Drug Dosage Individualization Based on a Random-Effects Linear Model

Díaz, Francisco Javier Rodríguez; Cogollo, Myladis R.; Spina, Edoardo; Santoro, Vincenza; Rendon, Diego M.; Leon, José de

doi:10.1080/10543406.2010.547264

Cited by 25 publications

(67 citation statements)

References 19 publications

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“…Perhaps the work that relates most to this paper is that on Dynamic treatment regimes (DTRs) [31]- [35]. A DTR is typically a sequence of decision rules, with one rule per stage of clinical intervention, where each rule maps up-to-date patient information to a recommended treatment [31].…”

Section: ) Dynamic Treatment Regimesmentioning

confidence: 99%

ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening

Alaa

Moon

Hsu

et al. 2016

IEEE Trans. Multimedia

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Breast cancer screening policies attempt to achieve timely diagnosis by the regular screening of apparently healthy women. Various clinical decisions are needed to manage the screening process; those include: selecting the screening tests for a woman to take, interpreting the test outcomes, and deciding whether or not a woman should be referred to a diagnostic test. Such decisions are currently guided by clinical practice guidelines (CPGs), which represent a "one-size-fits-all" approach that are designed to work well on average for a population, without guaranteeing that it will work well uniformly over that population. Since the risks and benefits of screening are functions of each patients features, personalized screening policies that are tailored to the features of individuals are needed in order to ensure that the right tests are recommended to the right woman. In order to address this issue, we present ConfidentCare: a computer-aided clinical decision support system that learns a personalized screening policy from the electronic health record (EHR) data. ConfidentCare operates by recognizing clusters of "similar" patients, and learning the "best" screening policy to adopt for each cluster. A cluster of patients is a set of patients with similar features (e.g. age, breast density, family history, etc.), and the screening policy is a set of guidelines on what actions to recommend for a woman given her features and screening test scores. ConfidentCare utilizes an iterative algorithm that applies K-means clustering to the women's feature space, followed by learning an active classifier (decision tree) for every cluster. The algorithm ensures that the policy adopted for every cluster of patients satisfies a predefined accuracy requirement with a high level of confidence. We show that our algorithm outperforms the current CPGs in terms of cost-efficiency and false positive rates.

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Section: ) Dynamic Treatment Regimesmentioning

confidence: 99%

ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening

Alaa

Moon

Hsu

et al. 2016

IEEE Trans. Multimedia

View full text Add to dashboard Cite

show abstract

“…The algorithm is not justified with machine learning concepts, but with the concepts of a solid general theory called Statistical Decision Theory, which prescribes widely accepted general principles for both the estimation of population parameters and the prediction of individual parameters in the presence of uncertainty [24]. The algorithm was extended by Diaz et al [6] to the general situation in which some covariates have random effects. Here, we review the algorithm assuming that the population of patients satisfies model (1) [5,6].…”

Section: Random-effects Linear Models and Drug Dosage Individualizationmentioning

confidence: 99%

“…The feature that makes random-effects (linear or nonlinear) models so useful for personalized medicine is that a random coefficient can be viewed as a parameter that is a characteristic constant for a particular patient, but that varies across patients [4][5][6]. In this sense, the variability of a random coefficient is considered to be the result of real variation in biological and environmental factors, and not just a mathematical trick to handle the variability of patients' pharmacological response.…”

Section: Why Should We Use Random-effects Models In Personalized Medimentioning

confidence: 99%

“…For pedagogical reasons, this article focuses on model (1); the general situation in which is considered random is studied in reference [6].…”

Section: The Random Intercept Linear Modelmentioning

confidence: 99%

“…To keep things simple, we will describe a simple version of a randomeffects linear model, namely the random-intercept linear model, which has potential applications to drug dosage individualization. Diaz et al [5,6] proposed using a model of a pharmacokinetic or pharmacodynamic response from a particular patient that assumes that the response, the drug dosage, and the clinical, environmental, biological or demographic covariates of a particular patient are related through the following equation:…”

Section: Why Should We Use Random-effects Models In Personalized Medimentioning

confidence: 99%

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Role of Statistical Random-Effects Linear Models in Personalized Medicine

Díaz¹

2012

cppm

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Some empirical studies and recent developments in pharmacokinetic theory suggest that statistical randomeffects linear models are valuable tools that allow describing simultaneously patient populations as a whole and patients as individuals. This remarkable characteristic indicates that these models may be useful in the development of personalized medicine, which aims at finding treatment regimes that are appropriate for particular patients, not just appropriate for the average patient. In fact, published developments show that random-effects linear models may provide a solid theoretical framework for drug dosage individualization in chronic diseases. In particular, individualized dosages computed with these models by means of an empirical Bayesian approach may produce better results than dosages computed with some methods routinely used in therapeutic drug monitoring. This is further supported by published empirical and theoretical findings that show that random effects linear models may provide accurate representations of phase III and IV steady-state pharmacokinetic data, and may be useful for dosage computations. These models have applications in the design of clinical algorithms for drug dosage individualization in chronic diseases; in the computation of dose correction factors; computation of the minimum number of blood samples from a patient that are necessary for calculating an optimal individualized drug dosage in therapeutic drug monitoring; measure of the clinical importance of clinical, demographic, environmental or genetic covariates; study of drug-drug interactions in clinical settings; the implementation of computational tools for web-site-based evidence farming; design of pharmacogenomic studies; and in the development of a pharmacological theory of dosage individualization.

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Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed‐effects models

Díaz

2016

Statistics in Medicine

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We propose statistical definitions of the individual benefit of a medical or behavioral treatment and of the severity of a chronic illness. These definitions are used to develop a graphical method that can be used by statisticians and clinicians in the data analysis of clinical trials from the perspective of personalized medicine. The method focuses on assessing and comparing individual effects of treatments rather than average effects, and can be used with continuous and discrete responses, including dichotomous and count responses. The method is based on new developments in generalized linear mixed-effects models, which are introduced in this article. To illustrate, analyses of data from the STAR*D clinical trial of sequences of treatments for depression and data from a clinical trial of respiratory treatments are presented. The estimation of individual benefits is also explained.

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Drug Dosage Individualization Based on a Random-Effects Linear Model

Cited by 25 publications

References 19 publications

ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening

ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening

Role of Statistical Random-Effects Linear Models in Personalized Medicine

Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed‐effects models

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