Related Experiment Video

Updated: Apr 22, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K

Bayesian nonparametric estimation of targeted agent effects on biomarker change to predict clinical outcome

Rebecca Graziani1, Michele Guindani2, Peter F Thall2

  • 1Università Commerciale Luigi Bocconi, Milan, Italy.

Biometrics
|October 17, 2014
PubMed

Insights

This study introduces a new statistical framework to analyze how targeted cancer therapies affect biomarkers and patient outcomes. It helps understand biomarker changes and their link to clinical results, improving treatment evaluation.

Area of Science:

  • Biostatistics
  • Translational Oncology
  • Biomarker Discovery

Background:

  • Targeted therapies aim to alter specific biological events in cancer patients, often measured by biomarkers.
  • Evaluating these therapies requires understanding biomarker distribution changes and their association with clinical outcomes.
  • Complex biomarker distributions and patient variability pose significant estimation challenges.

Purpose of the Study:

  • To develop a probabilistically coherent framework for modeling biomarker changes post-treatment.
  • To assess the relationship between biomarker profile shifts and clinical outcomes in cancer patients.
  • To provide a robust method for evaluating targeted agent efficacy.

Main Methods:

  • A hierarchical Bayesian nonparametric mixture model was developed for biomarker analysis.
  • A functional profile, analogous to the receiver operating characteristic curve, was defined for pre- and post-treatment biomarker distributions.
  • Patient biomarker profiles were clustered and used as covariates in clinical outcome regression models.

Main Results:

  • The framework successfully models complex biomarker distributions and their changes.
  • Individual patient biomarker profile functionals were clustered, revealing patient heterogeneity.
  • The biomarker profile was shown to be a useful covariate in predicting clinical outcomes.

Conclusions:

  • The proposed Bayesian nonparametric framework offers a coherent approach to analyzing targeted therapy effects on biomarkers and clinical outcomes.
  • This methodology enhances the understanding of biomarker dynamics and their impact on treatment efficacy.
  • The framework was validated using data from a prostate cancer trial targeting platelet-derived growth factor.
Keywords:
Bayesian nonparametricsBiomarker profilesNested Dirichlet processReceiving operating curveSurvival analysis

More Related Videos

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

3.8K
Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis07:40

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis

Published on: March 20, 2021

20.1K

Related Concept Videos

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...
775
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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,...
622
Measurement of Bioavailability: Pharmacodynamic Methods01:20

Measurement of Bioavailability: Pharmacodynamic Methods

Pharmacodynamic methods provide insights into a drug's effects on physiological processes over time and play a crucial role in understanding bioavailability and therapeutic efficacy. These methods can be broadly classified into acute pharmacological and therapeutic response approaches, each with distinct mechanisms and applications.The acute pharmacological response method directly correlates a drug's physiological effects, such as ECG or pupil diameter changes, to its time course in the body.
1.0K
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
114
Pharmacokinetic–Pharmacodynamic Relationship: Problems01:24

Pharmacokinetic–Pharmacodynamic Relationship: Problems

The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...
116
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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...
359