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

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:

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...

Hazard Ratio

Hazard Ratio

The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial evaluating a...

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...

Introduction to Epidemiology

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...

Study Designs in Epidemiology

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.

You might also read

Related Articles

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

Sort by

Same author

Long-Chain Fatty Acid Oxidation Disorder Genes: A Comprehensive Genetic Database of LC-FAOD Variants, Genotypes, and Phenotypes.

Human mutation·2026

Same author

The clinical utility of functional testing in fibroblasts to diagnose primary mitochondrial disease.

medRxiv : the preprint server for health sciences·2026

Same author

Gaucher Disease Treated With Lentiviral-Mediated Gene Therapy: First Case.

Journal of inherited metabolic disease·2026

Same author

Cumulative Antigen Suppression Reduces Clonal Plasma Cell Evolution in Gaucher Disease.

American journal of hematology·2026

Same author

Honoring Padma Shri Professor I.C. Verma - A Tribute from the Indian Medical Advisory Board (IMAB).

Indian journal of pediatrics·2026

Same author

The global impact of imiglucerase therapy in children with Gaucher disease types 1 and 3: a real-world analysis from the International Collaborative Gaucher Group Gaucher Registry.

Orphanet journal of rare diseases·2026

Search research articles

Related Experiment Video

Updated: May 29, 2026

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

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

Published on: January 8, 2020

Reducing selection bias in case-control studies from rare disease registries.

J Alexander Cole¹, John S Taylor, Thomas N Hangartner

¹Biomedical Data Sciences and Informatics, Genzyme, a Sanofi Company, 500 Kendall Street, Cambridge, MA 02142, USA.

Orphanet Journal of Rare Diseases

|September 14, 2011

Summary

Case-control matching using the risk-set method effectively minimizes selection bias in rare disease registries. This approach ensures comparable patient cohorts for more reliable data analysis in Gaucher disease research.

Related Experiment Videos

Last Updated: May 29, 2026

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

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

Published on: January 8, 2020

Area of Science:

Clinical Epidemiology
Rare Disease Research
Biostatistics

Background:

Rare disease registries are crucial for demographic and outcome data but susceptible to selection bias due to observational design.
Heterogeneity and small patient numbers in rare diseases limit traditional clinical trial designs.
Bias in registry data can negatively impact the validity of analyses.

Purpose of the Study:

To demonstrate the utility of case-control matching and the risk-set method for controlling bias in rare disease registry data.
To address the limitations of observational study designs in rare disease research.
To improve the validity of data analyses from rare disease registries.

Main Methods:

Case-control matching using the risk-set method was applied to data from the International Collaborative Gaucher Group (ICGG) Gaucher Registry.
Two groups were identified: patients with avascular osteonecrosis (AVN) and those without AVN.
Frequency distributions of gender, birth decade, treatment, and splenectomy status were compared before and after matching; odds ratios were calculated.

Main Results:

Case-control matching created comparable cohorts of patients with and without AVN based on gender, age, treatment, and splenectomy status.
The matching process resulted in odds ratios close to 1.00, indicating the successful minimization of bias.
This demonstrates the effectiveness of the risk-set method in controlling for confounding variables.

Conclusions:

Case-control selection bias was demonstrated in a rare disease registry and effectively minimized using case-control matching.
The risk-set method is a valuable approach for analyzing data from rare disease registries.
This methodology enhances the study of heterogeneous patient cohorts in rare disease research.