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XNN001 Introductory epidemiological concepts - sampling, bias and error
- 1. INTRODUCTORY EPIDEMIOLOGICAL CONCEPTS – SAMPLING, BIAS AND ERROR XNN001 Population nutrition and physical activity assessment
- 2. Study design Research question Target population Study design Sampling frame Data collection tools Data collection methods Generalisability of findings Sampling selection
- 3. Sampling – what is it? Selection of a smaller number of units from a larger group In research aim to enable generalisation to a target population
- 4. Why do we sample? Not possible to study ALL people in a population Feasible and realistic financially to study smaller subset of a population Unethical if sample is larger than necessary (overpowered) 1. 2. 3. Aim to provide an accurate representation of the target population Allows for generalisation from sample to broader population Need to minimise sampling error and bias
- 6. How big should a sample be? Sample size calculations to determine required size Based on variables to be measured - expected difference, expected response rates, cluster effect, attrition etc Small sample size Larger sample size Less likely that sample is representative of target population Limited POWER to detect ‘effect’ More likely that sample is representative of target population Increased POWER to detect ‘effect’
- 8. 1. Simple Random Sampling Subset of individuals chosen from a list of individuals from the broader population (sampling frame) Each individual chosen at random all subjects have equal chance of being selected Most likely to achieve sample representative of population (least selection bias) May be difficult to achieve in practice Not ideal for special interest groups/ population minorities
- 10. 2. Systematic sampling Units sampled at regular intervals Width of intervals randomly determined inadequate sampling of rare individuals who may be of interest chance that random dispersion is “unlucky” and inadequate Researcher pattern must ensure sampling does not hide
- 11. Systematic sampling - example
- 13. 3. Stratified sampling Population divided into subgroups prior to sampling To ensure adequate numbers of subjects from subgroups are included e.g. male and female subgroups Then simple random sample the individuals among male group and then female group
- 14. Target population – Brisbane households Sampling frame – electoral roll Sampling frame – electoral roll MALES Sampling frame – electoral roll FEMALES SAMPLE
- 15. 4. Cluster sampling Total population broken down into ‘groups’ or ‘clusters’ Number of clusters then randomly selected from all eligible clusters All individuals in each selected cluster become potential subjects.
- 16. 4. Cluster sampling One-stage cluster sampling Clusters are selected randomly All individuals within clusters are invited to participate in the study Two-stage cluster sampling Clusters are selected randomly Lists of all elements within clusters are obtained random samples drawn from lists
- 17. Cluster sampling - example Simple Random Sampling Stage 1 Stage 2 All Schools in Brisbane School A – all students School B – all students Random sample Random sample
- 18. 5. multi-stage sampling Complex form of cluster sampling Population divided into clusters and sub-clusters Used when selecting from very large population
- 19. Nationwide retail chain random selection of region Region 1 Region 2 random selection of stores Store 1 Store 2 Store 1 Store 2 Stratified sampling Male Female Male Female Male Female Male random selection 20 20 20 20 20 20 20 20 Female
- 20. Non-probability sampling Sampling techniques that do not rely on random selection When sampling frame not able to be identified e.g. visitors to a particular internet site When sampling populations are difficult to access (e.g. drug users, street based sex workers). When very strict inclusion and exclusion criteria are necessary (e.g. in pharmaceutical drug testing)
- 21. 1. Convenience sampling Units ‘selected’ based on ease of access Volunteers Shoppers in a supermarket Respondents to advertisements Clinic attendees The sample usually is different from the target population Cannot generalise results to general population
- 22. 2. Quota sample Population divided into defined subgroups e.g. males; females Proportions of subgroups in population identified Convenience sample of each subgroup to make up required numbers
- 23. 3. Purposive sample Deliberate selection of individuals by researchers based on a predefined criteria INCLUSION & EXCULSION CRITERIA Often used in pharmaceutical drug testing Also called judgmental sampling
- 24. 4. Snowball sampling Involves asking subjects to provide names of others who may meet study criteria Useful for sampling populations difficult to access Also called networking drug users street-based sex workers underground networks
