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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:
Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Hindsight Biases01:12

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...

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Updated: May 15, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Index event bias-a numerical example.

Luc J M Smits1, Sander M J van Kuijk, Pieter Leffers

  • 1Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Maastricht University Medical Centre, Peter Debyeplein 1, 6229 HA Maastricht, The Netherlands. luc.smits@epid.unimaas.nl

Journal of Clinical Epidemiology
|December 22, 2012
PubMed
Summary
This summary is machine-generated.

Index event bias can distort studies on recurrent disease. This bias, caused by selecting patients with prior disease, can make risk factors appear unrelated or less influential than they truly are.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Medical Research Methodology

Background:

  • Recurrent disease studies may yield unexpected results, with established risk factors appearing less influential.
  • Patient selection based on prior disease episodes is a suspected cause of these paradoxical findings.
  • This selection bias has been termed 'index event bias'.

Purpose of the Study:

  • To provide a theoretical, quantitative example of index event bias.
  • To demonstrate how patient selection impacts the observed association between risk factors and recurrent disease.

Main Methods:

  • Development of a theoretical model to illustrate index event bias.
  • Quantitative analysis of disease recurrence in a selected versus unselected population.

Main Results:

  • Demonstrated that index event bias can create inverse associations between risk factors and disease, even when none exist in the general population.
  • Showed that the crude association between a risk factor and disease is biased towards the null value due to this selection bias.

Conclusions:

  • Index event bias is a significant methodological concern in studies of recurrent disease.
  • This bias can lead to erroneous conclusions about the role of risk factors in disease recurrence.
  • Careful consideration of patient selection criteria is crucial to mitigate index event bias in epidemiological research.