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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

216
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...
216
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

463
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
463
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

247
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
247
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

213
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...
213
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.1K
Quadratic Models01:23

Quadratic Models

146
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
146

You might also read

Related Articles

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

Sort by
Same author

Assessing Ecological Connectivity for <i>Loxodonta africana</i> Across Transfrontier Conservation Areas in Southern Mozambique.

Ecology and evolution·2026
Same author

A Comparison of Skin Lesions' Diagnoses Between AI-Based Image Classification, an Expert Dermatologist, and a Non-Expert.

Diagnostics (Basel, Switzerland)·2025
Same author

A Regularized MANOVA Test for Semicontinuous High-Dimensional Data.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

Large Italian Multicenter Study on Prognostic Value of Baselines Variables in mCRPC Patients Treated with <sup>223</sup>RaCl<sub>2</sub>: Ten Years of Clinical Experience.

Diagnostics (Basel, Switzerland)·2025
Same author

The association between surgical complications and compliance to the World Health Organization Surgical Safety Checklist: A retrospective analysis of hospital records.

Journal of evaluation in clinical practice·2024
Same author

Interpregnancy interval and adverse perinatal outcomes: A within-individual comparative method.

Health science reports·2024
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jan 3, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K

Multistate quantile regression models.

Alessio Farcomeni1, Marco Geraci2

  • 1Department of Economics and Finance, University of Rome "Tor Vergata", Rome, Italy.

Statistics in Medicine
|November 20, 2019
PubMed
Summary
This summary is machine-generated.

We developed new regression methods for analyzing time-to-event data in complex multistate processes. Our approach accurately estimates conditional quantiles, outperforming existing methods in simulations and real-world infection data analysis.

Keywords:
censored quantilescross-infectionduration models

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.4K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.7K

Related Experiment Videos

Last Updated: Jan 3, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.4K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.7K

Area of Science:

  • Biostatistics
  • Stochastic Processes
  • Medical Statistics

Background:

  • Analyzing time-to-event data in multistate processes is complex.
  • Existing methods for censored quantile regression or competing risks have limitations.

Purpose of the Study:

  • To develop novel regression methods for inference on conditional quantiles of time-to-transition in multistate processes.
  • To address limitations in current methods for survival, recurrent event, semicompeting, and competing risk data.

Main Methods:

  • Utilized an ad hoc representation of the underlying stochastic process.
  • Applied methods for censored quantile regression tailored for multistate processes.
  • Conducted simulation studies to evaluate finite sample performance.

Main Results:

  • The proposed approach demonstrated superior finite sample performance compared to naive censored quantile regression and competing risk methods.
  • The methods accurately handled censored data and dependencies between states.
  • A quantile-dependent effect of catheterization on time to hospital-acquired infection was identified in cirrhotic patients.

Conclusions:

  • The developed regression methods provide a robust tool for analyzing time-to-transition in complex multistate processes.
  • The approach offers improved accuracy and performance over existing methods, particularly in the presence of censoring and competing risks.
  • The findings highlight the utility of these methods in clinical research, as demonstrated by the analysis of hospital-acquired infections.