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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
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...
43
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
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...
6.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.9K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

149
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,...
149
Randomized Experiments01:13

Randomized Experiments

7.0K
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...
7.0K

You might also read

Related Articles

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

Sort by
Same author

Multiomic analysis identifies glutaminolysis-dependent metabolic enhancement of immune memory utilised for vaccine development.

Nature communications·2026
Same author

Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

Enabling AI to Drive Innovation and Precision across Oncology R&D.

Cancer discovery·2026
Same author

Anti-tumor necrosis factor treatment from diagnosis is more effective and less costly than conventional "step-up" care for patients with active Crohn's disease: a cost-effectiveness analysis from the PROFILE trial.

Journal of Crohn's & colitis·2025
Same author

Telomere attrition becomes an instrument for clonal selection in aging hematopoiesis and leukemogenesis.

Nature genetics·2025
Same author

Disease-specific B cell clones are shared between patients with Crohn's disease.

Nature communications·2025
Same journal

Resolving CYP2D6 Structural Complexity with Long-Read Sequencing: Implications for Tamoxifen Precision Dosing in Thai Breast Cancer Patients.

Clinical pharmacology and therapeutics·2026
Same journal

Identification of a Functional CYP2C8 Variant Allele that Alters Splicing, Reduces Protein Expression, and Increases Drug Exposure.

Clinical pharmacology and therapeutics·2026
Same journal

Risk of Hyperkalemia in Patients with Heart Failure Treated with Spironolactone in Combination with Sacubitril/Valsartan vs. Renin-Angiotensin System Inhibitors.

Clinical pharmacology and therapeutics·2026
Same journal

Composite Endpoints in Contemporary Cardiovascular Trials: Trends in Phase 3 Trials and Key Issues in Regulatory Review.

Clinical pharmacology and therapeutics·2026
Same journal

Patient-Specific Determinants of Response to BCMA- and GPRC5D-Targeted CAR T-Cell Therapy in Multiple Myeloma: A QSP Analysis of Clinical Trial and Real-World Data.

Clinical pharmacology and therapeutics·2026
Same journal

Reply to: "The Future of Clinical Pharmacology: The Right Medicine at the Right Dose for Each Patient".

Clinical pharmacology and therapeutics·2026
See all related articles

Related Experiment Video

Updated: Jul 7, 2025

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

8.3K

Using Machine Learning to Individualize Treatment Effect Estimation: Challenges and Opportunities.

Alicia Curth1, Richard W Peck2,3, Eoin McKinney4,5

  • 1Department of Applied Mathematics & Theoretical Physics, University of Cambridge, Cambridge, UK.

Clinical Pharmacology and Therapeutics
|December 21, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning can improve treatment decisions for individual patients by estimating conditional average treatment effects (CATE) from observational data, addressing limitations of clinical trial generalizability.

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Related Experiment Videos

Last Updated: Jul 7, 2025

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

8.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Area of Science:

  • Biostatistics
  • Health Informatics
  • Machine Learning

Background:

  • Current treatment decisions rely on extrapolating average effects from randomized clinical trials (RCTs) to diverse real-world patients.
  • This extrapolation is often inaccurate due to heterogeneity in treatment effects and differences between trial and real-world populations.

Purpose of the Study:

  • To review the potential of machine learning (ML) for estimating conditional average treatment effects (CATE) in individual patients using observational data.
  • To explore challenges and opportunities in applying ML for CATE estimation in personalized medicine.

Main Methods:

  • Utilizing machine learning algorithms to analyze observational data for estimating CATE.
  • Addressing key challenges such as ensuring identification assumptions, managing covariate shift, and learning without true labels.

Main Results:

  • ML offers a promising approach for more accurate personalized treatment effect estimation compared to traditional RCT extrapolation.
  • Identified challenges require further methodological development and validation for reliable CATE estimation.

Conclusions:

  • Machine learning holds significant potential to enhance patient benefit by enabling more precise treatment effect predictions.
  • Further research, collaboration, and methodological advancements are crucial for the effective implementation of CATE estimates in clinical practice.