Instructor Resource Chapter 8 Copyright © Scott B. Patten, Permission granted for classroom use with Epidemiology for Canadian Students: Principles, Methods & Critical Appraisal (Edmonton: Brush Education Inc.

Chapter 8. Selection error and selection bias in descriptive studies

Objectives Define selection bias as a type of systematic error arising from participation or nonparticipation in a study. Identify sources of selection bias: selection itself, nonconsent, attrition, and missing data Describe the mechanism by which defective selection can introduce bias into an estimated frequency such as prevalence. Describe the direction of bias in a defective prevalence study in which selection depends on disease status.

Sources of errorType of bias Random errorChanceN/A Systematic error Measurement error Flaws in study design (flawed measurement leading to misclassification) Misclassification bias Selection errorFlaws in study design (flawed sampling procedures that choose participants) Selection bias Other factors related to participation (e.g., subjects withdrawing from a study)

Selection bias Selection bias is a type of systematic error that results from study-design defects, and other factors, that affect who participates in an epidemiologic study.

Selection bias due to sampling procedures To understand flawed sampling procedures, it helps to consider ideal sampling procedures. Ideal sampling procedures deliver probability samples, where the probability of selection for each member of the sample is known. Probability samples can be simple (e.g., simple random samples) or complex (e.g., where selection probabilities differ among respondents).

Selecting a simple random sample The first step is to identify a sampling frame. Then, randomly select observations from that sample.

Sampling frames Examples of sampling frames include: area-based frames (e.g., based on geography) telephone-based frames (lists of telephone numbers) mailing-based frames (lists of mailing addresses) disease registries

A note about telephone frames Telephone survey methods are of declining importance in epidemiology due to difficulties in obtaining a representative sample in this way. Since a listing of telephone numbers does not include unlisted numbers, random-digit dialing or random-digit substitution is preferable.

Sampling when a frame is not available Possibilities include: random digit dialing

Selection error from other factors All of the following are sources of selection error: nonconsent attrition missing data* * Missing data are often handled using specialized techniques that allow educated guesses to be made about the likely values of the missing variables (imputation), but if missing data results in nonparticipation (e.g., the respondents with missing data are Excluded form calculation of a parameter), the implications are the same.

Ethical issues Informed consent is required for research to respect personal autonomy (respect for persons). Other ethical principles include: beneficence nonmalfeasance utilitarian principle confidentiality privacy justice

Mechanisms of selection bias With selection bias, the prevalence estimate from a study is related both to the frequencies in the population and to selection.

Mechanisms of selection bias Note that if there is only 1 selection probability (e.g., a simple random sample), all of the p selection terms will disappear and the right-hand side of the equation will reduce to A/(A+B) or PREVALENCE

Mechanisms of selection bias But what if the p selection associated with those who have the disease (A) is greater than that of those without (B)? Will the expected value of the prevalence estimate still resemble PREVALENCE? Will it be too high or too low? Can you think of a reason why this might occur?

Mechanisms of selection bias What if the p selection associated with those who do not have the disease (B) is greater than that of those with the disease (A)? Can you think of a reason why this might occur?

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