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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

697
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...
697
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

483
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.
483
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

555
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
555
Group Design02:01

Group Design

11.0K
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...
11.0K
Censoring Survival Data01:09

Censoring Survival Data

631
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...
631
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.9K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.9K

You might also read

Related Articles

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

Sort by
Same author

Electronic connectivity between hospital pairs: impact on emergency department-related utilization.

Journal of the American Medical Informatics Association : JAMIA·2025
Same author

A Comprehensive Simulation Study to Evaluate the Effect Size and Study Length Relationship in Single-Group Interrupted Time Series Analysis.

Evaluation & the health professions·2025
Same author

Adenotonsillectomy and Health Care Utilization in Children With Snoring and Mild Sleep Apnea: A Randomized Clinical Trial.

JAMA pediatrics·2025
Same author

Health Care Resource Use and Costs After Hospitalization With Multiple Organ Dysfunction in Children.

JAMA network open·2025
Same author

Problematic meta-analyses: Bayesian and frequentist perspectives on combining randomized controlled trials and non-randomized studies.

BMC medical research methodology·2024
Same author

Electronic connectivity between hospital pairs: impact on emergency department-related utilization.

Journal of the American Medical Informatics Association : JAMIA·2023
Same journal

The "Twilight Zone" Is a Danger Zone: Why the Occupational-Clinical Divide in Burnout Assessment Is a False Dichotomy.

Evaluation & the health professions·2026
Same journal

Evaluating Equity in AI-Supported Functional Assessment: Agreement Between Clinician Judgment and Digital Metrics in Stroke Rehabilitation.

Evaluation & the health professions·2026
Same journal

Psychometric Properties of the Arabic Version of the PROMIS Sleep Disturbance 8b Short Form Among Nurses.

Evaluation & the health professions·2026
Same journal

Commentary: Systemic Inequities in Japan's Technical Intern Training Program (TITP): Health, Labor, and Legal Vulnerabilities of Foreign Trainees.

Evaluation & the health professions·2026
Same journal

Application of Patient-Reported Outcome Measurements in Traditional Chinese Medicine Clinical Trials for Musculoskeletal Disorders in China: A Registry-Based Analysis.

Evaluation & the health professions·2026
Same journal

Divergent Socioeconomic Pathways to Biologically Uncontrolled Diabetes by Gender: A Bayesian Analysis of NHANES 2021-2023.

Evaluation & the health professions·2026
See all related articles

Related Experiment Video

Updated: Mar 24, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K

Power Considerations for Multiple-Group (Controlled) Interrupted Time Series Analysis: A Comprehensive Simulation

Ariel Linden1

  • 1Department of Medicine, Division of Clinical Informatics & Digital Transformation (DoC-IT), University of California, San Francisco, CA, USA.

Evaluation & the Health Professions
|March 17, 2026
PubMed
Summary
This summary is machine-generated.

This study provides guidance on power for multiple-group (controlled) interrupted time series (MG-ITSA) designs. Key factors influencing power include study length, control units, effect size, and autocorrelation.

Keywords:
autocorrelationcontrolled interrupted time series analysisinterrupted time series analysismultiple-group interrupted time series analysispowersample size

More Related Videos

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

16.1K
The Power of Interstimulus Interval for the Assessment of Temporal Processing in Rodents
10:27

The Power of Interstimulus Interval for the Assessment of Temporal Processing in Rodents

Published on: April 19, 2019

7.4K

Related Experiment Videos

Last Updated: Mar 24, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K
Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

16.1K
The Power of Interstimulus Interval for the Assessment of Temporal Processing in Rodents
10:27

The Power of Interstimulus Interval for the Assessment of Temporal Processing in Rodents

Published on: April 19, 2019

7.4K

Area of Science:

  • Health research methodology
  • Statistical power analysis

Background:

  • Limited guidance exists for power considerations in multiple-group (controlled) interrupted time series (MG-ITSA) designs.
  • MG-ITSA designs are crucial for evaluating interventions in public health and clinical research.

Purpose of the Study:

  • To estimate statistical power for MG-ITSA designs based on various factors.
  • To provide recommendations for optimizing power in MG-ITSA studies.

Main Methods:

  • Simulations were used to estimate power.
  • Factors examined included number of time periods, control units, treatment introduction timing, and autocorrelation.
  • Measures of effect included difference-in-differences (DID) in level and trend.

Main Results:

  • Higher power was associated with longer studies, more control units, larger effect sizes, and lower autocorrelation.
  • DID in level required fewer time periods than DID in trend for desired power.
  • Treatment introduction at the midpoint generally yielded higher power for DID in trend.

Conclusions:

  • Researchers can increase power by increasing control units, time periods, and effect size, or by using DID in level.
  • Autocorrelation must be accounted for in time series regression models.
  • Consideration of these factors is essential for efficient MG-ITSA study design in health research.