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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...
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:
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Data Collection by Survey01:07

Data Collection by Survey

The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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...
Archival Research01:40

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...

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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Bias analysis to guide new data collection.

Timothy L Lash1, Thomas P Ahern

  • 1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus.

The International Journal of Biostatistics
|April 14, 2012
PubMed
Summary
This summary is machine-generated.

Bias analysis helps quantify uncertainty and guides new data collection strategies in epidemiology. It revealed that further CYP2D6 genotyping was needed for tamoxifen resistance studies, but not for vitamin K antagonist cancer studies.

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Area of Science:

  • Epidemiologic data analysis
  • Biostatistics
  • Pharmacogenomics

Background:

  • Bias analysis is crucial for quantifying uncertainty from systematic errors and refining hypotheses in epidemiology.
  • A key utility of bias analysis is identifying productive strategies for new data collection to validate associations.

Purpose of the Study:

  • To illustrate the value of bias analysis in guiding new data collection strategies using two distinct examples.
  • To assess the need for comprehensive CYP2D6 genotyping in tamoxifen resistance research.
  • To evaluate the utility of bias analysis in cancer incidence studies involving vitamin K antagonist therapy.

Main Methods:

  • Bias analysis was employed to evaluate potential explanations for observed null associations.
  • Example 1: Assessed the impact of incomplete CYP2D6 genotyping on tamoxifen resistance and breast cancer recurrence.
  • Example 2: Investigated non-differential misclassification of vitamin K antagonist therapy using instrumental variables for cancer incidence.

Main Results:

  • Bias analysis indicated that comprehensive CYP2D6 genotyping was necessary to resolve uncertainty in the tamoxifen study.
  • In the vitamin K antagonist study, bias analysis suggested that new data collection would likely not be a productive use of resources due to potential misclassification.

Conclusions:

  • Bias analysis effectively guides decisions on resource allocation for new data collection in epidemiologic research.
  • The utility of further investigation depends on the specific research question and potential sources of bias.