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

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.5K
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:  
1.5K
Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

1.5K
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
1.5K
Bias01:22

Bias

7.9K
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...
7.9K
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

1.3K
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...
1.3K
Introduction to Epidemiology01:26

Introduction to Epidemiology

2.3K
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,...
2.3K
Methods of Documentation I: Source-Oriented Records01:18

Methods of Documentation I: Source-Oriented Records

1.8K
Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
1.8K

You might also read

Related Articles

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

Sort by
Same author

Pregnancy intentions by sexual orientation among pregnancies across the life course.

American journal of epidemiology·2026
Same author

Social network characteristics and cognitive function, decline, and mortality: A joint modeling approach.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

A Pilot Study of Plasma Cell-Free DNA Fragmentomics in Gallbladder Cancer.

JHEP reports : innovation in hepatology·2026
Same author

Fathers' adverse childhood experiences and children's behavior problems.

American journal of preventive medicine·2026
Same author

Improvements in Cogstate Test Performance Depend on Number and Frequency of Prior Tests: Evidence from a Randomized Follow-Up Design.

Alzheimer disease and associated disorders·2026
Same author

Gabapentinoid Polypharmacy Among Medicare Beneficiaries During the Poststroke Recovery Period.

Health services research·2026

Related Experiment Video

Updated: Mar 14, 2026

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
09:00

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

5.5K

A General Framework for Considering Selection Bias in EHR-Based Studies: What Data Are Observed and Why?

Sebastien Haneuse1, Michael Daniels2

  • 1Harvard T.H. Chan School of Public Health.

EGEMS (Washington, DC)
|September 27, 2016
PubMed
Summary

Electronic health records (EHR) offer cost-effective comparative effectiveness research (CER). A new framework addresses selection bias in EHR data, improving transparency and statistical methods for missing data analysis.

Keywords:
2014 Group Health Seattle SymposiumComparative Effectiveness Research (CER)Electronic Health Record (EHR)MethodsMissing DataSelection Bias

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.2K
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

15.4K

Related Experiment Videos

Last Updated: Mar 14, 2026

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
09:00

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

5.5K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.2K
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

15.4K

Area of Science:

  • Health Informatics
  • Biostatistics
  • Comparative Effectiveness Research

Background:

  • Electronic health records (EHR) are valuable for cost-effective comparative effectiveness research (CER).
  • EHR data present methodologic challenges for CER, including confounding and selection bias due to missing data.
  • Existing literature primarily addresses confounding bias, with limited focus on selection bias in EHR-based CER.

Purpose of the Study:

  • To propose a novel general framework for addressing selection bias in EHR-based CER.
  • To provide structure for analyzing the complex interplay of patient/provider decisions and EHR system variability.
  • To enhance transparency of missing data assumptions and align statistical methods with data complexity.

Main Methods:

  • Development of a new conceptual framework for selection bias in EHR data.
  • Consideration of decision-making processes by patients and healthcare providers influencing data recording.
  • Analysis of variability across different EHR systems.

Main Results:

  • The proposed framework structures the analysis of selection bias in EHR data.
  • It facilitates enhanced transparency regarding assumptions about missing data.
  • It enables better alignment of statistical methods with the intricate nature of EHR data.

Conclusions:

  • A structured approach is needed to effectively manage selection bias in EHR-based CER.
  • The framework aids researchers in understanding and mitigating bias arising from data collection and system variability.
  • Improved methods are crucial for reliable EHR-based comparative effectiveness research.