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
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
Blinding01:11

Blinding

2.5K
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
2.5K
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 Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

70
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...
70
Blind Procedures02:07

Blind Procedures

10.7K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
10.7K

You might also read

Related Articles

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

Sort by
Same author

Abbreviated DAPT Regimens Across the Entire Spectrum of Bleeding Risk According to the PRECISE-HBR Score.

JACC. Cardiovascular interventions·2026
Same author

Improved prostate cancer grading by incorporating Gleason pattern quantification, invasive cribriform and intraductal carcinoma in the new QUICC-score.

Annals of diagnostic pathology·2026
Same author

Patient Versus Prediction-Level Evaluation of a Dynamic Clinical Prediction Model of Sepsis.

Research square·2026
Same author

Effect of Using Personalized Estimates of Diabetes Risk During Primary Care Visits for People With Prediabetes.

Learning health systems·2026
Same author

Patient Versus Prediction-Level Evaluation of a Dynamic Clinical Prediction Model of Sepsis.

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

Feasibility of Causality-Aware Machine Learning for Drug Safety on OMOP-CDM.

Studies in health technology and informatics·2026

Related Experiment Video

Updated: Aug 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

14.5K

Estimating individualized treatment effects from randomized controlled trials: a simulation study to compare

Alexandros Rekkas1, Peter R Rijnbeek2, David M Kent3

  • 1Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands. a.rekkas@erasmusmc.nl.

BMC Medical Research Methodology
|March 28, 2023
PubMed
Summary

Predicting treatment benefits requires considering baseline risk. Linear interaction models work well for moderate sample sizes, while complex models suit larger datasets with non-linear effects.

Keywords:
Absolute benefitPrediction modelsTreatment effect heterogeneity

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K

Related Experiment Videos

Last Updated: Aug 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

14.5K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K

Area of Science:

  • Medical research
  • Biostatistics
  • Clinical trial analysis

Background:

  • Baseline outcome risk is crucial for determining absolute treatment benefit.
  • Risk-based methods are used in guidelines for personalizing medical decisions.
  • Accurate prediction of individualized treatment effects is essential.

Purpose of the Study:

  • To compare easily applicable risk-based methods for predicting individualized treatment effects.
  • To evaluate the performance of different statistical models in predicting treatment benefits based on baseline risk.
  • To identify optimal methods for personalizing medical treatment decisions.

Main Methods:

  • Simulated randomized controlled trial (RCT) data with varying assumptions for treatment effects, baseline risk, interaction shapes, and harms.
  • Predicted absolute benefit using models with constant relative treatment effect, prognostic index stratification, linear interaction, restricted cubic splines interaction, and an adaptive approach.
  • Evaluated predictive performance using root mean squared error, discrimination, and calibration measures.

Main Results:

  • The linear-interaction model showed optimal or near-optimal performance in many scenarios with moderate sample sizes (N=4,250).
  • Restricted cubic splines models were optimal for strong non-linear treatment effects, especially with larger sample sizes (N=17,000).
  • Adaptive approaches also required larger sample sizes for optimal performance.

Conclusions:

  • An interaction between baseline risk and treatment assignment is important for improving treatment effect predictions.
  • The choice of prediction model depends on sample size and the nature of the treatment effect interaction.
  • Findings were illustrated using data from the GUSTO-I trial.