INTRODUCTION TO BIAS
In epidemiology, bias refers to systematic errors in the design, conduct, or analysis of a study
that can lead to incorrect estimates of associations between exposures and outcomes.
A bias is a systematic error, or deviation from the truth, in results or inferences. Biases can
operate in either direction: different biases can lead to underestimation or overestimation of
the true intervention effect. Biases can vary in magnitude: some are small (and trivial compared
with the observed effect) and some are substantial (so that an apparent finding may be entirely
due to bias).
Selection bias: This occurs when there's a systematic difference between the characteristics of
those selected for a study and those who are not, leading to an inaccurate representation of
the population.
For example In a study investigating the association between smoking and lung cancer, if the
study only includes individuals from affluent neighborhoods where smoking rates are lower, it
may underestimate the true risk of lung cancer associated with smoking.
Control; Randomization helps mitigate selection bias in experimental studies. In observational
studies, matching or stratification can be employed to ensure that exposed and unexposed
groups are similar in terms of potential confounders.
Information bias: This bias arises from errors in the measurement or classification of exposure,
outcome, or confounding variables.
For example in a retrospective study on dietary habits and obesity, if participants are asked to
recall their food intake over the past year, their responses may be influenced by their current
weight status, leading to misclassification of exposure.
Control; Improving data collection methods, using standardized instruments, and minimizing
reliance on self-reporting can help reduce information bias. Blinding of data collectors to the
exposure and outcome status of participants can also be effective.
Confounding bias: Confounding occurs when a variable is associated with both the exposure
and outcome, and it distorts the true association between them. For example; in a study
examining the relationship between alcohol consumption and heart disease, age is a
confounding variable because older individuals are more likely to have heart disease and may
also drink less alcohol due to health concerns.
Control; Statistical techniques such as stratification, multivariate regression, and propensity
score matching can control for confounding by accounting for potential confounding variables
in the analysis. Additionally, randomization in experimental studies helps to evenly distribute
potential confounders between study groups
In epidemiology, bias refers to systematic errors in the design, conduct, or analysis of a study
that can lead to incorrect estimates of associations between exposures and outcomes.
A bias is a systematic error, or deviation from the truth, in results or inferences. Biases can
operate in either direction: different biases can lead to underestimation or overestimation of
the true intervention effect. Biases can vary in magnitude: some are small (and trivial compared
with the observed effect) and some are substantial (so that an apparent finding may be entirely
due to bias).
Selection bias: This occurs when there's a systematic difference between the characteristics of
those selected for a study and those who are not, leading to an inaccurate representation of
the population.
For example In a study investigating the association between smoking and lung cancer, if the
study only includes individuals from affluent neighborhoods where smoking rates are lower, it
may underestimate the true risk of lung cancer associated with smoking.
Control; Randomization helps mitigate selection bias in experimental studies. In observational
studies, matching or stratification can be employed to ensure that exposed and unexposed
groups are similar in terms of potential confounders.
Information bias: This bias arises from errors in the measurement or classification of exposure,
outcome, or confounding variables.
For example in a retrospective study on dietary habits and obesity, if participants are asked to
recall their food intake over the past year, their responses may be influenced by their current
weight status, leading to misclassification of exposure.
Control; Improving data collection methods, using standardized instruments, and minimizing
reliance on self-reporting can help reduce information bias. Blinding of data collectors to the
exposure and outcome status of participants can also be effective.
Confounding bias: Confounding occurs when a variable is associated with both the exposure
and outcome, and it distorts the true association between them. For example; in a study
examining the relationship between alcohol consumption and heart disease, age is a
confounding variable because older individuals are more likely to have heart disease and may
also drink less alcohol due to health concerns.
Control; Statistical techniques such as stratification, multivariate regression, and propensity
score matching can control for confounding by accounting for potential confounding variables
in the analysis. Additionally, randomization in experimental studies helps to evenly distribute
potential confounders between study groups
