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

Díaz, Francisco Javier Rodríguez

doi:10.1002/sim.7005

Cited by 16 publications

(81 citation statements)

References 26 publications

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“…To investigate the relationships of the response with the unique characteristics of the studied subjects, another family of statistical models, usually called GLMMs (or random-effects (REs) linear models) is used. Using these models can be a valuable tool and a comprehensive conceptual framework for the development of personalized medicine [23].…”

Section: Discussionmentioning

confidence: 99%

Individualizing the dosage of Methylphenidate in children with attention deficit hyperactivity disorder

Shirafkan

Mahmoudi-Gharaei

Fotouhi

et al. 2020

BMC Med Res Methodol

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Background: Attention deficit hyperactivity disorder (ADHD) is one of the most common childhood mental health disorders. Stimulant drugs as the most commonly used treatment and first-line therapy for ADHD have side effects. One of the newest approaches to select the best choices and optimize dosages of medications is personalized medicine.Methods: This historical cohort study was carried out on the data taken from the period of 2008 to 2015. Eligible subjects were included in the study randomly. We used mixed-effects logistic regression models to personalize the dosage of Methylphenidate (MPH) in ADHD. The patients' heterogeneity was considered using subject-specific random effects, which are treated as the realizations of a stochastic process. To recommend a personalized dosage for a new patient, a two-step procedure was proposed. In the first step, we obtained estimates for population parameters. In the second step, the dosage of the drug for a new patient was updated at each follow-up. Results: Of the 221 children enrolled in the study, 169 (76.5%) were male and 52 (23.5%) were females. The overall mean age at the beginning of the study is 82.5 (± 26.5) months. In multivariable mixed logit model, three variables (severity of ADHD, time duration receiving MPH, and dosage of MPH) had a significant relationship with improvement. Based on this model the personalized dosage of MPH was obtained. Conclusions: To determine the dosage of MPH for a new patient, the more the severity of baseline is, the more of an initial dose is required. To recommend the dose in the next times, first, the estimation of random coefficient should be updated. The optimum dose increased when the severity of ADHD increased. Also, the results show that the optimum dose of MPH as one proceeds through the period of treatment will decreased.

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Section: Discussionmentioning

confidence: 99%

Individualizing the dosage of Methylphenidate in children with attention deficit hyperactivity disorder

Shirafkan

Mahmoudi-Gharaei

Fotouhi

et al. 2020

BMC Med Res Methodol

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“…Analyses of primary and secondary outcomes will be conducted on an intention-to-treat basis, so all included patients will be analysed as randomised. Generalised linear mixed models 51 (GLMMs) will be used for analyses of between group differences when all measurements are considered. The GLMM will be adjusted for potential confounders (eg, demographic variables).…”

Section: Methodsmentioning

confidence: 99%

The Supporting Patient Activation in Transition to Home (sPATH) intervention: a study protocol of a randomised controlled trial using motivational interviewing to decrease re-hospitalisation for patients with COPD or heart failure

et al. 2017

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IntroductionDeficient hospital discharging and patients struggling to handle postdischarge self-management have been identified as potential causes of re-hospitalisation rates. Despite an increased interest in interventions aiming to reduce re-hospitalisation rates, there is yet no best evidence on how to support patients in being active participants in their self-management postdischarge. The aim of this paper is to describe the study protocol for an upcoming randomised controlled trial (RCT) of the Supporting Patient in Activation to Home (sPATH) intervention.Methods/analysisThe described study is a randomised, controlled, analysis-blinded, two-site trial, with primary outcome re-hospitalisation within 90 days. In total, 290 participants aged 18 years or older with chronic obstructive pulmonary disease or congestive heart failure who are admitted to hospital and who are living in an own home will be eligible for inclusion into an intervention (n=145) or control group (n=145). Patients who need an interpreter to communicate in Swedish, or who have a diagnosis of dementia or cognitive impairment, will be excluded from inclusion. The sPATH intervention, developed with a theoretical base in the self-determination theory, consists of five postdischarge motivational interviewing sessions (face to face or by phone). The intervention covers the self-management areas medication management, follow-up/care plan, symptoms/signs of worsening condition and relations/contacts with healthcare providers. This RCT will add to the literature on evidence to support patient activation in postdischarge self-management.Ethics and disseminationThe study is approved by the Regional Research Ethics Committee (No. 2014/1498-31/2) in Stockholm, Sweden. The results of the study will be published in peer-reviewed journals and presented at international and national scientific conferences.Trial registration numberNCT02823795; Pre-results.

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“…Two-dimensional personalized medicine (2-PM) models are tools for measuring the severity of a patient's chronic disease and the individual benefits of medical or behavioral treatments [1,2]. The patient's disease severity at a specific point in time is defined as the probability of missing the therapeutic target, and the individual's benefit is therefore measured as the reduction in disease severity produced by the treatment.…”

Section: Introductionmentioning

confidence: 99%

A graphical approach to assess the goodness-of-fit of random-effects linear models when the goal is to measure individual benefits of medical treatments in severely ill patients

Wang

Díaz

2020

BMC Med Res Methodol

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Background: Two-dimensional personalized medicine (2-PM) models are tools for measuring individual benefits of medical treatments for chronic diseases which have potential applications in personalized medicine. These models assume normality for the distribution of random effects. It is necessary to examine the appropriateness of this assumption. Here, we propose a graphical approach to assessing the goodness-of-fit of 2-PM models with continuous responses. Methods: We propose benefit quantile-quantile (BQQ) plots which compare the empirical quantiles of individual benefits from a patient sample predicted through an empirical Bayes (EB) approach versus the quantiles of the theoretical distribution of individual benefits derived from the assumption of normality for the random effects. We examine the performance of the approach by conducting a simulation study that compared 2-PM models with non-normal distributions for the random effects versus models with comparable normal distributions. Cramer-von Mises discrepancies were used to quantify the performance of the approach. The approach was illustrated with data from a clinical trial of imipramine for patients with depression. Results: Simulations showed that BQQ plots were able to capture deviations from the normality assumption for the random effects and did not show any asymmetric deviations from the y = x line when the random effects were normally distributed. For the depression data, the points of the BQQ plot were scattered around closely to the y = x line, without presenting any asymmetric deviations. This implied the adequacy of the normality assumption for the random effects and the goodness-of-fit of the 2-PM model for the imipramine data. Conclusion: BQQ plots are sensitive to violations of the normality assumption for the random effects, suggesting that the approach is a useful tool for examining the goodness-of-fit of random-effects linear models when the goal is to measure individual treatment benefits.

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

Cited by 16 publications

References 26 publications

Individualizing the dosage of Methylphenidate in children with attention deficit hyperactivity disorder

Individualizing the dosage of Methylphenidate in children with attention deficit hyperactivity disorder

The Supporting Patient Activation in Transition to Home (sPATH) intervention: a study protocol of a randomised controlled trial using motivational interviewing to decrease re-hospitalisation for patients with COPD or heart failure

A graphical approach to assess the goodness-of-fit of random-effects linear models when the goal is to measure individual benefits of medical treatments in severely ill patients

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