Chance, bias, and confounding should be ruled out in order to talk about a valid statistical association
A valid statistical association does not imply causation!
For example, incidence of prostate cancer has increased recently, as have the sales of flat-screen TVs. But there is no causation
The two types of errors in epidemiology
Type 1 error – when we conclude that there is a difference when in reality there is no difference
To avoid type 1 errors, we strengthen the statistical power, by using statistic significance, p < 0,05, confidence intervals, etc.
Type 2 error – when we conclude that there is no difference when in reality there is one
To avoid type 2 errors, we must use a large sample size and accurate measurements
Bias
A bias is a systematic error which leads us to conclusions which are systematically different from the truth
Bias does not apply equally to the different groups measured
Selection bias
Occurs when the sample group is not representative of the population from which it is drawn
Examples
Healthy worker effect – the working population is healthier than the general population, so a sample of working people does not represent the general population
Volunteer bias – people who volunteer to join a study have different characteristics than the general population
Volunteers are generally more healthy, have lower mortality and are more likely to comply with doctor’s orders
Prevented by randomizing instead of selecting people to the groups, and making sure the sample group is representative of the population
Information bias
Occurs during collection, analysis, and interpretation of data
Examples
Recall bias – people who are diseased may recall their exposure to risk factors better than those who are healthy
Interviewer bias – different interviewing approaches towards different groups prompt different responses
Publication bias – when the outcome of a study influences the decision to publish the study
Studies which find no association are often not published, despite these results being as important as studies which find associations
Misclassification = assigning someone to the wrong group
Bias in screening
Volunteer bias
Length bias
Screening selectively identifies patients with a long preclinical and clinical phase and less frequently identifies patients with shorter phases
The patients with long phases would have a better prognosis regardless of the screening program
Lead-time bias
Screening causes cases to be diagnosed earlier in the natural history of the disease
This makes it seem like the patients live longer, but this is just because of the earlier diagnosis and not because of the earlier treatment
Atomistic fallacy
Observations at the individual level are not necessarily true on the populational level
Examples
Infant mortality is associated with low birthweight on an individual level, but not on the populational level
