Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

Bias in Epidemiological Studies

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:

Halo Effect

Halo Effect

The halo effect is a cognitive bias in which an individual's overall impression influences judgments about their specific traits. This psychological phenomenon leads people to associate positive characteristics with those they perceive as generally good and negative characteristics with those they view as bad. This effect is particularly influential in social perception, professional evaluations, and decision-making processes.The Psychological Basis of the Halo EffectThe halo effect is rooted...

Bias

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...

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

Confounding in Epidemiological Studies

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...

You might also read

Related Articles

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

Sort by

Same author

Long-term effectiveness and overdiagnosis in the NHS breast screening programme: a cohort study with up to 33 years of follow-up.

British journal of cancer·2026

Same author

Comparative cost-effectiveness of screening for lung cancer using different risk scores and thresholds: the Yorkshire Lung Screening Trial.

Thorax·2026

Same author

Individualised progesterone receptor modulator prevention strategies for triple-negative breast cancer in BRCA1 pathogenic variant carriers.

NPJ breast cancer·2026

Same author

Targeted Prostate Health Checks for prostate cancer in men at high risk: a feasibility pilot in primary and secondary care in North East London.

BJGP open·2026

Same author

Transforming endometrial cancer diagnosis: the case for molecular triage in abnormal uterine and post-menopausal bleeding.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society·2026

Same author

Response to: Adjusting for non-compliance and contamination with a more plausible assumption.

Journal of medical screening·2026

Search research articles

Related Experiment Video

Updated: Jul 15, 2026

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

Avoiding bias from aggregate measures of exposure.

Stephen W Duffy¹, Håkan Jonsson, Olorunsola F Agbaje

¹Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Charterhouse Square, London EC1M 6BQ, UK.

Journal of Epidemiology and Community Health

|April 17, 2007

Summary

Log-linear regression models can inaccurately estimate individual-level health effects when using exposure proportions. Excess relative risk models provide a more accurate method for assessing risk factor impact in epidemiological studies.

Related Experiment Videos

Last Updated: Jul 15, 2026

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

Area of Science:

Epidemiology
Biostatistics
Public Health

Background:

Proportions exposed to risk factors or interventions are sometimes used in log-linear regressions for disease incidence or mortality.
This approach is common in descriptive epidemiology and health policy evaluation.

Purpose of the Study:

To demonstrate the potential for substantial inaccuracies in individual-level effect estimates derived from log-linear regression models.
To illustrate the correct estimation of individual-level effects using excess relative risk models.

Main Methods:

The study utilizes data on prostate-specific antigen (PSA) testing and prostate cancer incidence.
Log-linear regression and excess relative risk models are employed for analysis.

Main Results:

Log-linear regression models using exposure proportions can yield inaccurate individual-level effect estimates.
Excess relative risk models provide a more accurate assessment of individual-level risk factor effects.

Conclusions:

Standard log-linear regression approaches may misestimate the true impact of risk factors or interventions at the individual level.
Excess relative risk modeling offers a superior alternative for accurate individual-level effect estimation in epidemiological research.