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

461
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...
461
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

328
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.
328
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

390
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
390
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

502
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,...
502
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

500
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...
500
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.7K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.7K

You might also read

Related Articles

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

Sort by
Same author

Surgery or radiotherapy for early-stage cancer study protocol for an emulated target trial of radical radiotherapy versus radical cystectomy, with either following neoadjuvant chemotherapy, for organ-confined muscle-invasive bladder cancer.

BMJ open·2026
Same author

PERsonalised Medicine for Intensification of Treatment (PERMIT) in type 2 diabetes mellitus: a target trial emulation from routine data.

Health technology assessment (Winchester, England)·2026
Same author

Multi-arm multi-stage platform trials for neurological disease: accelerating progress.

Lancet (London, England)·2026
Same author

Repurposed drug prioritization pipeline for a multi-arm platform trial in clinical Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Sample Size Calculation for the ROCI Design.

Statistics in medicine·2026
Same author

IV-learner: learning conditional average treatment effects using instrumental variables.

Biostatistics (Oxford, England)·2026
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Dec 28, 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

15.0K

Estimating treatment effects under untestable assumptions with nonignorable missing data.

Manuel Gomes1, Michael G Kenward2, Richard Grieve3

  • 1Department of Applied Health Research, University College London, London, UK.

Statistics in Medicine
|February 15, 2020
PubMed
Summary
This summary is machine-generated.

Estimating treatment effects with missing data is challenging. The full-likelihood approach is less sensitive to exclusion restriction assumptions than Heckman-type models for nonignorable missing data.

Keywords:
Heckman modelaverage treatment effectsfull-information maximum likelihoodmissing not at randommultiple imputationselection models

More Related Videos

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.1K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.2K

Related Experiment Videos

Last Updated: Dec 28, 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

15.0K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.1K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.2K

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Nonignorable missing data complicates treatment effect estimation.
  • Heckman selection models require strict assumptions about data distribution and exclusion restrictions.
  • Existing research compares multiple imputation (MI) and maximum likelihood to Heckman-type models but overlooks exclusion restriction reliance.

Purpose of the Study:

  • Critically examine the role of exclusion restrictions in Heckman, MI, and full-likelihood selection models for nonignorable missing data.
  • Investigate how methodological choices impact bias and root-mean-squared error in treatment effect estimation.
  • Assess the sensitivity of different models to exclusion restriction assumptions.

Main Methods:

  • Comparative analysis of Heckman, multiple imputation (MI), and full-likelihood selection models.
  • Evaluation of model performance based on bias and root-mean-squared error.
  • Application to the REFLUX study data with nonignorable missing outcome data.

Main Results:

  • The relative performance of selection models varies significantly with the strength and relevance of the exclusion restriction.
  • Full-likelihood models demonstrate greater robustness to alternative exclusion restriction assumptions compared to Heckman-type models.
  • The choice of method has practically important implications for treatment effect inference.

Conclusions:

  • Full-likelihood selection models are a suitable method for handling nonignorable missing data due to their reduced sensitivity to exclusion restriction assumptions.
  • Understanding the role of exclusion restrictions is crucial for accurate treatment effect estimation in the presence of missing data.
  • Findings are illustrated using the REFLUX study evaluating laparoscopic surgery for gastro-esophageal reflux disease.