Efficient algorithms for covariate analysis with dynamic data using nonlinear mixed-effects model

Author:

Yuan Min1ORCID,Zhu Zhi2,Yang Yaning2ORCID,Zhao Minghua2,Sasser Kate3,Hamadeh Hisham3,Pinheiro Jose4,Xu Xu Steven3

Affiliation:

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

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

3. Genmab US, Inc., Princeton, NJ, USA

4. Janssen Research and Development, Raritan, NJ, USA

Abstract

Nonlinear mixed-effects modeling is one of the most popular tools for analyzing repeated measurement data, particularly for applications in the biomedical fields. Multiple integration and nonlinear optimization are the two major challenges for likelihood-based methods in nonlinear mixed-effects modeling. To solve these problems, approaches based on empirical Bayesian estimates have been proposed by breaking the problem into a nonlinear mixed-effects model with no covariates and a linear regression model without random effect. This approach is time-efficient as it involves no covariates in the nonlinear optimization. However, covariate effects based on empirical Bayesian estimates are underestimated and the bias depends on the extent of shrinkage. Marginal correction method has been proposed to correct the bias caused by shrinkage to some extent. However, the marginal approach appears to be suboptimal when testing covariate effects on multiple model parameters, a situation that is often encountered in real-world data analysis. In addition, the marginal approach cannot correct the inaccuracy in the associated p-values. In this paper, we proposed a simultaneous correction method (nSCEBE), which can handle the situation where covariate analysis is performed on multiple model parameters. Simulation studies and real data analysis showed that nSCEBE is accurate and efficient for both effect-size estimation and p-value calculation compared with the existing methods. Importantly, nSCEBE can be >2000 times faster than the standard mixed-effects models, potentially allowing utilization for high-dimension covariate analysis for longitudinal or repeated measured outcomes.

Funder

Anhui Natural Science Foundation

Doctoral research funding of Anhui Medical University

National Natural Science Foundation of China

Publisher

SAGE Publications

Subject

Health Information Management,Statistics and Probability,Epidemiology

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Covariate modeling in pharmacometrics: General points for consideration;CPT: Pharmacometrics & Systems Pharmacology;2024-04-02

2. SAMBA: A novel method for fast automatic model building in nonlinear mixed‐effects models;CPT: Pharmacometrics & Systems Pharmacology;2022-02

3. Research on a Key Question in the Parlange Solution;KSCE Journal of Civil Engineering;2021-09-14

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