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

652
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...
652
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

867
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
867
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.1K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.1K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

456
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.
456
Censoring Survival Data01:09

Censoring Survival Data

604
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...
604
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

367
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
367

You might also read

Related Articles

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

Sort by
Same author

Validation of a self-harm prediction model in black youth seeking outpatient mental health care.

American journal of epidemiology·2026
Same author

The Generativity of Variability for Evidence-Based Practice.

Nursing philosophy : an international journal for healthcare professionals·2026
Same author

Large-Scale Synchronization Dynamics During Epileptic Seizures: A Patient-Independent EEG Network Analysis.

Entropy (Basel, Switzerland)·2026
Same author

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same author

Interrupted Time Series Methods for Nonrandom Sampling Study Designs With Known Sampling Weights.

Statistics in medicine·2026
Same author

Liquid Perception and Event and Nursing.

Nursing philosophy : an international journal for healthcare professionals·2025

Related Experiment Video

Updated: Feb 23, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

A robust interrupted time series model for analyzing complex health care intervention data.

Maricela Cruz1, Miriam Bender2, Hernando Ombao1,3

  • 1Department of Statistics, University of California, Irvine, CA, USA.

Statistics in Medicine
|August 30, 2017
PubMed
Summary

A new robust interrupted time series (robust-ITS) model analyzes healthcare changes, accounting for data variation and correlation shifts. This method improves understanding of intervention effectiveness in complex care delivery systems.

Keywords:
complex interventionshealth care outcomesintervention analysissegmented regressiontime series

More Related Videos

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

15.4K
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

687

Related Experiment Videos

Last Updated: Feb 23, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
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

15.4K
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

687

Area of Science:

  • Health Services Research
  • Biostatistics
  • Health Policy Analysis

Background:

  • Healthcare policy emphasizes evidence-based care for improved patient outcomes.
  • Analyzing complex care delivery with time-series data presents statistical challenges, especially with interventions.
  • Traditional interrupted time series (ITS) methods have limitations in modeling post-intervention changes in data variability and correlation.

Purpose of the Study:

  • To introduce a novel robust interrupted time series (robust-ITS) model.
  • To address limitations of current ITS methods, including modeling changes in data variation and correlation.
  • To provide a comprehensive tool for analyzing interrupted time series data in healthcare.

Main Methods:

  • Development of the robust-ITS model for formal inference on change points.
  • The model assesses differences in pre- and post-intervention correlation and outcome variance.
  • Analysis includes differences in the mean outcome pre- and post-intervention.

Main Results:

  • The robust-ITS model successfully identifies change points and quantifies shifts in correlation and variance.
  • Patient satisfaction data from a nursing care delivery model intervention was analyzed.
  • The proposed method provides a more complete picture of intervention effects than standard ITS.

Conclusions:

  • The robust-ITS model offers a significant advancement for analyzing interrupted time series data in healthcare.
  • It overcomes key limitations of existing methods by modeling changes in data dependency and variability.
  • The freely available R Shiny toolbox facilitates the adoption of this robust method for evaluating healthcare interventions.