Nonlinear Mixed Effects Models for Repeated Measures Data

Lindstrom, Mary J.; Bates, Douglas M.

doi:10.2307/2532087

Cited by 1,540 publications

(1,094 citation statements)

References 15 publications

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“…Nonlinear mixed‐effect modeling (NLME) enables variability among individuals to be integrated into the description of any given process 18. In our case, the structural part of the model corresponds to the solution of a system of ordinary differential equations (ODEs).…”

Section: Methodsmentioning

confidence: 99%

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

Ouerdani

Struemper

Suttle

et al. 2015

CPT Pharmacometrics Syst. Pharmacol.

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The objective was to leverage tumor size data from preclinical experiments to propose a model of tumor growth and angiogenesis inhibition for the analysis of pazopanib efficacy in renal cell carcinoma (RCC) patients. We analyzed tumor data in mice with RCC CAKI‐2 cell line treated with pazopanib. Clinical tumor size data obtained in a subset of patients with RCC were also analyzed. A model accounting for the processes of tumor growth, angiogenesis, and drug effect was developed. The final tumor model was composed of two variables: the tumor and its vasculature. Our results show that, both in mice and in humans, pazopanib exhibits a dual mechanism of action, and parameter estimation values highlight the inherent difference between mice and humans on the time scale of tumor size response. We developed a semimechanistic tumor growth inhibition model that takes into account tumor angiogenesis in order to describe the effects of pazopanib in mice. Analyzing rich preclinical data with a semimechanistic model may be a relevant approach to facilitate the description of sparse clinical longitudinal tumor size data and to provide insights for the understanding of the drug mechanisms of action in patients.

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Section: Methodsmentioning

confidence: 99%

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

Ouerdani

Struemper

Suttle

et al. 2015

CPT Pharmacometrics Syst. Pharmacol.

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“…Besides MCEM and the TS method, another approach is to consider approximation methods based on Taylor expansions or Laplace approximations to approximate the intractable integral of the likelihood. [13][14][15] While these approximate methods perform well in many cases, a drawback is that their performances are often less satisfactory if the intraindividual data are sparse, the interindividual variability is large, and the response of the model is too discrete such as a logistic mixed model. 16 In this paper, we will focus on the MCEM and the TS method.…”

Section: Monte Carlo (Mc) Em Algorithmmentioning

confidence: 99%

A mechanistic nonlinear model for censored and mismeasured covariates in longitudinal models, with application in AIDS studies

Zhang

Wong

2017

Statistics in Medicine

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When modeling longitudinal data, the true values of time-varying covariates may be unknown because of detection-limit censoring or measurement error.A common approach in the literature is to empirically model the covariate process based on observed data and then predict the censored values or mismeasured values based on this empirical model. Such an empirical model can be misleading, especially for censored values since the (unobserved) censored values may behave very differently than observed values due to the underlying data-generation mechanisms or disease status. In this paper, we propose a mechanistic nonlinear covariate model based on the underlying data-generation mechanisms to address censored values and mismeasured values. Such a mechanistic model is based on solid scientific or biological arguments, so the predicted censored or mismeasured values are more reasonable. We use a Monte Carlo EM algorithm for likelihood inference and apply the methods to an AIDS dataset, where viral load is censored by a lower detection limit. Simulation results confirm that the proposed models and methods offer substantial advantages over existing empirical covariate models for censored and mismeasured covariates.

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“…The estimated parameters from Model I were used in Model II to describe the PK of those conjugates, while k rel and y 3 was estimated for each conjugated polymer in order to compare the release constants (k rel ). The non-linear mixed effect models were fitted by use of the nlmeODE and the nlme functions, which used the approximation from Lindstrom et al [26] to fit the non-linear mixed effects model. Both the error term and the between-subject random effect added to the release constant k rel followed independent normal distributions.…”

Section: Two Compartmental Model Analysis Of Sct-polymer Derivatives mentioning

confidence: 99%

PK/PD modelling of comb-shaped PEGylated salmon calcitonin conjugates of differing molecular weights

Ryan

Frías²,

Wang

et al. 2011

Journal of Controlled Release

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Salmon calcitonin (sCT) was conjugated via cysteine-1 to novel combed-shaped endfunctionalised poly(PEG) methyl ether methacrylate) (sCT-P) comb-shaped polymers, to yield conjugates of total molecular weights (MW) inclusive of sCT: 6.5, 9.5, 23 and 40 kDa. The conjugates were characterised by HPLC and their in vitro and in vivo bioactivity was measured by cAMP assay on human T47D cells and following intravenous (i.v.) injection to rats, respectively. Stability against endopeptidases, rat serum and liver homogenates was assessed. There were linear and exponential relationships between conjugate MW with potency and efficacy respectively, however the largest MW conjugate still retained 70% of E max and an EC 50 of 3.7 nM. In vivo, while free sCT and the conjugates reduced serum [calcium] to a maximum of 15-30 % over 240 min, the half life (T½) was increased and the area under the curve (AUC) was extended in proportion to conjugate MW. Likewise, the polymer conferred protection on sCT against attack by trypsin, chymotrypsin, elastase, rat serum and liver homogenates, with the best protection afforded by sCT-P (40 kDa). Mathematical modelling accurately predicted the MW relationships to in vitro efficacy, potency, in vivo PK and enzymatic stability. With a significant increase in T½ for sCT, the 40 kDa MW comb-shaped PEG conjugate of sCT may have potential as a long-acting injectable formulation.

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Nonlinear Mixed Effects Models for Repeated Measures Data

Cited by 1,540 publications

References 15 publications

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

A mechanistic nonlinear model for censored and mismeasured covariates in longitudinal models, with application in AIDS studies

PK/PD modelling of comb-shaped PEGylated salmon calcitonin conjugates of differing molecular weights

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