A quick and accurate method for the estimation of covariate effects based on empirical Bayes estimates in mixed-effects modeling: Correction of bias due to shrinkage

Author:

Yuan Min1ORCID,Xu Xu Steven2,Yang Yaning3,Xu Jinfeng4,Huang Xiaohui1,Tao Fangbiao1,Zhao Liang5,Zhang Liping2,Pinheiro Jose2

Affiliation:

1. School of Public Health Administration, Anhui Medical School, Hefei, China

2. Janssen Research and Development, Raritan, New Jersey, NJ, USA

3. Department of Statistics and Finance, University of Science and Technology of China, Hefei, China

4. Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong

5. Division of Quantitative Methods and Modeling, OGD/ORS at US Food and Drug Administration, Silver Spring, MD, USA

Abstract

Nonlinear mixed-effects modeling is a popular approach to describe the temporal trajectory of repeated measurements of clinical endpoints collected over time in clinical trials, to distinguish the within-subject and the between-subject variabilities, and to investigate clinically important risk factors (covariates) that may partly explain the between-subject variability. Due to the complex computing algorithms involved in nonlinear mixed-effects modeling, estimation of covariate effects is often time-consuming and error-prone owing to local convergence. We develop a fast and accurate estimation method based on empirical Bayes estimates from the base mixed-effects model without covariates, and simple regressions outside of the nonlinear mixed-effect modeling framework. Application of the method is illustrated using a pharmacokinetic dataset from an anticoagulation drug for the prevention of major cardiovascular events in patients with acute coronary syndrome. Both the application and extensive simulations demonstrated that the performance of this high-throughput method is comparable to the commonly used maximum likelihood estimation in nonlinear mixed-effects modeling.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Anhui Provincial Education Department

Publisher

SAGE Publications

Subject

Health Information Management,Statistics and Probability,Epidemiology

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