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

Causality in Epidemiology01:21

Causality in Epidemiology

1.1K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.1K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

304
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,...
304
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

434
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...
434
Censoring Survival Data01:09

Censoring Survival Data

284
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...
284
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

256
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
256
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

350
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...
350

You might also read

Related Articles

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

Sort by
Same author

Wearable Data Economy: Implications of the FDA's 2026 General Wellness Policy.

Circulation. Population health and outcomes·2026
Same author

Qualitative Analysis of User Experiences of a mHealth Self-Care Intervention for Care Partners of Individuals with Traumatic Brain Injury.

Archives of rehabilitation research and clinical translation·2026
Same author

Mobile intervention for emerging adults with regular cannabis use: a micro-randomized trial.

Lancet regional health. Americas·2026
Same author

Conduction mode transition in Ag nanowire-mesh hybrid electrodes by junction welding for high-performance transparent conductive electrodes.

Nanoscale·2026
Same author

Is More Always Better With Digital Health Interventions? Shifting Engagement From Maximizing Use to Supporting Health.

Mayo Clinic proceedings. Digital health·2026
Same author

Q-Learning with clustered-SMART (cSMART) data: examining moderators in the construction of clustered adaptive interventions.

Biometrics·2026

Related Experiment Video

Updated: Oct 17, 2025

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

10.8K

Estimating time-varying causal excursion effect in mobile health with binary outcomes.

Tianchen Qian1, Hyesun Yoo2, Predrag Klasnja3

  • 1Department of Statistics, University of California, Irvine, Donald Bren Hall, Irvine, California 92697, U.S.A.

Biometrika
|October 11, 2021
PubMed
Summary

This study introduces a new causal effect definition for micro-randomized trials with binary outcomes. The developed estimator offers a more plausible approach for analyzing mobile health intervention effects in real-world settings.

Keywords:
Binary outcomeCausal excursion effectCausal inferenceLongitudinal dataMicro-randomized trialsMobile healthRelative riskSemiparametric efficiency theory

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.7K

Related Experiment Videos

Last Updated: Oct 17, 2025

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

10.8K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.7K

Area of Science:

  • Digital Health
  • Biostatistics
  • Behavioral Science

Background:

  • Wearable technology enables mobile health interventions in daily life.
  • Micro-randomized trials (MRTs) are crucial for gathering data on these interventions.
  • Longitudinal binary outcomes are common in MRTs, necessitating robust analytical methods.

Purpose of the Study:

  • To define a causal excursion effect for primary aim analysis in MRTs with binary outcomes.
  • To develop a semiparametric, locally efficient estimator for causal effects under plausible assumptions.
  • To provide a statistical framework for evaluating time-varying mobile health interventions.

Main Methods:

  • Proposed a novel definition of the causal excursion effect.
  • Derived a semiparametric estimator based on existing literature.
  • Developed a new estimator for primary aim analysis under relaxed assumptions.
  • Conducted simulation studies to compare estimator performance.
  • Applied methods to data from the BariFit micro-randomized trial.

Main Results:

  • The proposed causal excursion effect definition is suitable for MRTs with longitudinal binary outcomes.
  • The developed estimator performs well in simulation studies compared to existing methods.
  • The methods were successfully illustrated using the BariFit trial data.

Conclusions:

  • The novel causal excursion effect and associated estimator provide a valuable tool for analyzing mobile health interventions in MRTs.
  • The findings support the use of advanced statistical methods for optimizing digital health interventions.
  • The BariFit trial exemplifies the application of these methods for weight maintenance support post-bariatric surgery.