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

7.1K
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...
7.1K
Sample Size Calculation01:19

Sample Size Calculation

3.6K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
3.6K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

5.6K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
5.6K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.4K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

151
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
151
Odds Ratio01:09

Odds Ratio

196
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
196

You might also read

Related Articles

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

Sort by
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

Effective monitoring of online AI decision-making algorithms in just-in-time adaptive interventions.

NPJ digital medicine·2026
Same author

SigmaScheduling: Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions.

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks·2026
Same author

Non-Stationary Latent Auto-Regressive Bandits.

Reinforcement learning journal·2026
Same author

Harnessing Causality in Reinforcement Learning With Bagged Decision Times.

Proceedings of machine learning research·2026
Same author

Digital Twins for Just-in-Time Adaptive Interventions (JITAIs): Framework for Optimizing and Continually Improving JITAIs.

Journal of medical Internet research·2026
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Aug 1, 2025

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

14.5K

Sample size considerations for micro-randomized trials with binary proximal outcomes.

Eric R Cohn1, Tianchen Qian2, Susan A Murphy3

  • 1Department of Biostatistics, Harvard University, Cambridge, Massachusetts, USA.

Statistics in Medicine
|April 24, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a sample size formula for micro-randomized trials (MRTs) with binary outcomes. The formula ensures statistical power for detecting causal effects in mobile health intervention development.

Keywords:
causal excursion effectcausal inferencelongitudinal data analysismobile healthsample size calculation

More Related Videos

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.0K
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

10.7K

Related Experiment Videos

Last Updated: Aug 1, 2025

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

14.5K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.0K
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

10.7K

Area of Science:

  • Biostatistics
  • Digital Health
  • Clinical Trials

Background:

  • Micro-randomized trials (MRTs) enable repeated randomization for mobile health interventions, generating longitudinal data with time-varying treatments.
  • Causal excursion effects are key outcomes in MRT analyses, particularly for binary proximal measures.
  • Existing methods lack specific sample size calculations for MRTs with binary outcomes.

Purpose of the Study:

  • To develop and validate a sample size formula for detecting nonzero marginal excursion effects in MRTs.
  • To provide practical guidelines for applying the sample size formula in trial planning.
  • To ensure adequate statistical power for MRTs investigating binary proximal outcomes.

Main Methods:

  • Development of a novel sample size formula tailored for MRTs with binary proximal outcomes.
  • Mathematical proof of the formula's power guarantees under specified working assumptions.
  • Simulation studies to assess the impact of assumption violations on statistical power.

Main Results:

  • The proposed sample size formula is proven to guarantee statistical power under defined assumptions.
  • Simulation results indicate that certain assumption violations minimally impact power, while others suggest directional changes.
  • The formula was successfully applied to size an MRT for excessive drinking interventions.

Conclusions:

  • The developed sample size formula is a valuable tool for planning MRTs with binary proximal outcomes.
  • Practical guidelines and an R package (MRTSampleSizeBinary) facilitate the formula's application.
  • This work supports robust trial design in mobile health intervention research.