Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Randomized Experiments

Randomized Experiments

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...

Censoring Survival Data

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...

Cluster Sampling Method

Cluster Sampling Method

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...

Blinding

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.

Random Error

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...

Group Design

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...

You might also read

Related Articles

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

Sort by

Same author

Improving inclusivity in UK clinical trials through good practice in the collection and use of ethnicity data: recommendations from the INCLUDED study.

BMJ evidence-based medicine·2026

Same author

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

Biostatistics (Oxford, England)·2026

Same author

Impact of using routine healthcare data on the efficiency of implementation trials: a qualitative comparative case study.

Trials·2026

Same author

Protocol for the development and validation of a risk score to predict risk of adverse events associated with systemic anti-cancer treatment in late-stage lung cancer: Lung Cancer Improved Decisions (LUCID).

Diagnostic and prognostic research·2026

Same author

PROMoting the use of studies within a trial (PROMETHEUS): Results and experiences from a large programme to evaluate the routine embedding of recruitment and retention strategies within randomised controlled trials routinely.

Research methods in medicine & health sciences·2026

Same author

The PROMoting the USE of SWATs (PROMETHEUS) programme: Lessons learnt and future developments for SWATs.

Research methods in medicine & health sciences·2026

Search research articles

Related Experiment Video

Updated: Apr 28, 2026

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

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

Published on: January 8, 2020

Are missing data adequately handled in cluster randomised trials? A systematic review and guidelines.

Karla Díaz-Ordaz¹, Michael G Kenward², Abie Cohen³

¹Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK karla.diaz-ordaz@lshtm.ac.uk.

Clinical Trials (London, England)

|June 7, 2014

Summary

Missing data are common in cluster randomized trials but poorly handled, often with methods assuming strong conditions. Improved reporting and accessible methods are needed for valid trial inferences.

Keywords:

Cluster randomised trials missing data multiple imputation

More Related Videos

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

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

Published on: September 20, 2019

Related Experiment Videos

Last Updated: Apr 28, 2026

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

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

Published on: January 8, 2020

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

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

Published on: September 20, 2019

Area of Science:

Biostatistics
Clinical Trials Methodology
Epidemiology

Background:

Missing data can introduce bias and affect variance estimation in statistical analyses.
Handling missing data is particularly complex in cluster randomized trials.
A review of current practices for managing missing data in these trials was lacking.

Purpose of the Study:

To systematically review the reporting and handling of missing data in published cluster randomized trials.
To identify common statistical methods used to address missing data in this trial design.

Main Methods:

Systematic identification of cluster randomized trials published in English in 2011 via MEDLINE/PubMed.
Exclusion of non-randomized, pilot/feasibility, secondary/interim analyses, economic evaluations, and trials without individual-level data.
Extraction of information on missing data and statistical handling methods from a random sample of included trials.

Main Results:

Missing data were present in 72% of the 132 included trials.
Only 32 trials reported methods for handling missing data.
Common methods included single imputation (22 trials) and multiple imputation without accounting for clustering (8 trials).

Conclusions:

Missing data are prevalent in cluster randomized trials but inadequately reported.
Current methods often rely on strong, unstated assumptions, potentially compromising inference validity.
More accessible methods valid under general Missing-at-Random assumptions should be promoted.