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

147
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...
147
Actuarial Approach01:20

Actuarial Approach

57
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,...
57
Censoring Survival Data01:09

Censoring Survival Data

56
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...
56
Randomized Experiments01:13

Randomized Experiments

6.7K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.7K
Life Tables01:22

Life Tables

70
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
70
Trimmed Mean01:10

Trimmed Mean

2.8K
While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
2.8K

You might also read

Related Articles

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

Sort by
Same author

The ARDS, Pneumonia, and Sepsis (APS) Consortium: Rationale, Design, and Feasibility of a National Platform for Phenotyping Critical Illness Syndromes.

Chest·2026
Same author

Automated Calls Added to SMS Reminders Reduce Missed Appointments among High-Risk Patients.

NEJM catalyst innovations in care delivery·2026
Same author

Individualized Treatment Effects of Therapeutic Hypothermia in Children Postcardiac Arrest: A Reanalysis of Two Randomized Clinical Trials.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies·2026
Same author

Principal stratification with U-statistics under principal ignorability.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same author

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
Same author

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026

Related Experiment Video

Updated: May 30, 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

Weighting methods for truncation by death in cluster-randomized trials.

Dane Isenberg1, Michael O Harhay1, Nandita Mitra1

  • 1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Statistical Methods in Medical Research
|January 31, 2025
PubMed
Summary

This study introduces new weighting methods to estimate treatment effects in cluster-randomized trials, addressing challenges with patient survival and improving causal inference for patient-centered outcomes.

Keywords:
Estimandscluster-randomized trialsgeneralized linear mixed modelsprincipal scoreprincipal stratificationsurvival scoresurvivor average causal effect

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: May 30, 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 Methodology
  • Epidemiology

Background:

  • Patient-centered outcomes are crucial in clinical studies but can be truncated by death in vulnerable populations.
  • Estimating treatment effects in the presence of death requires specialized causal inference methods like the survivor average causal effect.
  • Existing methods for survivor average causal effect are primarily for individually randomized trials, with limited application to cluster-randomized trials.

Purpose of the Study:

  • To develop and evaluate novel weighting methods for estimating the survivor average causal effect in cluster-randomized trials.
  • To address the limitations of existing methods that rely on strong distributional assumptions for outcome modeling.
  • To provide a robust and computationally efficient approach for causal inference in complex trial designs.

Main Methods:

  • Proposed two novel weighting methods to estimate the survivor average causal effect.
  • Established assumptions to account for latent clustering effects for point identification.
  • Developed computationally efficient asymptotic variance estimators for the proposed methods.
  • Conducted simulations to assess finite-sample performance and robustness.

Main Results:

  • The proposed weighting methods effectively estimate the survivor average causal effect in cluster-randomized trials.
  • These methods do not require complex outcome distribution modeling, offering a practical advantage.
  • Simulations demonstrated good operating characteristics and robustness to assumption violations.
  • The methods were illustrated using a real-world cluster-randomized trial in pediatric critical care.

Conclusions:

  • The developed weighting methods provide a valuable tool for causal inference in cluster-randomized trials, particularly for patient-centered outcomes.
  • These methods enhance the ability to address truncation by death in vulnerable patient groups.
  • The approach offers a more flexible and robust alternative to existing methods for estimating the survivor average causal effect.