Sources of bias and error
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This document discusses sources of bias and error that can occur in research studies. It defines validity as the degree to which a measurement measures what it intends to measure. Reliability is defined as the degree to which repeated measurements produce similar results. There are two types of errors - random errors which are due to chance, and systematic errors which have a recognizable source or pattern. Bias is a deviation from the truth that can lead to conclusions that differ from reality. There are three main types of biases: selection bias due to systematic differences between study groups, measurement/misclassification bias from inaccurate measurements, and confounding bias when an extraneous factor is associated with both an exposure and outcome. Confounding can distort the apparent
Sources of bias and error
- 1. Sources of Bias and Error DENTED 371
- 2. • To err is human…… »Alexander Pope 1711
- 4. Validity • Validity: The degree to which a measurement measures what it purports to measure (Last) Degree to which the data measure what they were intended to measure – that is, the results of a measurement correspond to the true state of the phenomenon being measured (Fletcher) • also known as ‘Accuracy’
- 5. ﺍاﻟﻤﻮﺛﻮﻗﻴﯿﺔ Reliability • The degree of stability expected when a measurement is repeated under identical conditions; degree to which the results obtained from a measurement procedure can be replicated (Last) • Extent to which repeated measurements of a stable phenomenon – by different people and instruments, at different times and places – get similar results (Fletcher) ﺍاﻟﺪﻗﺔ ﺍاﺳﺘﻨﺴﺎﺥخ • Also known as ‘Reproducibility’ and ‘Precision’
- 6. Error 1. 2. 3. A false or mistaken result obtained in a study or experiment Random error is the portion of variation in measurement that has no apparent connection to any other measurement or variable, generally regarded as due to chance Systematic error which often has a recognizable source, e.g., a faulty measuring instrument, or pattern, e.g., it is consistently wrong in a particular direction (Last)
- 7. Bias • Deviation of results or inferences from the truth, or processes leading to such deviation. Any trend in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are systematically different from the truth. (Last) • A process at any stage of inference tending to produce results that depart systematically from true values (Fletcher)
- 9. Types of biases 1. Selection bias 2. Measurement / (mis)classification bias 3. Confounding bias
- 10. Selection bias • Errors due to systematic differences in characteristics between those who are selected for study and those who are not. (Last; Beaglehole) • When comparisons are made between groups of patients that differ in ways other than the main factors under study, that affect the outcome under study. (Fletcher)
- 11. Examples of Selection bias • Subjects: hospital cases under the care of a physician • Excluded: 1. Die before admission – acute/severe disease. 2. Not sick enough to require hospital care 3. Do not have access due to cost, distance etc. • Result: conclusions cannot be generalized • Also known as ‘Ascertainment Bias’ (Last) ﺍاﻟﺘﻴﯿﻘﻦ
- 12. Examples …response bias • Volunteer either because they are unwell, or worried about an exposure • Respondents to ‘effects of smoking’ usually not as heavy smokers as non-respondents. • In a cohort study of newborn children, the proportion successfully followed up for 12 months varied according to the income level of the parents
- 13. Measurement bias • Systematic error arising from inaccurate measurements (or classification) of subjects or study variables. (Last) • Occurs when individual measurements or classifications of disease or exposure are inaccurate (i.e. they do not measure correctly what they are supposed to measure) (Beaglehole) • If patients in one group stand a better chance of having their outcomes detected than those in another group. (Fletcher)
- 14. Confounding 1. A situation in which the effects of two processes are not separated. The distortion of the apparent effect of an exposure on risk brought about by the association with other factors that can influence the outcome 2. A relationship b/w the effects of two or more causal factors as observed in a set of data such that it is not logically possible to separate the contribution that any single causal factor has made to an effect (Last)
- 15. Confounding When another exposure exists in the study population (besides the one being studied) and is associated both with disease and the exposure being studied. If this extraneous factor – itself a determinant of or risk factor for health outcome is unequally distributed b/w the exposure subgroups, it can lead to confounding (Beaglehole)
- 16. Confounder … must be 1. Risk factor among the unexposed (itself a determinant of disease) 2. Associated with the exposure under study 3. Unequally distributed among the exposed and the unexposed groups
- 17. Examples … confounding SMOKING LUNG CANCER AGE (If the average ages of the smoking and non-smoking groups are very different) (As age advances chances of lung cancer increase)
- 18. Examples … confounding COFFEE DRINKING HEART DISEASE (Smoking increases the risk of heart ds) (Coffee drinkers are more likely to smoke) SMOKING