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

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

233
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...
233
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

339
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
339
Bias01:22

Bias

4.3K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
4.3K
Randomized Experiments01:13

Randomized Experiments

7.0K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.0K
Random and Systematic Errors01:20

Random and Systematic Errors

11.0K
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...
11.0K
Censoring Survival Data01:09

Censoring Survival Data

125
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
125

You might also read

Related Articles

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

Sort by
Same author

Weighted cumulative sum tests for random effect models with binary responses.

Statistical methods in medical research·2019
Same author

Goodness of fit tests for random effect models with binary responses.

Statistics in medicine·2018
See all related articles

Related Experiment Video

Updated: Jul 15, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Bias correction in random effects models with sparse binary responses.

Antonia K Korre1, Vassilis Gs Vasdekis1

  • 1Department of Statistics, Athens University of Economics and Business, Athens, Greece.

Statistical Methods in Medical Research
|September 30, 2023
PubMed
Summary

This study addresses sparse correlated binary data using a logit random effects model. An adjusted h-likelihood estimation corrects bias in random effects, improving fixed effects estimates for rare events and small samples.

Keywords:
Sparse binary databias correctioncalibration approachh-likelihoodrandom intercept models

More Related Videos

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
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.2K

Related Experiment Videos

Last Updated: Jul 15, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K
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
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.2K

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Sparse correlated binary data present challenges in statistical modeling, particularly with rare events or small sample sizes.
  • Logit random effects models are commonly used but can be sensitive to data sparseness.
  • Existing estimation methods may yield biased results under sparse conditions.

Purpose of the Study:

  • To propose an adjusted h-likelihood estimation method for logit random effects models with sparse correlated binary data.
  • To correct for bias in random effects estimates arising from data sparseness.
  • To improve the properties of fixed effects estimates in such models.

Main Methods:

  • Utilized a logit random effects model framework.
  • Adapted the regression calibration method for random effects estimation.
  • Developed an adjusted h-likelihood estimation procedure.
  • Conducted simulation studies across varying levels of data sparseness.

Main Results:

  • The proposed adjustment effectively corrects bias in random effects estimates.
  • Improved properties were observed for fixed effects estimates.
  • Simulation results demonstrated the method's efficacy under different sparseness levels.
  • The adjusted method was successfully applied to two real meta-analysis datasets.

Conclusions:

  • The adjusted h-likelihood estimation offers a robust approach for analyzing sparse correlated binary data.
  • This method enhances the reliability of parameter estimates in logit random effects models.
  • It provides a valuable tool for applications involving rare events or limited data.