Online Program Home
My Program

Abstract Details

Activity Number: 702
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #318640
Title: Individualizing Drug Dosage with Longitudinal Data
Author(s): Xiaolu Zhu* and Annie Qu
Companies: University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
Keywords: individualized drug dosage ; longitudinal data ; mixed-effects model ; personalized medicine

We propose a two-step procedure to personalize drug dosage over time under the frame- work 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 to 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.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association