A flexible mixed‐effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients

Abstract

We develop a new modeling approach to enhance a recently proposed method to detect increases of contrast-enhancing lesions (CELs) on repeated magnetic resonance imaging, which have been used as an indicator for potential adverse events in multiple sclerosis clinical trials. The method signals patients with unusual increases in CEL activity by estimating the probability of observing CEL counts as large as those observed on a patient's recent scans conditional on the patient's CEL counts on previous scans. This conditional probability index (CPI), computed based on a mixed-effect negative binomial regression model, can vary substantially depending on the choice of distribution for the patient-specific random effects. Therefore, we relax this parametric assumption to model the random effects with an infinite mixture of beta distributions, using the Dirichlet process, which effectively allows any form of distribution. To our knowledge, no previous literature considers a mixed-effect regression for longitudinal count variables where the random effect is modeled with a Dirichlet process mixture. As our inference is in the Bayesian framework, we adopt a meta-analytic approach to develop an informative prior based on previous clinical trials. This is particularly helpful at the early stages of trials when less data are available. Our enhanced method is illustrated with CEL data from 10 previous multiple sclerosis clinical trials. Our simulation study shows that our procedure estimates the CPI more accurately than parametric alternatives when the patient-specific random effect distribution is misspecified and that an informative prior improves the accuracy of the CPI estimates.

Lung Mechanics of Mechanically Ventilated Patients With COVID-19: Analytics With High-Granularity Ventilator Waveform Data

“…These curves were drawn for each individual patient and survivors and non-survivors were denoted with different colors. Risk factors for IEE and DT were explored with mixed negative binomial regression models, which was a generalization of the Poisson regression allowing for the conditional variance exceeds the conditional mean (22). Random-effects was allowed for intercepts to account for between-subject variance.…”

El impacto de especificar incorrectamente la distribución de los efectos aleatorios en las estimaciones de modelos lineales generalizados mixtos

“…En la literatura estadística, algunos autores han abordado los efectos de la especificación incorrecta de la distribución de los efectos aleatorios en los modelos lineales generalizados mixtos con respuesta normal y binaria (Neuhaus et al 1992, Heagerty & Kurland 2001, Neuhaus & McCulloch 2006, Litière et al 2007, Komàrek & Lesaffre 2008, Huang 2009, Neuhaus & McCulloch 2011b, pero han sido pocos los trabajos en los que se han analizado modelos lineales generalizados mixtos con respuesta Poisson (Fabio et al 2012, Milanzi et al 2012, Cook et al 2007) y con respuesta binomial negativa (Kondo et al 2015, Zhao et al 2014. Por lo anterior, es que el objetivo de este artículo identificar el impacto de la especificación incorrecta de la distribución de los efectos aleatorios en las inferencias sobre los parámetros fijos y los componentes de varianza en los modelos lineales generalizados mixtos.…”