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

Regression Toward the Mean01:52

Regression Toward the Mean

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 researchers try to extrapolate results...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Bias01:22

Bias

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...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Odds Ratio01:09

Odds Ratio

The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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:

You might also read

Related Articles

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

Sort by
Same author

Comparative effectiveness of alternative times to opioid agonist treatment taper initiation on taper completion and all-cause mortality among people with opioid use disorder: A retrospective population-based target trial emulation study in British Columbia, Canada, 2010-2020.

Addiction (Abingdon, England)·2026
Same author

Negatives about positivity and consistency as conditions for causal inference.

American journal of epidemiology·2026
Same author

Efficacy of a smartphone game to increase age and condom use at first sex among adolescents in Kenya (Tumaini): a randomised controlled trial.

The Lancet. Child & adolescent health·2026
Same author

Longitudinal Effects of a Smartphone Game (Tumaini) for HIV Prevention Among Kenyan Adolescents: 45-Month Trajectories of Condom Use-Related Proximal Outcomes From a Randomized Controlled Trial.

Journal of medical Internet research·2026
Same author

Effectiveness of slow-release oral morphine versus other OAT regimens in key sub-populations: protocol for population-based target trial emulation.

medRxiv : the preprint server for health sciences·2026
Same author

Improving Access to HIV Prevention Services in Community Pharmacies in the US Southeast: Protocol for a Hybrid Type 1 Effectiveness-Implementation Study.

JMIR research protocols·2025

Related Experiment Video

Updated: Jul 10, 2026

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

Reducing Bias and Mean Squared Error Associated With Regression-Based Odds Ratio Estimators.

Robert H Lyles1, Ying Guo, Sander Greenland

  • 1Department of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Atlanta, GA 30322 phone: 404-727-1310; fax: 404-727-1370.

Journal of Statistical Planning and Inference
|September 11, 2012
PubMed
Summary

We developed bias-corrected ratio estimators for regression models, significantly reducing bias and improving accuracy. These new methods offer better estimates for odds and risk ratios in statistical analysis.

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

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

Related Experiment Videos

Last Updated: Jul 10, 2026

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

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

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

Area of Science:

  • Biostatistics
  • Statistical modeling
  • Epidemiology

Background:

  • Maximum-likelihood estimators (MLEs) of log-linear or logistic regression coefficients are commonly exponentiated to estimate effect ratios.
  • These standard estimators can exhibit significant positive finite-sample bias.

Purpose of the Study:

  • To propose a novel bias correction method for exponentiated regression coefficients.
  • To reduce bias in odds ratio and risk ratio estimators.
  • To develop estimators with improved bias, variance, and mean squared error.

Main Methods:

  • A simple correction is introduced to mitigate bias from exponentiation.
  • This correction is combined with log-scale bias correction for enhanced accuracy.
  • A class of estimators is proposed offering controlled risk of underestimation.

Main Results:

  • The proposed correction substantially reduces exponentiation-induced bias.
  • Second-order bias in odds ratio estimators is completely removed in specific cases.
  • Simulation studies show reduced bias, variance, and mean squared error compared to standard MLEs.
  • Bootstrapping further enhances precision, yielding narrower confidence intervals with maintained coverage.

Conclusions:

  • The proposed bias correction methods provide more accurate ratio estimators in regression settings.
  • These methods are applicable to odds and risk ratio estimation in various common regression models.
  • The developed estimators offer a favorable balance between bias reduction and controlled estimation risk.