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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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 squares (OLS)...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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,...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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...

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A Bayesian Modeling Approach to Optimize Longitudinal Biomarker Sampling Schedules Using Hormonal Data.

Monica H Keith1,2, Margaret Corley3,4, Delaney J Glass5,6

  • 1Department of Anthropology, Vanderbilt University, Nashville, Tennessee, USA.

American Journal of Human Biology : the Official Journal of the Human Biology Council
|July 6, 2026
PubMed
Summary
This summary is machine-generated.

Optimizing biomarker sampling requires careful consideration of frequency. This Bayesian approach reveals that more frequent sampling does not always improve parameter precision, identifying specific thresholds for optimal data collection.

Keywords:
Bayesianbiomarkermodelingoptimizationsampling

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Area of Science:

  • Biostatistics
  • Endocrinology
  • Pharmacokinetics

Background:

  • Longitudinal biomarker sampling schedules are critical for study objectives.
  • Understanding intraindividual, interindividual, and population-level biomarker variation is key.
  • Bayesian modeling offers a framework for optimizing sampling strategies.

Purpose of the Study:

  • To develop and apply a Bayesian modeling approach for evaluating biomarker variation.
  • To optimize precision in parameter estimates for longitudinal biomarker data.
  • To inform biomarker sampling decisions based on data characteristics.

Main Methods:

  • Applied Bayesian linear and nonlinear mixed-effects models to longitudinal hormone data from 35 pubertal girls.
  • Systematically downsampled data from annual, biannual, and quarterly intervals with varying replicates.
  • Evaluated precision in parameter estimates across nine distinct sampling frequencies.

Main Results:

  • Sampling frequency significantly impacts model parameters at individual and population levels.
  • Increased total sample size did not consistently improve parameter estimate precision.
  • Identified optimal sampling thresholds beyond which precision decreased with additional measures.

Conclusions:

  • The study provides tools for Bayesian model building, evaluation, and sampling decisions.
  • Biomarker-specific features distinctly influence precision.
  • The presented hormone modeling methods are broadly applicable to diverse biological data.