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Type: Artigo
Title: Self-modeling ordinal regression with time invariant covariates – an application to prostate cancer data
Author: Shirazi, Aliakbar Mastani
Das, Kalyan
Pinheiro, Aluisio
Abstract: In a prostate cancer study, the severity of genito-urinary (bladder) toxicity is assessed for patients who were given different doses of radiation. The ordinal responses (severity of side effects) are recorded longitudinally along with the cancer stage of a patient. Differences among the patients due to time-invariant covariates are captured by the parameters. To build up a suitable framework for an analysis of such data, we propose the use of self-modeling ordinal longitudinal model where the conditional cumulative probabilities for a category of an outcome have a relation with shape-invariant model. Since patients suffering from a common disease usually exhibit a similar pattern, it is natural to build up a nonlinear model that is shape invariant. The model is essentially semi-parametric where the population time curve is modeled with penalized regression spline. Monte Carlo expectation maximization technique is used to estimate the parameters of the model. A simulation study is also carried out to justify the methodology used.
Subject: Estatística não paramétrica
Teoria do
Estatística matemática - Estudos longitudinais
Modelos lineares (Estatística)
Estatística ordinal
Nonparametric statistics
Spline theory
Mathematical statistics - Longitudinal studies
Linear models (Statistics)
Order statistics
Country: Reino Unido
Editor: Sage
Rights: fechado
Identifier DOI: 10.1177/0962280215594493
Date Issue: 2017
Appears in Collections:IMECC - Artigos e Outros Documentos

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