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

6.7K
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...
6.7K
Group Design02:01

Group Design

8.9K
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...
8.9K
Random Sampling Method01:09

Random Sampling Method

11.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
11.0K
Sampling Plans01:23

Sampling Plans

167
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
167
Systematic Sampling Method01:17

Systematic Sampling Method

10.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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.
Systematic sampling is one of the simplest methods...
10.0K
Stratified Sampling Method01:16

Stratified Sampling Method

11.7K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
11.7K

You might also read

Related Articles

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

Sort by
Same author

Functional principal component analysis forsparse censored data.

Biometrika·2026
Same author

Bayesian adaptive randomization in the I-SPY2 sequential multiple assignment randomized trial.

Biometrics·2026
Same author

Empirical Bound Information-Directed Sampling for Norm-Agnostic Bandits.

Reinforcement learning journal·2026
Same author

Assessing the utility of the Primary Care PTSD Screen for DSM-5 (PC-PTSD-5) as a screening tool among caregivers of hematopoietic stem cell transplantation survivors.

Cancer·2026
Same author

Independent Increments and Group Sequential Tests.

Statistics in medicine·2025
Same author

A Pilot Study of the Cancer Distress Coach-Caregiver App: A Digital Intervention for Reducing PTSD Symptoms in HCT Caregivers.

Psycho-oncology·2025
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
Same journal

A Bayesian phase I/II platform design with data augmentation accounting for delayed outcomes.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Jun 5, 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

14.4K

Adaptive randomization methods for sequential multiple assignment randomized trials (smarts) via thompson sampling.

Peter Norwood1, Marie Davidian2, Eric Laber3

  • 1Quantum Leap Healthcare Collaborative, 499 Illinois Ave, Suite 200, San Francisco, CA 94158, United States.

Biometrics
|December 16, 2024
PubMed
Summary
This summary is machine-generated.

Response-adaptive randomization (RAR) improves patient outcomes in sequential multiple assignment randomized trials (SMARTs). This study introduces Thompson Sampling-based RAR algorithms for SMARTs, enhancing ethical and statistical trial benefits.

Keywords:
inverse probability weighted estimatorprecision medicineresponse-adaptive randomization, treatment regime

More Related Videos

Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

41.7K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K

Related Experiment Videos

Last Updated: Jun 5, 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

14.4K
Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

41.7K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Adaptive Trial Design

Background:

  • Response-adaptive randomization (RAR) offers ethical and statistical advantages in single-stage trials.
  • Sequential Multiple Assignment Randomized Trials (SMARTs) are crucial for multi-stage treatment regime evaluation.
  • RAR benefits remain underexplored within the complex SMART framework.

Purpose of the Study:

  • To develop and evaluate novel RAR algorithms for SMARTs.
  • To adapt Thompson Sampling (TS) for use in multi-stage adaptive trial designs.
  • To ensure valid statistical inference for treatment regimes under RAR in SMARTs.

Main Methods:

  • Proposed a suite of RAR algorithms for SMARTs, extending Thompson Sampling (TS).
  • Developed post-study inferential procedures accounting for RAR's non-standard asymptotic behavior.
  • Utilized empirical studies on real-world SMART data for validation.

Main Results:

  • The proposed TS-based RAR algorithms improve in-trial subject outcomes.
  • Efficiency for post-trial treatment regime comparisons is maintained.
  • The algorithms are the first to address non-standard limiting behaviors in multi-stage adaptive trials.

Conclusions:

  • Thompson Sampling-based RAR is effective for SMARTs.
  • These methods enhance ethical trial conduct and patient outcomes.
  • The developed procedures support robust statistical inference in complex adaptive trials.