Jove
Visualize
Contact Us

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

6.6K
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...
6.6K
Randomized Experiments01:13

Randomized Experiments

8.3K
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...
8.3K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

207
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,...
207
Hazard Ratio01:12

Hazard Ratio

302
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
302
Cancer Survival Analysis01:21

Cancer Survival Analysis

481
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
481
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

318
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,...
318

You might also read

Related Articles

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

Sort by
Same author

(Cost-)effectiveness of focal therapy versus radical therapy (standard of care) in the treatment of men with intermediate-risk prostate cancer: study protocol for the ENFORCE focal randomised controlled trial.

BMJ open·2026
Same author

Intranasal monoclonal antibodies do not prevent respiratory infection in a randomized, controlled experimental infection trial.

npj drug discovery·2026
Same author

The role of hormonal markers in predicting reproductive lifespan in girls with Turner syndrome-a retrospective study.

Journal of the Endocrine Society·2026
Same author

Comparison of local large language models for extraction of signs and symptoms data from electronic health records.

PloS one·2026
Same author

Swab Testing to Optimize Pneumonia Treatment With Empiric Vancomycin: A Randomized Controlled Trial.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026
Same author

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Afternoon panel discussion).

Clinical trials (London, England)·2026
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 Experiment Video

Updated: Oct 23, 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

14.8K

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint.

Jeroen Hoogland1, Joanna IntHout2, Michail Belias2

  • 1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.

Statistics in Medicine
|August 17, 2021
PubMed
Summary
This summary is machine-generated.

Predicting individualized treatment effects offers better clinical decisions than average effects. This study outlines causal structures and logistic regression models for personalized absolute treatment effect predictions in randomized trials.

Keywords:
causal inferencepersonalized medicinepredictionregressiontreatment effect

More Related Videos

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

8.4K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

813

Related Experiment Videos

Last Updated: Oct 23, 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

14.8K
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

8.4K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

813

Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Randomized trials traditionally focus on average treatment effects.
  • Individualized treatment benefit may be better assessed using personalized predictions.
  • Absolute treatment effect prediction requires understanding individual risk and differential treatment effects.

Purpose of the Study:

  • To outline the causal structure for individualized treatment effect prediction.
  • To describe regression models and estimation techniques for personalized predictions.
  • To provide guidance on modeling and estimation for individualized treatment effect prediction in randomized trials.

Main Methods:

  • Utilizing potential outcomes framework for causal interpretation.
  • Employing logistic regression-based methods for probabilistic estimates.
  • Integrating causal inference and prediction research for amalgamation.

Main Results:

  • Demonstrated methods for moving from average to individualized treatment effect predictions.
  • Illustrated the potential of different modeling options using simulated data.
  • Provided applied examples of individualized treatment effect prediction in real-world trial data.

Conclusions:

  • Individualized treatment effect prediction enhances clinical decision-making.
  • Clear definition of estimands and understanding assumptions are crucial.
  • Amalgamation of causal inference and prediction is an active research area with practical implications.