Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...

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

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

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...

Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

Statistical Analysis: Overview

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

You might also read

Related Articles

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

Sort by

Same author

Smooth deconvolution of low-field NMR signals.

Analytica chimica acta·2024

Same author

Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data.

iScience·2023

Same author

Multidimensional adaptive P-splines with application to neurons' activity studies.

Biometrics·2022

Same author

Fast and simple super-resolution with single images.

Scientific reports·2022

Same author

Smoothness correction for better SOFI imaging.

Scientific reports·2021

Same author

Spatially Regularized Shape Analysis of the Hippocampus Using P-Spline Based Shape Regression.

IEEE journal of biomedical and health informatics·2019

Search research articles

Related Experiment Video

Updated: Jul 17, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Data exploration in meta-analysis with smooth latent distributions.

Paul H C Eilers¹

¹Department of Medical Statistics, Leiden University Medical Centre, P.O. Box 9604, RC Leiden 2300, The Netherlands. p.eilers@lumc.nl

Statistics in Medicine

|January 12, 2007

Summary

This study presents an efficient EM algorithm for meta-analysis with discrete outcomes. It estimates a smooth distribution of event probabilities, aiding data exploration and treatment effect analysis.

More Related Videos

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors

Published on: July 28, 2023

Arteriovenous Metabolomics to Measure In Vivo Metabolite Exchange in Brown Adipose Tissue

Arteriovenous Metabolomics to Measure In Vivo Metabolite Exchange in Brown Adipose Tissue

Published on: October 6, 2023

Related Experiment Videos

Last Updated: Jul 17, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors

Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors

Published on: July 28, 2023

Arteriovenous Metabolomics to Measure In Vivo Metabolite Exchange in Brown Adipose Tissue

Arteriovenous Metabolomics to Measure In Vivo Metabolite Exchange in Brown Adipose Tissue

Published on: October 6, 2023

Area of Science:

Biostatistics
Medical Informatics
Epidemiology

Background:

Meta-analysis is crucial for synthesizing evidence from multiple studies.
Discrete outcomes are common in clinical trials and epidemiological research.
Existing methods may not fully capture the underlying distribution of event probabilities.

Purpose of the Study:

To develop an efficient algorithm for meta-analysis with discrete outcomes.
To estimate a non-parametric smooth latent distribution of event probabilities.
To facilitate data exploration and computation of treatment effect statistics.

Main Methods:

Utilizes an Expectation-Maximization (EM) algorithm.
Employs a fine grid for estimation.
Applies penalized least squares for fast smoothing.
Handles one or two-dimensional latent distributions.

Main Results:

The proposed EM algorithm is simple and efficient.
Fast smoothing is achieved through penalized least squares.
The method effectively estimates the latent distribution of event probabilities.
The estimated distribution aids in data exploration.

Conclusions:

The developed method provides a robust approach for meta-analysis with discrete outcomes.
It enhances the understanding of event probability distributions.
The approach is valuable for both exploratory analysis and statistical inference of treatment effects.