Questions and Answers
Name the two basic types of error in epidemiologic research.
Systematic error and random error.
How do parameters differ from estimates?
Parameters cannot be calculated and are assumed to be error-free quantifications, whereas estimates are calculated using empirical data and are prone to error.
Provide a synonym for systematic error.
Bias
Provide a synonym for random error.
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Provide an antonym for biased.
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Provide an antonym for precise.
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List ways in which random error differs from systematic error.
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Probability models are used to adjust for bias in epidemiologic studies. Explain your response.
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What are the two common forms of statistical inference?
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How would expanding the sample size of an observational study affect random error and systematic error?
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Name the three general categories of bias in epidemiologic research.
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What is the effect of nondifferential misclassification on measures of association?
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A bias toward null means that the observed measure of association will underestimate the true risks or benefits associated with exposure.
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Define confounding.
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List the properties of a confounder.
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What does the Latin word confundere mean?
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Use of hospitalized cases and controls in case-control studies could result in what type of bias?
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If cases in a case-control study provide more complete responses about exposures to potential risk factors than controls, what type of bias will this cause?
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If the code book for a data set gets mixed-up so that exposed individuals are mistakenly coded as nonexposed and vice versa, what type of bias will this cause?
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Not all risk factors are confounders. Why?
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Why can the results of a study on smoking and lung cancer in men be generalized to the effects of smoking on lung cancer in women?
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Confidence intervals adjust for both random and systematic sources of error providing confidence in the study's results.
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Study Notes
Types of Error in Epidemiology
- Two basic types of errors: systematic error (bias) and random error (imprecision).
- Bias is an unbalanced error that affects study validity, while random error can be reduced by increasing sample size.
Parameters vs. Estimates
- Parameters are assumed to be error-free quantifications, while estimates are calculated from empirical data and subject to errors.
Bias and Precision
- Synonyms: Systematic error is synonymous with bias, and random error with imprecision.
- Antonyms: The antonym of biased is valid, and the antonym of precise is imprecise.
Characteristics of Errors
- Random error is balanced, decreases with larger sample sizes, and can be addressed using statistical methods like confidence intervals.
- Systematic error remains unaffected by sample size, and common statistical methods do not correct it.
Statistical Inference
- Two main forms of statistical inference: estimation and hypothesis testing.
Impact of Sample Size
- Increasing sample size reduces random error but has no effect on systematic error.
Types of Bias
- Three general categories of bias in epidemiology: selection bias, information bias, and confounding bias.
Nondifferential Misclassification
- This type of misclassification biases measures of association either toward the null or does not bias them at all.
Bias Toward the Null
- A bias toward the null underestimates the true risks or benefits associated with an exposure.
Confounding Definition and Properties
- Confounding: Distortion in a measure of association caused by extraneous factors.
- Properties of a confounder: Must be associated with exposure, an independent risk factor for disease, and not intermediate in the causal pathway.
Hospitalization Bias
- Using hospitalized cases and controls in a case-control study may lead to hospital admission rate bias (Berkson's bias).
Recall Bias
- When cases provide more complete exposure responses than controls, it results in recall bias, which biases results away from the null.
Misclassification Bias
- Mixing up codes for exposed and non-exposed individuals results in information or misclassification bias.
Risk Factors and Confounding
- Not all risk factors are confounders; only those associated with exposure and not equally distributed across groups or part of the causal pathway are considered confounders.
Generalizability of Findings
- Findings from studies on smoking and lung cancer in men can be applied to women due to similar causal mechanisms of the disease.
Confidence Intervals
- Confidence intervals only address random error and do not adjust for systematic sources, which limits their ability to provide overall validity in study results.
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Description
Test your knowledge on the sources of error in epidemiologic research with this flashcard quiz. Covering fundamental concepts such as systematic and random errors, as well as the distinction between parameters and estimates, this quiz is perfect for students of epidemiology.