| Title: | Estimation for the Power Series Cure Rate Model |
|---|---|
| Description: | Provide estimation for particular cases of the power series cure rate model <doi:10.1080/03610918.2011.639971>. For the distribution of the concurrent causes the alternative models are the Poisson, logarithmic, negative binomial and Bernoulli (which are includes in the original work), the polylogarithm model <doi:10.1080/00949655.2018.1451850> and the Flory-Schulz <doi:10.3390/math10244643>. The estimation procedure is based on the EM algorithm discussed in <doi:10.1080/03610918.2016.1202276>. For the distribution of the time-to-event the alternative models are slash half-normal, Weibull, gamma and Birnbaum-Saunders distributions. |
| Authors: | Diego Gallardo [aut, cre], Reza Azimi [ctb] |
| Maintainer: | Diego Gallardo <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.1 |
| Built: | 2024-11-24 03:24:57 UTC |
| Source: | https://github.com/cran/PScr |
This function provides the maximum likelihood estimation based on the EM algorithm for the Power Series cure rate model
EM.PScr(t, delta, z, model = 1, dist = 1, max.iter = 1000, prec = 1e-04)EM.PScr(t, delta, z, model = 1, dist = 1, max.iter = 1000, prec = 1e-04)
t |
observed times |
delta |
failure indicators |
z |
matrix of covariates (with n rows and r columns) |
model |
distribution to be used for the concurrent causes: 1 for Poisson, 2 for logarithmic, 3 for negative binomial, 4 for bernoulli and 5 for polylogarithm (Gallardo et al. 2018). 6 for Flory-Schulz (Azimi et al. 2022). |
dist |
distribution to be used for the time-to-event: 1 for slash half-normal (Gallardo et al., 2022), 2 for Weibull, 3 for gamma and 4 for Birnbaum-Saunders. |
max.iter |
maximum number of iterations to be used by the algorithm |
prec |
precision (in absolute value) for the parameters to stop the algorithm. |
The EM algorithm for the model is implemented as in Gallardo et al. (2017).
estimate |
a matrix containing the estimated parameters and their standard error |
loglike |
the estimated log-likelihood function evaluated in the maximum likelihood estimators |
AIC |
the Akaike information criterion |
BIC |
the Bayesian (also known as Schwarz) information criterion |
Diego I. Gallardo and Reza Azimi
Azimi, R, Esmailian, M, Gallardo DI and Gomez HJ. (2022). A New Cure Rate Model Based on Flory-Schulz Distribution: Application to the Cancer Data. Mathematics 10, 4643
Gallardo DI, Gomez YM and De Castro M. (2018). A flexible cure rate model based on the polylogarithm distribution. Journal of Statistical Computation and Simulation 88 (11), 2137-2149
Gallardo DI, Gomez YM, Gomez HJ, Gallardo-Nelson MJ, Bourguignon M. (2022) The slash half-normal distribution applied to a cure rate model with application to bone marrow transplantation. Mathematics, Submitted.
Gallardo DI, Romeo JS and Meyer R. (2017). A simplified estimation procedure based on the EM algorithm for the power series cure rate model. Communications in Statistics-Simulation and Computation 46 (8), 6342-6359.
require(mstate) data(ebmt4) attach(ebmt4) t = srv / 365.25 # Time in years delta=srv.s prophy=as.factor(proph) year2=ifelse(year=="1985-1989",0,1) z=t(model.matrix(~proph-1)) #Computes the estimation for Poisson-Slash half-normal cure rate model EM.PScr(t, delta, z, model=1, dist=1) #Computes the estimation for Flory-Schulz-Slash half-normal cure rate model EM.PScr(t, delta, z, model=6, dist=1)require(mstate) data(ebmt4) attach(ebmt4) t = srv / 365.25 # Time in years delta=srv.s prophy=as.factor(proph) year2=ifelse(year=="1985-1989",0,1) z=t(model.matrix(~proph-1)) #Computes the estimation for Poisson-Slash half-normal cure rate model EM.PScr(t, delta, z, model=1, dist=1) #Computes the estimation for Flory-Schulz-Slash half-normal cure rate model EM.PScr(t, delta, z, model=6, dist=1)