- 26. Measurement issues Error- validity when an estimate (eg, incidence, prevalence, mortality) or association (RR, OR) deviates from ‘true’ situation in nature May be introduced at any point during the study: Study design (quality) sampling Random error Measurement Analysis Systematic bias
- 27. Random error Fluctuations around a true value Related to poor precision Sources individual biological variation (always present) sampling variation measurement variation (protocols and training) Reduced by: larger sample sizes standard protocols and equipment
- 28. Systematic bias Any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on the risk of a disease Due to causes other than random error Problem of validity internal and/or external validity
- 29. I. Selection bias Arises when different criteria are used so the study population does not represent the population of interest for example: 1. 2. 3. 4. Referral Bias (Berkson’s Bias) Surveillance Bias Prevalence-Incidence Bias (Neyman’s Bias) Response Bias Attrition Bias Participation Bias
- 30. Types of bias Referral bias Occurs in case-control studies conducted in hospitals Causes a spurious association between the exposure and the disease, because of the different probabilities of admission to a hospital for those with/without a disease (or with/without the exposure) Surveillance bias For example: When conducting a case-control study to examine the relationship between oral contraceptive (OC) use and diabetes Women taking OCs are likely to have more Dr visits, so diabetes is more likely to be diagnosed in OC users than in non-OC users
- 31. 3. Prevalence-incidence bias Also known as Neyman’s bias Usually occurs when prevalent cases are used to investigate a disease-exposure association Prevalent cases represent survivors, who may be atypical with respect to exposure status Once a person is diagnosed with the disease, they may change their exposure
- 32. Types of bias Participation bias People who participate in research studies are often different to those who do not take part. Demographic, socioeconomic, cultural, lifestyle, and medical characteristics Self-selection bias (individual consent is essential in research, except public available information) Attrition bias Occurs when study participants withdraw before the study is completed and is often differential
- 33. II. Information bias Arises when inaccurate measurement or misclassification of study variables occurs Can affect exposure or outcome (or even confounders) Extent of bias depends on the particular variable whether non-differential or differential misclassification
- 34. Non-differential info-bias Error in measurement does not vary according to other variables (cases vs controls; exposed vs unexposed) Underestimate of the true association Any association that is observed is likely to be true
- 35. Differential info-bias Systematic error (ie non-random) May over-estimate or under-estimate the actual association, depending upon the situation.
- 36. Types of information bias 1. Recall Bias cases and controls recall their exposures differently It is human being’s nature to looking for reasons if something went wrong “If you seek, you will find.” 2. Detection Bias the exposed group is monitored more closely 3. Interviewer/observer Bias Not blinded Not properly trained
- 37. Types of information bias 4. Reporting Bias “Objectively” Cases tend to have better information Individuals who are part of a study may behave differently (Hawthorne effect) “Subjectively” Reluctant to report: attitudes, beliefs, perception Wish bias: subjects attempting to answer the question of “why me?” and the disease is not their fault (lifestyle), but others (work related exposure)
- 38. III. Confounding - definition An association between a given exposure and outcome is influenced by a third variable – confounding factor. To be a confounder: 1. Be a risk factor for disease 2. Be associated with the exposure 3. Not a result of the exposure Not be an intermediate between exposure and the outcome (i.e must not lie on the causal pathway)
- 39. Validity Do the study conclusions reflect the true value/relationship? External validity (generalisability): can the findings be generalised to other similar samples or the population-at-large? Internal validity: are the results correct for the particular group you have studied?
- 40. Reliability Accuracy -- how close to the true population value is your measurement value? Assess accuracy by comparing to “gold standard” Precision -- If you repeat your measurement/ sample selection/analysis on numerous occasions, will you get consistent results? Assess precision by inter-observer and intra-observer comparisons