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

Causality in Epidemiology01:21

Causality in Epidemiology

679
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
679
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

118
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...
118
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

79
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...
79
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

531
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
531
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

474
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
474
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

483
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
483

You might also read

Related Articles

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

Sort by
Same author

Joint structure learning and causal effect estimation for categorical graphical models.

Biometrics·2024
Same author

Learning Bayesian Networks: A Copula Approach for Mixed-Type Data.

Psychometrika·2024
Same author

Bayesian approaches to designing replication studies.

Psychological methods·2023
Same author

Bayesian learning of multiple directed networks from observational data.

Statistics in medicine·2020
Same author

Bayesian inference of causal effects from observational data in Gaussian graphical models.

Biometrics·2020
Same author

The Measure of Population Aging in Different Welfare Regimes: A Bayesian Dynamic Modeling Approach.

European journal of population = Revue europeenne de demographie·2020
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Aug 23, 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

Bayesian graphical modeling for heterogeneous causal effects.

Federico Castelletti1, Guido Consonni1

  • 1Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy.

Statistics in Medicine
|November 1, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian approach to personalize Acute Myeloid Leukemia (AML) treatments by analyzing protein levels and patient heterogeneity. It identifies distinct causal effects and clusters patients beyond traditional classifications for targeted therapies.

Keywords:
Dirichlet process mixturedirected acyclic graphpersonalized treatmentsubject-specific graphtumor heterogeneity

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K

Related Experiment Videos

Last Updated: Aug 23, 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
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K

Area of Science:

  • Computational Biology
  • Biostatistics
  • Oncology

Background:

  • Personalized medicine seeks molecular-based treatments over traditional clinical diagnoses.
  • Acute Myeloid Leukemia (AML) shows poor response to chemotherapy, necessitating targeted therapies.
  • Individual heterogeneity poses a significant challenge in developing personalized AML treatments.

Purpose of the Study:

  • To develop a method for personalized treatment strategies in AML using molecular data.
  • To address individual heterogeneity in AML patients through a novel statistical framework.
  • To estimate subject-specific causal effects of protein regulation on disease progression.

Main Methods:

  • Utilized a Directed Acyclic Graph (DAG) model to represent protein interactions and causal effects.
  • Employed a Dirichlet Process (DP) mixture of Gaussian DAG-models to handle heterogeneity.
  • Applied Bayesian Model Averaging (BMA) for subject-specific causal effect estimation.

Main Results:

  • Identified varying effects of protein regulation across different AML individuals.
  • Developed a clustering structure that groups patients based on underlying biological heterogeneity.
  • Demonstrated that patient clusters based on molecular data show limited similarity to traditional morphological classifications.

Conclusions:

  • The proposed Bayesian framework effectively captures individual heterogeneity in AML.
  • Subject-specific causal effect estimates can guide personalized therapeutic decisions.
  • This approach offers a more precise method for classifying AML patients beyond conventional categories.