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Variability and Error Module 5

This module explores the concepts of variability and error in measurements. It explains the differences between random and systematic error and discusses the different types of bias and how to combat them. Topics covered include random variability, random error, systematic error, selection bias, misclassification bias, and confounding.

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Variability and Error Module 5

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  1. Variability and ErrorModule 5

  2. Objectives • At the completion of this module, participants should be able to • Explain the differences between variability and error • Differentiate random from systematic error • Describe the different types of bias and understand how to combat them

  3. Introduction • Concepts • Variability and Error • Random and Systematic • Random variability is inherent to measurement; cannot be avoided • Random error results from sampling; can be alleviated by increasing sample size • Systematic error distorts measurement; avoided through careful study design

  4. Random Variability • Inherent to measurement • Background noise • The variability in measurement for which we lack another explanation • Present within individual subjects • Present between individuals • Cannot be avoided, but must be considered

  5. Random Error • Distinct from random variability • Arises as a result of sampling • Since we can’t study the entire population, we typically study a sample (which we hope will be representative) • The smaller the sample, the greater the likelihood (by chance alone) that the sample will not be representative “enough” (this is random error) • Combat random error by increasing sample size

  6. Systematic Error • Synonymous with bias • Results from some systematic problem such that sample measurements will always deviate from the true population value • Different types: selection bias, misclassification (information) bias and confounding • Cannot be fixed by increasing sample size • Selection & misclassification bias can only be addressed in study design • Confounding may also be addressed in analysis

  7. Types of Systematic Error • Selection Bias • Study population is not representative of the general population • Misclassification (Information) Bias • Misclassification of exposure or outcome • Confounding • Relationship between exposure and disease is distorted by some extraneous variable

  8. Selection Bias • Selection bias describes the selection of a study population is not representative of the general population • Increasing the sample size will simply produce a larger population that is still not representative of the general population • May arise at multiple points in the conduct of a study - recruitment, follow-up, analysis

  9. Examples - Solutions

  10. Consequences of Selection Bias • External validity - limited ability to extrapolate the results of a study to a more general population • Internal validity - distorted results if a particular sub-group of participants are not included in the study

  11. Misclassification Bias • Also known as information bias • A form of error that results from systematic inaccuracy in measurement • Non-differential misclassification bias blurs differences between groups, resulting in a bias towards the null • Differential misclassification bias inflates differences between groups, causing bias away from the null

  12. Examples - Solutions

  13. Confounding • A mixing of effects that results in distortion of the exposure-disease relationship by a third (extraneous) factor • For a variable to be considered a confounder it must meet 2 criteria • Association with outcome in the absence of exposure • Association with exposure but not as a consequence of the exposure (i.e. not an intermediary)

  14. Confounding • Several reports have suggest an association between thymectomy and remission of myasthenia gravis • Patients who undergo thymectomy tend to have milder disease (and to be younger in age) as do people who tend to remit • The relationship between thymectomy and MG remission is confounded by disease severity and age

  15. Combating Confounding • May be avoided by matching on the confounding factor • Confounding differs from other forms of systematic error (bias) in that its presence can be controlled for in the analysis (stratification, multi-variate analysis) • Randomization is the ideal - controls for both known and unknown confounding factors (but not possible in observational studies)

  16. Summary • Science requires measurement • Need to consider several sources of variability and error • Random variability is inherent; it is the noise that interferes with measurement of the signal (e.g. mean height)

  17. Summary • Random error results from a sample size that is insufficient to overcome the role of chance • Random error may be reduced by increasing sample size • Systematic error is unacceptable • Correcting systematic error requires a fundamental change in the way data is collected (and / or analyzed)

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