
Same journal

Hepatitis C Virus Cascade of Care in Florida Emergency Departments.

Medical care·2026
Same journal

Association of Neighborhood Socioeconomic Disadvantage and Uptake of Diabetes Prevention Interventions.

Medical care·2026
Same journal

Machine Learning for Evaluating the Heterogeneous Effects of Intensive In-Hospital Rehabilitation During the Postacute Phase After Hip Fracture Surgery on Activities of Daily Living.

Medical care·2026
Same journal

Hospital-Physician Integration and Differences in the Use of Orthopedic Care Across Race and Ethnicity.

Medical care·2026
Same journal

Temporal Misalignment and Selection Bias in "Burn Pit Smoke Exposure and Sleep Apnea in US Veterans.

Medical care·2026
Same journal

The Impact of an Oncology Hospital at Home Program on Health Care Costs.

Medical care·2026
See all related articles

Related Experiment Video

Updated: Jun 22, 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

Cost analysis with censored data.

Yijian Huang1

  • 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA. yhuang5@emory.edu

Medical Care
|June 19, 2009
PubMed
Summary
This summary is machine-generated.

Statistical analysis of censored cost data in medical research presents challenges. Recent advances address issues like dependent censoring for accurate economic evaluations.

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

Related Experiment Videos

Last Updated: Jun 22, 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

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

Area of Science:

  • Health Economics
  • Biostatistics
  • Medical Research Methodology

Background:

  • Economic evaluation is crucial for medical interventions alongside safety and efficacy.
  • Censored cost data in medical studies poses significant statistical challenges.
  • Advances in statistical methods are needed to address these data complexities.

Purpose of the Study:

  • To identify and describe statistical issues arising from censored cost data in medical research.
  • To review recent statistical developments for analyzing censored medical costs.
  • To discuss the applicability and limitations of available methods for time-restricted and lifetime cost analyses.

Main Methods:

  • Review of statistical issues including induced dependent censoring and limited identifiability.
  • Discussion of statistical methods for time-restricted medical costs.
  • Examination of methods for lifetime medical costs analyzed jointly with survival time.

Main Results:

  • Key statistical challenges in censored cost data have been identified.
  • Recent statistical developments offer solutions for analyzing medical costs.
  • Methods exist for both time-restricted and lifetime cost evaluations.

Conclusions:

  • Addressing censored cost data is vital for robust economic evaluations in healthcare.
  • Statistical methodologies are evolving to handle complex cost data in clinical research.
  • Understanding the limitations of current methods is essential for practical application.