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SES as assessed at baseline was used in all analyses, as allowing SES to vary over time would introduce reverse causation biases.
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The causation bias suggests the tendency to assume a cause-effect relationship in situations in which none exists (or there is a correlation or association).
An interplay of a healthy cohort effect and reversed causation bias due to prodromal symptoms impedes detection of a possible association with dementia and Parkinson disease.
A particular advantage of investigating education is avoiding reverse causation bias: incident cancer may lead to downward occupational mobility and reduced income but generally will not affect educational status achieved by early adulthood.
With regard to dementia, an interplay of a healthy cohort effect and reversed causation bias due to prodromal symptoms impedes detection of a possible association, although identification of the factors that contribute to the healthy cohort would be of interest for determining the aetiology of dementia.
This analysis defines the exposure accurately but may suffer from reverse causation bias.
The authors considered reverse causation bias as the most plausible explanation.
First, it is possible that findings are compromised by reverse causation bias.
Incomplete control for confounding or reverse causation bias could also explain the substantial risk in lean diabetes.
To minimize reverse causation bias, we excluded diabetes cases diagnosed within the first year since urine sample collection.
Reverse causation bias was avoided by excluding respondents hospitalised for a fall injury during the year prior to the survey.
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CEO of Professional Science Editing for Scientists @ prosciediting.com