Empirical Bayes estimation of random effects of a mixed-effects proportional odds Markov model for ordinal data

The objective of this work was to investigate the factors influencing the quality of empirical Bayes estimates (EBEs) of individual random effects of a mixed-effects Markov model for ordered categorical data. It was motivated by an attempt to develop a model-based dose adaptation tool for clinical u...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine Vol. 104; no. 3; pp. 505 - 513
Main Authors: Paule, Inès, Girard, Pascal, Tod, Michel
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ireland Ltd 01.12.2011
Elsevier
Subjects:
Antineoplastic Agents - adverse effects
Antineoplastic Agents - therapeutic use
Bayes Theorem
Biological and medical sciences
Capecitabine
Colorectal Neoplasms - drug therapy
Deoxycytidine - adverse effects
Deoxycytidine - analogs & derivatives
Deoxycytidine - therapeutic use
Empirical Bayes estimation
Empirical Research
Fluorouracil - adverse effects
Fluorouracil - analogs & derivatives
Fluorouracil - therapeutic use
Hand-Foot Syndrome - etiology
Humans
Individual random effects
Internal Medicine
Markov Chains
Medical sciences
Mixed-effects models
Optimization algorithm comparison
Ordered categorical data models
Other
Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects)
Technology. Biomaterials. Equipments. Material. Instrumentation
Individual random effects
Ordered categorical data models
Empirical Bayes estimation
Mixed-effects models
Optimization algorithm comparison
Biomedical engineering
ISSN:0169-2607, 1872-7565, 1872-7565
Online Access:Get full text
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Summary:The objective of this work was to investigate the factors influencing the quality of empirical Bayes estimates (EBEs) of individual random effects of a mixed-effects Markov model for ordered categorical data. It was motivated by an attempt to develop a model-based dose adaptation tool for clinical use in colorectal cancer patients receiving capecitabine, which induces severe hand-and-foot syndrome (HFS) toxicity in more than a half of the patients. This simulation-based study employed a published mixed-effects model for HFS. The quality of EBEs was assessed in terms of accuracy and precision, as well as shrinkage. Three optimization algorithms were compared: simplex, quasi-Newton and adaptive random search. The investigated factors were amount of data per patient, distribution of categories within patients, magnitude of the inter-individual variability, and values of the effect model parameters. The main factors affecting the quality of EBEs were the values of parameters governing the dose–response relationship and the within-subject distribution of categories. For the chosen HFS toxicity model, the accuracy and precision of EBEs were rather low, and therefore the feasibility of their use for individual model-based dose adaptation seemed limited.
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ISSN:0169-2607
1872-7565
1872-7565
DOI:10.1016/j.cmpb.2011.04.006