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

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.1K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

7.0K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
7.0K
Multiple Regression01:25

Multiple Regression

3.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.3K
Correlation and Regression00:53

Correlation and Regression

3.8K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.8K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Binomial Probability Distribution01:15

Binomial Probability Distribution

13.1K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
13.1K

You might also read

Related Articles

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

Sort by
Same author

Prenatal and childhood exposure to perfluoroalkyl substances and fat distribution in 9-year-old children: A prospective cohort study in the Faroe Islands.

Environmental research·2026
Same author

Bystander interventions and survival after out-of-hospital cardiac arrest according to neighborhood ethnicity.

Resuscitation·2026
Same author

Risk of Porcine reproductive and respiratory syndrome virus introduction into Danish pig farms: a register-based study.

Porcine health management·2026
Same author

Comparing disparities in geographic proximity to exercise-based cardiac rehabilitation before and after decentralisation of services: a repeated cross-sectional study using individual-level register data.

International journal for equity in health·2025
Same author

Diagnostic performance of upper airway sampling sites for SARS-CoV-2 and influenza testing.

Microbiology spectrum·2025
Same author

Adding a tonsil specimen to a throat swab does not improve the detection rate of SARS-CoV-2 - a multicenter randomized controlled trial.

Diagnostic microbiology and infectious disease·2025

Related Experiment Video

Updated: Apr 28, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K

Spatial correlation in Bayesian logistic regression with misclassification.

Kristine Bihrmann1, Nils Toft1, Søren Saxmose Nielsen1

  • 1Faculty of Medical and Health Sciences, University of Copenhagen, Grønnegårdsvej 8, DK-1870 Frederiksberg C, Denmark.

Spatial and Spatio-Temporal Epidemiology
|June 4, 2014
PubMed
Summary

Adjusting logistic regression for outcome misclassification is crucial for unbiased estimates. Bayesian models with spatial correlation, particularly ICAR(ρ), offer improved performance in handling such data.

Keywords:
Bayesian logistic regressionConditional autoregressive modelsMisclassificationSlice samplingSpatial correlation

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K

Related Experiment Videos

Last Updated: Apr 28, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K

Area of Science:

  • Biostatistics
  • Spatial Epidemiology
  • Statistical Modeling

Background:

  • Standard logistic regression assumes perfect outcome measurement, which is often violated in real-world data.
  • Outcome misclassification can introduce bias into regression estimates if not properly addressed.
  • Spatial correlation is a common feature in many datasets, further complicating analysis.

Purpose of the Study:

  • To present Bayesian logistic regression models that adjust for outcome misclassification in the presence of spatial correlation.
  • To evaluate the performance of different spatial models, including fixed effects, independent random effects, and conditional autoregressive (CAR) models.
  • To identify the most effective model for handling misclassified and spatially correlated data.

Main Methods:

  • Development and application of Bayesian logistic regression models incorporating outcome misclassification adjustment.
  • Implementation of spatially correlated random effects using intrinsic conditional autoregressive (ICAR) and ICAR with a spatial autocorrelation parameter (ICAR(ρ)) priors.
  • Parameter estimation using Markov Chain Monte Carlo (MCMC) methods, with slice sampling employed for enhanced convergence.
  • Performance evaluation through a comprehensive simulation study.

Main Results:

  • Adjustment for outcome misclassification is essential for obtaining unbiased regression estimates.
  • The ICAR model demonstrated superior performance when spatial correlation was strong.
  • The ICAR(ρ) model exhibited the best performance under weak or moderate spatial correlation.
  • The ICAR(ρ) model is recommended for situations with unknown spatial correlation, provided convergence is achieved.

Conclusions:

  • Accurate statistical modeling requires accounting for both outcome misclassification and spatial correlation.
  • The choice of spatial model (ICAR vs. ICAR(ρ)) depends on the strength of the spatial correlation.
  • The Bayesian framework provides a flexible approach to model complex data structures, including misclassification and spatial dependencies.