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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
Standard Error of the Mean01:13

Standard Error of the Mean

The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
Introduction to Normal Distributions01:29

Introduction to Normal Distributions

Standardized test scores often follow a symmetric distribution that can be modeled with the normal distribution, a fundamental concept in statistics. This distribution is particularly useful for interpreting test performance fairly across populations, as it provides a mathematical framework for understanding variability and central tendency in large datasets.From Histogram to Frequency DistributionRaw test data are often displayed using histograms, where the height of each bar represents the...
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)...
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Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...

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Related Experiment Video

Updated: Jul 8, 2026

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

Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: the npde add-on

Emmanuelle Comets1, Karl Brendel, France Mentré

  • 1INSERM, U738, Paris, France. emmanuelle.comets@bichat.inserm.fr

Computer Methods and Programs in Biomedicine
|January 25, 2008
PubMed
Summary

Normalised prediction distribution errors (npde) offer a robust method for evaluating pharmacokinetic/pharmacodynamic models. This R package computes npde, which should follow a standard normal distribution for accurate model assessment.

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Last Updated: Jul 8, 2026

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

Area of Science:

  • Pharmacometrics
  • Statistical Modeling
  • Computational Biology

Background:

  • Pharmacokinetic/pharmacodynamic (PK/PD) data analysis commonly employs nonlinear mixed-effect models.
  • Model evaluation is crucial but often overlooked in PK/PD analyses.
  • Normalised prediction distribution errors (npde) have emerged as a promising tool for model assessment.

Purpose of the Study:

  • To introduce and describe an R add-on package for computing npde.
  • To provide a practical tool for evaluating nonlinear mixed-effect models used in PK/PD analysis.
  • To demonstrate the utility of npde in assessing model fit to data.

Main Methods:

  • Development of an R package to calculate npde.
  • Utilizing the full predictive distribution of observations, including multiple within-subject data points.
  • Simulating datasets under different model assumptions for validation.

Main Results:

  • The npde package computes npde, which are expected to follow a standard normal distribution under the null hypothesis of a correct model.
  • Demonstrated application of the package using simulated datasets, one with correct parameters and one with incorrect parameters.
  • The npde method effectively distinguishes between adequately and inadequately describing models.

Conclusions:

  • The developed R package provides an effective method for computing npde, enhancing model evaluation in PK/PD studies.
  • npde serves as a valuable tool for assessing the goodness-of-fit of nonlinear mixed-effect models.
  • Proper model evaluation using tools like npde is essential for reliable PK/PD data analysis.