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

Multiple Regression01:25

Multiple Regression

3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
Response Surface Methodology01:16

Response Surface Methodology

580
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
580
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

162
Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
162
Regression Toward the Mean01:52

Regression Toward the Mean

6.8K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.8K
Randomized Experiments01:13

Randomized Experiments

8.8K
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...
8.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

226
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
226

You might also read

Related Articles

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

Sort by
Same author

Adverse Childhood Experiences and Growth Outcomes in Childhood: A Longitudinal EHR-Based Study.

medRxiv : the preprint server for health sciences·2026
Same author

Generative artificial intelligence implementation in REDCap.

JAMIA open·2026
Same author

End-to-end extraction of temporal information from psychiatric discharge summaries.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Exploration of NMDA and GABA receptor-mediated plasticity induced by 10-Hz repetitive transcranial magnetic stimulation.

Translational psychiatry·2026
Same author

Benchmarking reliability and calibration of LLMs for multi-cancer early detection test communication.

JAMIA open·2026
Same author

Smartphone-based cognitive assessment in older adults with depression: Feasibility and task performance using ecological momentary assessment.

Journal of affective disorders·2026
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Multi-study R-learner for estimating heterogeneous treatment effects across studies using statistical machine

Cathy Shyr1, Boyu Ren2, Prasad Patil3

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN 37203, United States.

Biostatistics (Oxford, England)
|December 18, 2025
PubMed
Summary
This summary is machine-generated.

Estimating heterogeneous treatment effects (HTE) is crucial for precision medicine. Our multi-study R-learner framework effectively leverages multiple studies to improve HTE estimation, especially when treatment assignment varies between studies.

Keywords:
between-study heterogeneitycausal inferenceconditional average treatment effectmachine learningprecision medicine

More Related Videos

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K
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

6.3K

Related Experiment Videos

Last Updated: Jan 8, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K
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

6.3K

Area of Science:

  • Biostatistics
  • Machine Learning
  • Precision Medicine

Background:

  • Heterogeneous treatment effect (HTE) estimation is vital for personalized medicine.
  • Existing multi-study methods often rely on restrictive assumptions about study homogeneity.
  • Challenges arise when patient covariate profiles overlap across multiple studies.

Purpose of the Study:

  • To propose a flexible machine learning framework, the multi-study R-learner, for estimating HTE using data from multiple studies.
  • To develop a method that explicitly accounts for heterogeneity in conditional average treatment effects (CATE), potential outcomes, and treatment assignment mechanisms across studies.
  • To improve upon existing multi-study approaches by enabling information borrowing across studies.

Main Methods:

  • Developed a multi-study R-learner framework leveraging cross-study learning principles.
  • Employed a data-adaptive objective function to integrate nuisance function estimates with study-specific CATEs.
  • Utilized membership probabilities to facilitate information sharing across studies.
  • Extended the original R-learner to a multi-study setting, allowing incorporation of various machine learning techniques.

Main Results:

  • The proposed multi-study R-learner is asymptotically normal.
  • Demonstrated increased efficiency compared to the standard R-learner when treatment assignment mechanisms differ between studies.
  • Showcased favorable performance of the multi-study R-learner using cancer data from both randomized controlled trials and observational studies, particularly under between-study heterogeneity.

Conclusions:

  • The multi-study R-learner offers a flexible and robust approach to HTE estimation in multi-study settings.
  • The framework effectively handles between-study heterogeneity, leading to more accurate personalized treatment effect estimates.
  • This method advances precision medicine by enabling better utilization of diverse data sources for treatment decision-making.