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...
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,...
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...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

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 interest.
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.

You might also read

Related Articles

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

Sort by
Same author

Lipoprotein(a) lipidome and chronic kidney disease: Enrichment in triacylglycerols and diacylglycerols.

Journal of clinical lipidology·2026
Same author

Authors' Response to Comments by Di Tanna et al.

Statistics in medicine·2026
Same author

Lipoprotein(a) lipidome and diet: responses to reducing saturated fat intake in African Americans in a randomized trial.

Journal of lipid research·2026
Same author

Prevalence of undiagnosed hypertension and risk assessment using a validated survey in community-based screening in Amman, Jordan.

PloS one·2026
Same author

Meta-Analysis of Cost-Effectiveness.

Statistics in medicine·2026
Same author

Dietary and Physical Activity Habits of Potential Living Kidney Donors.

Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation·2026
Same journal

Correction.

Journal of biopharmaceutical statistics·2026
Same journal

Leveraging external controls in clinical trials: estimands, estimation, assumptions.

Journal of biopharmaceutical statistics·2026
Same journal

Special issue of nonclinical statistics in regulatory applications guest editors' notes.

Journal of biopharmaceutical statistics·2026
Same journal

Comparison of flexible parametric modeling and nonparametric methods to estimate restricted mean survival time: A simulation study.

Journal of biopharmaceutical statistics·2026
Same journal

Simulated treatment comparisons with jackknife pseudo values for estimating population-adjusted marginal treatment effects.

Journal of biopharmaceutical statistics·2026
Same journal

Sample sizes for randomized controlled trials utilizing Bayesian response adaptive randomization for continuous outcomes.

Journal of biopharmaceutical statistics·2026
See all related articles

Related Experiment Video

Updated: May 25, 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

Average cost-effectiveness ratio with censored data.

Heejung Bang1, Hongwei Zhao

  • 1Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, California, USA. hbang@ucdavis.edu

Journal of Biopharmaceutical Statistics
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces new statistical methods for calculating the average cost-effectiveness ratio (ACER) with censored data. These methods improve cost-effectiveness analysis by providing reliable confidence intervals for ACER from prospective studies.

More Related Videos

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Last Updated: May 25, 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

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Health Economics
  • Biostatistics
  • Medical Decision Making

Background:

  • Cost-effectiveness analysis (CEA) is crucial for healthcare decision-making.
  • The incremental cost-effectiveness ratio (ICER) is widely used, but the average cost-effectiveness ratio (ACER) is also important for single interventions.
  • Statistical methods for ACER inference, especially with censored data, are limited.

Purpose of the Study:

  • To propose and evaluate statistical methods for constructing confidence intervals for the ACER.
  • To address the challenge of censored data in cost and effectiveness measurements.
  • To provide reliable tools for cost-effectiveness analysis.

Main Methods:

  • Development of statistical methods including Fieller, Taylor, and bootstrap approaches.
  • Application of methods to censored cost and effectiveness data.
  • Simulation studies to assess method performance characteristics.

Main Results:

  • The proposed methods provide valid confidence intervals for ACER from censored data.
  • Performance characteristics of Fieller, Taylor, and bootstrap methods were evaluated.
  • The study offers practical solutions for analyzing ACER in prospective studies.

Conclusions:

  • The developed statistical methods enhance the analysis of ACER in the presence of censoring.
  • These methods are valuable for accurate cost-effectiveness evaluations in healthcare.
  • The research contributes to the statistical inference of ACER for censored data.