Abstract
Dense data can be classified into superdense information-poor data (type 1 dense data) and dense information-rich data (type 2 dense data). Arbitrary, random, or optimal thinning may be applied to type 1 dense data to minimise computational burden and statistical issues (such as autocorrelation). In contrast, a prospective or retrospective optimal design can be applied to type 2 dense data to maximise information gain from limited resources (capital and/or time). Here we describe a retrospective optimal selection strategy for quantification of unbound drug concentration from a discrete set of plasma samples where the total drug concentration has been measured.
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D.W. and S.D. wrote the main manuscript text. All authors reviewed the manuscript and were involved in the study from which the motivating example was derived.
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D.W. received the University of Otago Doctoral scholarship. T.H. is the owner of Zenith Technology Limited contracted to perform the Phase I trial reported by Athenex Limited. N.H. is the medical director of Zenith Technology Limited. C.J., S.D., P.G. have no conflicts of interests to disclose.
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Wang, D., Hung, T., Hung, N. et al. Optimal sample selection applied to information rich, dense data. J Pharmacokinet Pharmacodyn 51, 33–37 (2024). https://doi.org/10.1007/s10928-023-09883-7
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DOI: https://doi.org/10.1007/s10928-023-09883-7