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

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

174
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...
174
Censoring Survival Data01:09

Censoring Survival Data

69
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...
69
Actuarial Approach01:20

Actuarial Approach

64
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,...
64
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

104
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.
104
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

158
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...
158
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

107
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,...
107

You might also read

Related Articles

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

Sort by
Same author

The effect of nasal olfactory stimulation on apnea of prematurity: a randomized cross-over trial.

Pediatric research·2026
Same author

The FICUS cluster randomized controlled trial of a family support intervention in adult intensive care units: mental health and family functioning outcomes.

Intensive care medicine·2026
Same author

Corrective steps during neonatal mask ventilation - a narrative review of the evidence behind the MR SOPA acronym.

Resuscitation plus·2026
Same author

Influence of mode of delivery on neonatal QTc.

Journal of perinatology : official journal of the California Perinatal Association·2026
Same author

The first breaths after birth-early lung function in healthy term infants.

American journal of respiratory and critical care medicine·2026
Same author

Distinct Clinico-pathogenic Subgroups in Pediatric Lyme Neuroborreliosis.

Open forum infectious diseases·2026

Related Experiment Video

Updated: Jun 13, 2025

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

14.4K

Outcomes Truncated by Death in RCTs: A Simulation Study on the Survivor Average Causal Effect.

Stefanie von Felten1, Chiara Vanetta1,2, Christoph M Rüegger3

  • 1Department of Biostatistics at Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.

Biometrical Journal. Biometrische Zeitschrift
|June 12, 2025
PubMed
Summary

Estimating treatment effects with missing outcomes due to death is challenging. Survivor average causal effect (SACE) and multiple imputation methods reduce bias better than complete case analysis in randomized controlled trials.

Keywords:
SACEestimandmultiple imputationprincipal stratification

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

10.1K
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

2.0K

Related Experiment Videos

Last Updated: Jun 13, 2025

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

14.4K
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

10.1K
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

2.0K

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Estimating unbiased treatment effects in randomized controlled trials (RCTs) is complicated by continuous outcomes truncated by death.
  • The survivor average causal effect (SACE) is one approach, but relies on untestable assumptions.

Purpose of the Study:

  • To compare the performance of SACE estimation with complete case analysis (CCA) and multiple imputation (MI) for continuous outcomes in the presence of death.
  • To evaluate these methods under various treatment effect scenarios on outcomes and survival.

Main Methods:

  • A simulation study was conducted with nine scenarios varying treatment effects on cognitive development and 2-year survival.
  • Compared bias, mean squared error, and coverage of SACE, CCA, and MI estimators against true treatment effects and SACE.

Main Results:

  • SACE and MI provided similar treatment effect estimates, significantly reducing bias compared to CCA.
  • Both SACE and MI demonstrated robustness to the omission of a covariate, indicating resilience to assumption violations.

Conclusions:

  • SACE and MI are valuable methods for handling continuous outcomes truncated by death in RCTs, particularly when mortality is intrinsic to the population.
  • While SACE assumptions may be violated, the methods offer practical advantages over CCA in specific clinical trial contexts.