2023
DOI: 10.1093/ije/dyad001
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Simultaneous adjustment of uncontrolled confounding, selection bias and misclassification in multiple-bias modelling

Abstract: Background Adjusting for multiple biases usually involves adjusting for one bias at a time, with careful attention to the order in which these biases are adjusted. A novel, alternative approach to multiple-bias adjustment involves the simultaneous adjustment of all biases via imputation and/or regression weighting. The imputed value or weight corresponds to the probability of the missing data and serves to ‘reconstruct’ the unbiased data that would be observed based on the provided assumption… Show more

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Cited by 6 publications
(2 citation statements)
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“…While random error can be minimised through increased sample size, systematic errors (bias) may be more subtle and challenging to prevent 7 . As such, the impact of multiple sources of bias (e.g., unmeasured/uncontrolled confounding, misclassification and selection bias) on effect estimates should ideally be explored through formal quantitative bias analyses, as described elsewhere 58–60 . Furthermore, descriptive estimates of HDP burden are likely conservative underestimates when these conditions are identified from administrative databases.…”
Section: Conceptual and Methodological Challengesmentioning
confidence: 99%
“…While random error can be minimised through increased sample size, systematic errors (bias) may be more subtle and challenging to prevent 7 . As such, the impact of multiple sources of bias (e.g., unmeasured/uncontrolled confounding, misclassification and selection bias) on effect estimates should ideally be explored through formal quantitative bias analyses, as described elsewhere 58–60 . Furthermore, descriptive estimates of HDP burden are likely conservative underestimates when these conditions are identified from administrative databases.…”
Section: Conceptual and Methodological Challengesmentioning
confidence: 99%
“…Depending on the particular study, multiple residual biases could exist, and jointly quantifying the impact of all of these biases is necessary to properly assess robustness of results 34. Bias formulas and probabilistic bias analyses can be applied for multiple biases, but specification is more complicated, and the biases should typically be accounted for in the reverse order from which they arise (appendices 2 and 3 show an applied example) 74647. Bounding methods are available for multiple biases 34…”
Section: Pitfalls Of Methodsmentioning
confidence: 99%