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

Randomized Experiments01:13

Randomized Experiments

8.9K
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...
8.9K
Cluster Sampling Method01:20

Cluster Sampling Method

14.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.0K
Censoring Survival Data01:09

Censoring Survival Data

529
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...
529
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

561
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...
561
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

580
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...
580
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

183
Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
183

You might also read

Related Articles

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

Sort by
Same author

Patterns of Social Risk Factors and Associations With Health Outcomes: Findings From a Latent Class Analysis.

Inquiry : a journal of medical care organization, provision and financing·2026
Same author

Predictors of Tobacco Use Behaviors Among Syrian Americans.

Tobacco use insights·2026
Same author

Survival Disparities in Hispanic/Latino Patients With Gastric Cancer at a High-Volume Cancer Center.

The Journal of surgical research·2026
Same author

A phase II randomized placebo-controlled study of fisetin to improve physical function in breast cancer survivors: the TROFFi study rationale and trial design.

Therapeutic advances in medical oncology·2026
Same author

Correction: Tobacco Use, Experiences and Knowledge among Indigenous Mexican Agricultural Workers.

Journal of immigrant and minority health·2026
Same author

Tobacco Use, Experiences and Knowledge Among Indigenous Mexican Agricultural Workers.

Journal of immigrant and minority health·2025

Related Experiment Video

Updated: Jan 17, 2026

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

15.1K

Handling Missing Outcome Data in Cluster Randomized Trials With Both Individual- and Cluster-Level Dropout.

Analissa Avila1, Beth A Glenn2, Roshan Bastani2

  • 1Department of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, California, USA.

Statistics in Medicine
|September 17, 2025
PubMed
Summary

Missing data in cluster randomized trials (CRTs) can be sporadic or systematic. This study found specific multiple imputation methods perform well, offering robust sensitivity analyses for missing outcome data in CRTs.

Keywords:
clustered datamissing datamissing not at randommultiple imputationsystematically missing

More Related Videos

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

6.3K
A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
04:53

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

11.1K

Related Experiment Videos

Last Updated: Jan 17, 2026

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

15.1K
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

6.3K
A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
04:53

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

11.1K

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Missing outcome data are prevalent in cluster randomized trials (CRTs), posing challenges for statistical inference.
  • Missingness can manifest as sporadic (individual dropout) or systematic (cluster dropout), potentially driven by different mechanisms.
  • Effective handling of both types of missing data is crucial for reliable CRT results.

Purpose of the Study:

  • To develop and evaluate practical methods for handling both sporadic and systematic missing outcome data in CRTs.
  • To assess the performance of various multilevel multiple imputation (MI) techniques under different missing data scenarios.
  • To create sensitivity analysis methods for evaluating inference robustness under missing at random (MAR) and missing not at random (MNAR) assumptions.

Main Methods:

  • A simulation study evaluated the performance of several multilevel multiple imputation (MI) methods, including full conditional specification (FCS), for handling missing CRT outcome data.
  • The simulation examined performance under a multilevel covariate-dependent missingness assumption.
  • Novel methods for sensitivity analysis were developed to test robustness under distinct MAR and MNAR assumptions for individual and cluster dropout.

Main Results:

  • Several FCS-based MI methods demonstrated good performance for addressing sporadic and systematic missingness in CRTs across various simulated scenarios.
  • An FCS approach utilizing a two-stage estimator was found to perform poorly.
  • The developed sensitivity analysis methods, incorporating graphical displays, effectively visualized robustness under different missing data assumptions.

Conclusions:

  • Specific multilevel MI techniques, particularly certain FCS methods, offer a viable solution for managing complex missing outcome data patterns in CRTs.
  • The proposed sensitivity analysis framework allows researchers to assess the impact of potential unobserved missing data mechanisms (MNAR) on study findings.
  • These methods enhance the reliability and interpretability of results from CRTs with substantial missing outcome data.