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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

186
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
186
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
69
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

127
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
127
Survival Tree01:19

Survival Tree

85
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
85
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

130
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
130
Test for Homogeneity01:23

Test for Homogeneity

2.0K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Reduction techniques for survival analysis.

Lifetime data analysis·2026
Same author

Likelihood-based modeling of covariate-specific time-dependent receiver operating characteristic curves.

Statistical methods in medical research·2026
Same author

Rejoinder to the discussion on ''Nonparanormal Adjusted Marginal Inference''.

Biometrics·2026
Same author

Interpretations of Menstrual Blood Appearance and Diagnostic Potential Among Social Media Users: Cross-Sectional Mixed Methods Social Media Listening Study.

Journal of medical Internet research·2026
Same author

Nonparanormal adjusted marginal inference.

Biometrics·2026
Same author

Smooth transformation models for survival analysis: A tutorial using R.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
Same journal

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Heterogeneous treatment effect estimation for observational data using model-based forests.

Susanne Dandl1,2, Andreas Bender1,2, Torsten Hothorn3

  • 1Institut für Statistik, Ludwig-Maximilians-Universität München, Munich, Germany.

Statistical Methods in Medical Research
|February 9, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a modified model-based forest approach to estimate heterogeneous treatment effects in observational data, addressing confounding for complex outcomes like survival and ordinal data.

Keywords:
Heterogeneous treatment effectscensored survival datageneralized linear modelobservational datapersonalized medicinerandom foresttransformation model

More Related Videos

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
08:16

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

18.8K
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

8.2K

Related Experiment Videos

Last Updated: Jul 4, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
08:16

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

18.8K
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

8.2K

Area of Science:

  • Biostatistics
  • Econometrics
  • Epidemiology

Background:

  • Estimating heterogeneous treatment effects is crucial in medicine and economics.
  • Existing methods for complex outcomes (survival, count, ordinal) require strict assumptions and struggle with noncollapsibility.
  • Model-based forests can estimate effects but are limited to randomized trials.

Purpose of the Study:

  • To adapt model-based forests for estimating heterogeneous treatment effects in observational studies.
  • To address confounding by incorporating an orthogonalization strategy.
  • To evaluate the method's performance for generalized linear and transformation models.

Main Methods:

  • Modification of model-based forests to handle confounding in observational data.
  • Application of Robinson's (1988) orthogonalization strategy.
  • Simulation studies with various outcome distributions.
  • Assessment of Riluzole's effect on Amyotrophic Lateral Sclerosis progression for survival and ordinal outcomes.

Main Results:

  • The orthogonalization strategy effectively reduces confounding effects in simulations.
  • The modified approach allows for simultaneous estimation of treatment and prognostic effects.
  • Demonstrated practical application for survival and ordinal outcome data.

Conclusions:

  • The proposed modifications enhance model-based forests for heterogeneous treatment effect estimation in observational data.
  • The method provides a robust approach for complex outcome types.
  • This work facilitates more reliable treatment effect estimation in real-world settings.