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

Bootstrapping01:24

Bootstrapping

860
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
860
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

311
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...
311
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

375
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...
375
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

664
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
664
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.2K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.7K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.7K

You might also read

Related Articles

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

Sort by
Same author

Use of Metabotyping to Identify Individuals With Different Triglyceride Response Curves After Intake of High-Fat Meals.

Molecular nutrition & food research·2026
Same author

Association between dietary protein intake and frailty index among home-dwelling older adults; an 8-year follow-up study.

The journal of nutrition, health & aging·2026
Same author

Correction: Clinical dietitian-led nutrition counseling and exercise to reduce cardiovascular risk in adults living with a BMI above 27 and severe mental illness: the NORMI-Heart trial protocol.

Frontiers in nutrition·2026
Same author

Effects of an individualized dietitian-led nutrition intervention on body composition during the first year after traumatic spinal cord injury: A randomized controlled trial.

Clinical nutrition ESPEN·2026
Same author

Clinical dietitian-led nutrition counseling and exercise to reduce cardiovascular risk in adults living with a BMI above 27 and severe mental illness: the NORMI-Heart trial protocol.

Frontiers in nutrition·2026
Same author

Comparison of mortality and causes of death between treatment-engaged individuals with severe alcohol or opioid use disorder: a prospective 19-year clinical cohort study.

BMC psychiatry·2026

Related Experiment Video

Updated: Mar 1, 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

3.8K

Model-based bootstrapping when correcting for measurement error with application to logistic regression.

John P Buonaccorsi1, Giovanni Romeo2, Magne Thoresen2

  • 1Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts, U.S.A.

Biometrics
|May 31, 2017
PubMed
Summary
This summary is machine-generated.

Measurement error in regression models causes bias. New model-based bootstrapping methods improve bias correction and inference in logistic regression, offering advantages over standard techniques.

Keywords:
BootstrapLogistic regressionMeasurement errorReplication

More Related Videos

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

10.9K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Related Experiment Videos

Last Updated: Mar 1, 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

3.8K
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

10.9K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Modeling

Background:

  • Measurement error in predictors biases regression coefficients and inferences.
  • Existing correction methods face challenges with standard errors, confidence intervals, and residual bias.
  • The bootstrap is a potential solution but has limitations, especially for non-linear models like logistic regression.

Purpose of the Study:

  • To develop novel model-based bootstrapping methods for correcting measurement error in logistic regression.
  • To address challenges in bias estimation and robustness not typically found in standard regression models.
  • To compare the performance of new model-based bootstrap methods against simple bootstrap and other standard techniques.

Main Methods:

  • Development of new model-based bootstrapping techniques tailored for logistic regression with measurement error.
  • Application of the methodology to real-world data through two illustrative examples.
  • Conducting simulation studies to rigorously assess and compare various bootstrap and standard correction methods.

Main Results:

  • Model-based bootstrapping demonstrates potential for estimating bias in logistic regression models with measurement error.
  • The proposed methods show distinct improvements compared to simple bootstrap and other standard approaches.
  • While not universally perfect, model-based bootstrapping offers enhanced accuracy and reliability.

Conclusions:

  • New model-based bootstrapping methods provide a valuable advancement for handling measurement error in logistic regression.
  • These techniques offer improved bias correction and more reliable inference, particularly in complex models.
  • Further research and application of model-based bootstrapping are warranted for robust statistical analysis.