Why the study?
Biostatistical models carry enormous underlying uncertainties that often remain unaccounted for due to lack of researcher awareness, causing models to fail in clinical applications.
Design
Review
Key result
Unaccounted aleatory and epistemic variations can surreptitiously spoil the validity of biostatistical models despite a good fit for the data, leading to large imprecision in clinical applications.
Authors
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Individual-level use of group-derived models risks unaccounted imprecision; leaves open standardized methods for uncertainty quantification in biostatistical research.
Key points are not available for this paper at this time.
Abhaya Indrayan (2024) studied this question. Unaccounted aleatory and epistemic variations can surreptitiously spoil the validity of biostatistical models despite a good fit for the data, leading to large imprecision in clinical applications.
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