Cure models to estimate time until hospitalization due to COVID-19

Pedrosa-Laza, Maria; López-Cheda, Ana; Cao, Ricardo

doi:10.1007/s10489-021-02311-8

Cited by 13 publications

(10 citation statements)

References 71 publications

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“…From the applications of the model based on the DB distribution in real data, we observed that the posterior mean for β is not very sensitive to the choice of prior distributions for the parameter β. From the results obtained in this study, we can conclude that the proposed methodology can be very useful for researchers working with discrete medical life data in the presence of a cure fraction, a common situation in both medical and engineering applications [23,32,42,44].…”

Section: Discussionmentioning

confidence: 77%

Discrete Bilal Distribution in the Presence of Right-Censored Data and a Cure Fraction

Freitas

Achcar

Peres

et al. 2022

Stat., optim. inf. comput.

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The statistical literature presents many continuous probability distributions with only one parameter, which are extensively used in the analysis of lifetime data, such as the exponential, the Lindley, and the Rayleigh distributions. Alternatively, the use of discretized versions of these distributions can provide a better fit for the data in many applications. As the novelty of this study, we present inferences for the discrete Bilal distribution (DB) with one parameter introduced by Altun et al. (2020) in the presence of right-censored data and cure fraction. We assume standard maximum likelihood methods based on asymptotic normality of the maximum likelihood estimators and also a Bayesian approach based on MCMC (Markov Chain Monte Carlo) simulation methods to get inferences for the parameters of the discrete BD distribution. The use of the proposed model was illustrated with three examples considering real medical lifetime data sets. From these applications, we concluded that the proposed model based on the discrete DB distribution has good performance even with the inclusion of a cure fraction in comparison to other existing discrete models, such as the DsFx-I, Lindley, Rayleigh, and Burr-Hatke probability distributions. Moreover, the model can be easily implemented in standard existing software, such as the R package. Under a Bayesian approach, we assumed a gamma prior distribution for the parameter of the DB discrete distribution. We also provided a brief sensitivity analysis assuming the half-normal distribution in place of the gamma distribution for the parameter of the DB distribution. From the obtained results of this study, we can conclude that the proposed methodology can be very useful for researchers dealing with medical discrete lifetime data in the presence of right-censored data and cure fraction.

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Section: Discussionmentioning

confidence: 77%

Discrete Bilal Distribution in the Presence of Right-Censored Data and a Cure Fraction

Freitas

Achcar

Peres

et al. 2022

Stat., optim. inf. comput.

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“…We model this state as a cure model [ 38 ]. Cure models are a special type of survival analysis model wherein it is assumed that there is a proportion of subjects who will never experience the event of interest and thus the survival curve will eventually reach a plateau [ 39 ]. In our case, we consider that the cured patients are those who will not be admitted to hospital.…”

Section: Methodsmentioning

confidence: 99%

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

Lafuente

López

Mateo

et al. 2023

Heliyon

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“…To compute the initial values of model parameters to start the NCG algorithm, we first calculated the non-parametric estimates of the cured proportions for nodule categories 1 and 4 (obtained by taking the Kaplan-Meier estimate of the survival function at the largest observed lifetime). Then, we equated these to p 0 (x) with x = 1 and 4, as in (7), for different values of 𝛼 in the interval [0, 1]. By solving the two equations for different values of 𝛼, we obtained a set of values of the regression parameters 𝛽 0 and 𝛽 1 .…”

Section: Real Data Applicationmentioning

confidence: 99%

On the parameter estimation of Box‐Cox transformation cure model

Pal

Roy

2023

Statistics in Medicine

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We propose an improved estimation method for the Box‐Cox transformation (BCT) cure rate model parameters. Specifically, we propose a generic maximum likelihood estimation algorithm through a non‐linear conjugate gradient (NCG) method with an efficient line search technique. We then apply the proposed NCG algorithm to BCT cure model. Through a detailed simulation study, we compare the model fitting results of the NCG algorithm with those obtained by the existing expectation maximization (EM) algorithm. First, we show that our proposed NCG algorithm allows simultaneous maximization of all model parameters unlike the EM algorithm when the likelihood surface is flat with respect to the BCT index parameter. Then, we show that the NCG algorithm results in smaller bias and noticeably smaller root mean square error of the estimates of the model parameters that are associated with the cure rate. This results in more accurate and precise inference on the cure rate. In addition, we show that when the sample size is large the NCG algorithm, which only needs the computation of the gradient and not the Hessian, takes less CPU time to produce the estimates. These advantages of the NCG algorithm allows us to conclude that the NCG method should be the preferred estimation method over the already existing EM algorithm in the context of BCT cure model. Finally, we apply the NCG algorithm to analyze a well‐known melanoma data and show that it results in a better fit when compared to the EM algorithm.

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Cure models to estimate time until hospitalization due to COVID-19

Cited by 13 publications

References 71 publications

Discrete Bilal Distribution in the Presence of Right-Censored Data and a Cure Fraction

Discrete Bilal Distribution in the Presence of Right-Censored Data and a Cure Fraction

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

On the parameter estimation of Box‐Cox transformation cure model

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