Cure Rate Model with Measurement Error

Mizoi, Marcia F.; Bolfarine, Heleno; Pedroso-de-Lima, Antonio Carlos

doi:10.1080/03610910601095991

Cited by 15 publications

(18 citation statements)

References 11 publications

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“…The first paper that estimated the promotion time cure model (2) in the presence of measurement error is Mizoi et al (2007). The authors considered the case where only one covariate is measured with error (say X1), and they assumed that the model is fully parametric, with F (•) equal to the distribution of a Weibull random variable.…”

Section: Measurement Errorsmentioning

confidence: 99%

Cure Models in Survival Analysis

Amico

Keilegom

2018

Annu. Rev. Stat. Appl.

129

106

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When analysing time-to-event data it often happens that a certain fraction of the data corresponds to subjects that will never experience the event of interest. These event times are considered as infinite and the subjects are said to be cured. Survival models that take this feature into account are commonly referred to as cure models. This paper gives a review of the literature on cure regression models in which the event time (response) is subject to random right censoring and has a positive probability to be equal to infinity. Contents FACULTY OF ECONOMICS AND BUSINESS Naamsestraat 69 bus 3500 3000 LEUVEN, BELGIË tel.

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Section: Measurement Errorsmentioning

confidence: 99%

Cure Models in Survival Analysis

Amico

Keilegom

2018

Annu. Rev. Stat. Appl.

129

106

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“…In this section, we present a version of the cure rate model introduced in [4] and discussed among others by authors of references [5,8,16,17]. Suppose that for an individual i in the population, we let M i denote the number of metastatic-competent tumour cells for that individual left active after the initial treatment.…”

Section: Cure Fraction Modelmentioning

confidence: 99%

“…A unified approach is pursued in [7], which refers to this model as the promotion time model. Among other recent extensions we can cite a measurement error investigation in [8] and a general class of transformation cure frailty models considered by Yin [9].…”

Section: Introductionmentioning

confidence: 99%

Cure rate survival models with missing covariates: a simulation study

Fonseca

Valença

Bolfarine

2013

Journal of Statistical Computation and Simulation

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In this paper we study the cure rate survival model involving a competitive risk structure with missing categorical covariates. A parametric distribution that can be written as a sequence of one-dimensional conditional distributions is specified for the missing covariates. We consider the missing data at random situation so that the missing covariates may depend only on the observed ones. Parameter estimates are obtained by using the EM algorithm via the method of weights. Extensive simulation studies are conducted and reported to compare estimates efficiency with and without missing data. As expected, the estimation approach taking into consideration the missing covariates presents much better efficiency in terms of mean square errors than the complete case situation. Effects of increasing cured fraction and censored observations are also reported. We demonstrate the proposed methodology with two real data sets. One involved the length of time to obtain a BS degree in Statistics, and another about the time to breast cancer recurrence.

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“…We also consider the standard approach to model the uncertainty about the variance eliciting an inverse-gamma prior distribution for σϵ2. Following the literature, 13,16,27 we fix σϵ2 in a known value. By doing that, we provide a Bayesian formulation for the Mizoi et al.’s model, 16 as a particular case, if additionally the error is normally distributed.…”

Section: Introductionmentioning

confidence: 99%

Bayesian cure fraction models with measurement error in the scale mixture of normal distribution

Marinho

Loschi

2020

Stat Methods Med Res

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Cure fraction models have been widely used to model time-to-event data when part of the individuals survives long-term after disease and are considered cured. Most cure fraction models neglect the measurement error that some covariates may experience which leads to poor estimates for the cure fraction. We introduce a Bayesian promotion time cure model that accounts for both mismeasured covariates and atypical measurement errors. This is attained by assuming a scale mixture of the normal distribution to describe the uncertainty about the measurement error. Extending previous works, we also assume that the measurement error variance is unknown and should be estimated. Three classes of prior distributions are assumed to model the uncertainty about the measurement error variance. Simulation studies are performed evaluating the proposed model in different scenarios and comparing it to the standard promotion time cure fraction model. Results show that the proposed models are competitive ones. The proposed model is fitted to analyze a dataset from a melanoma clinical trial assuming that the Breslow depth is mismeasured.

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Cure Rate Model with Measurement Error

Cited by 15 publications

References 11 publications

Cure Models in Survival Analysis

Cure Models in Survival Analysis

Cure rate survival models with missing covariates: a simulation study

Bayesian cure fraction models with measurement error in the scale mixture of normal distribution

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