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Related Concept Videos

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...
Cause and Effect01:53

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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:
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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...
Introduction to R01:11

Introduction to R

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's functionality,...

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Updated: Jun 5, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Data linkage: a powerful research tool with potential problems.

Megan A Bohensky1, Damien Jolley, Vijaya Sundararajan

  • 1Centre of Research Excellence in Patient Safety, Dept of Epidemiology & Preventive Medicine, School Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, Australia. megan.bohensky@monash.edu

BMC Health Services Research
|December 24, 2010
PubMed
Summary
This summary is machine-generated.

Patient characteristics like age, sex, race, and health status can affect data linkage accuracy. Incomplete data linkage may lead to biased clinical outcomes, impacting research validity.

Related Experiment Videos

Last Updated: Jun 5, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Area of Science:

  • Health Informatics
  • Biostatistics
  • Clinical Research Methodology

Background:

  • Growing interest in multi-source data linkage for clinical performance and patient outcomes measurement.
  • Sub-optimal or incomplete data linkage can compromise utility and introduce systematic bias.
  • Identifying factors influencing data linkage validity is crucial for reliable health outcome reporting.

Purpose of the Study:

  • To synthesize evidence on participant or population characteristics affecting data linkage validity and completeness.
  • To identify factors associated with systematic bias in reported outcomes due to incomplete data linkage.

Main Methods:

  • A narrative review using structured search methods.
  • Searched Medline, EMBASE, and CINAHL databases (1991-2007) for "data linkage" and "medical record linkage".
  • Included studies empirically evaluated linkage issues, reported patient characteristics, analyzed matched vs. unmatched records, and were in English.

Main Results:

  • 33 out of 1810 articles met inclusion criteria, showing marked heterogeneity.
  • Key characteristics unevenly distributed between matched and unmatched records included: age (72%), race (64%), socio-economic status (82%), and health status (72%).
  • Geographical/hospital site (93%) and sex (50%) were also significant factors.

Conclusions:

  • Patient and population factors significantly influence data linkage completeness and validity.
  • Incomplete data linkage can result in systematic bias in reported clinical outcomes.
  • Researchers and readers must consider these influencing factors when interpreting data linkage study results.