z-logo
Premium
Individualizing drug dosage with longitudinal data
Author(s) -
Zhu Xiaolu,
Qu Annie
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7016
Subject(s) - covariate , random effects model , inference , computer science , statistics , mathematics , mathematical optimization , econometrics , algorithm , medicine , artificial intelligence , meta analysis
We propose a two‐step procedure to personalize drug dosage over time under the framework of a log‐linear mixed‐effect model. We model patients' heterogeneity using subject‐specific random effects, which are treated as the realizations of an unspecified stochastic process. We extend the conditional quadratic inference function to estimate both fixed‐effect coefficients and individual random effects on a longitudinal training data sample in the first step and propose an adaptive procedure to estimate new patients' random effects and provide dosage recommendations for new patients in the second step. An advantage of our approach is that we do not impose any distribution assumption on estimating random effects. Moreover, the new approach can accommodate more general time‐varying covariates corresponding to random effects. We show in theory and numerical studies that the proposed method is more efficient compared with existing approaches, especially when covariates are time varying. In addition, a real data example of a clozapine study confirms that our two‐step procedure leads to more accurate drug dosage recommendations. Copyright © 2016 John Wiley & Sons, Ltd.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here