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

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

83.6K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
83.6K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

2.7K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
2.7K
Contaminants and Errors01:16

Contaminants and Errors

147
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
147
Random Error01:04

Random Error

2.0K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
2.0K
Random and Systematic Errors01:20

Random and Systematic Errors

12.7K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
12.7K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.0K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.0K

You might also read

Related Articles

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

Sort by
Same author

Interval Cancers after Negative Screening Contrast-enhanced Mammography.

Radiology. Imaging cancer·2026
Same author

Abbreviated MRI for the Evaluation of Treatment Response in Patients Undergoing Neoadjuvant Chemotherapy for Breast Cancer.

Radiology·2026
Same author

An Improved Misclassification Simulation Extrapolation (MC-SIMEX) Algorithm.

Statistics in medicine·2026
Same author

Economic and Humanistic Burden of Multimorbidity in the United States: A Longitudinal Study of Expenditure and Quality of Life Trajectories, 2019-2022.

International journal of environmental research and public health·2025
Same author

Adaptive Breast MRI Scanning Using AI.

Radiology·2025
Same author

Hormonal Regulation of Background Parenchymal Enhancement at Contrast-enhanced Mammography.

Radiology·2025
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
Same journal

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.

Statistical methods in medical research·2026
Same journal

Empirical likelihood inference for the area under the receiver operating characteristic (ROC) curve with verification biased data.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Sep 22, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

Simulation extrapolation method for measurement error: A review.

Varadan Sevilimedu1, Lili Yu2

  • 1Department of Epidemiology and Biostatistics, 5803Memorial Sloan Kettering Cancer Center, Manhattan, New York, USA.

Statistical Methods in Medical Research
|May 24, 2022
PubMed
Summary
This summary is machine-generated.

Measurement error in statistics can bias results. This review focuses on the simulation extrapolation method for correcting continuous measurement error models, highlighting its development and application.

Keywords:
Measurement errorbiasepidemiologyextrapolationsimulation extrapolationsurvival

More Related Videos

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.6K
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

3.4K

Related Experiment Videos

Last Updated: Sep 22, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.6K
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

3.4K

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Measurement error is a common issue in statistical analysis, arising from data collection limitations.
  • This error can lead to biased estimates, reduced statistical power, and inaccurate confidence intervals in regression models.
  • Existing literature offers broad reviews of measurement error correction but lacks a specific focus on simulation extrapolation.

Purpose of the Study:

  • To provide a comprehensive review exclusively focused on the simulation extrapolation (SIMEX) method.
  • To highlight the development and application of SIMEX in continuous measurement error models over the last 25 years.
  • To address the gap in literature regarding detailed reviews of SIMEX.

Main Methods:

  • The review synthesizes existing literature on measurement error correction techniques.
  • It specifically details the simulation extrapolation (SIMEX) method, applicable to both functional and structural measurement error models.
  • The focus is on continuous measurement error models.

Main Results:

  • The simulation extrapolation (SIMEX) method offers a practical approach for partially correcting measurement error.
  • SIMEX is adaptable to various statistical models, including functional and structural types.
  • The review underscores the method's widespread use and ease of application in continuous data.

Conclusions:

  • The simulation extrapolation (SIMEX) method is a valuable tool for addressing measurement error in statistical modeling.
  • This review provides a focused examination of SIMEX's evolution and utility, particularly for continuous variables.
  • Further research and application of SIMEX are encouraged to mitigate the impact of measurement error in statistical analyses.