semicure |
Semiparametric cure model |
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DESCRIPTION
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Fit a semiparametric cure model.
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USAGE
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semicure(formula = formula(data), cureform, data = sys.parent(), link = "logit", model = c("glm", "gam"), emmax = 100, eps = 1e-4, debug = T, tail = c("zero", "none"), savedata = T, ...)
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ARGUMENTS
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formula:
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a formula expression as for other regression models. It specifies the effects of covariates (on the righthand side of the ~) on the failure time (on the lefthand side of the ~) of uncured patients through the proportional hazards assumption.
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cureform:
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a formula expression as for formula without the response. It specifies the effect of covariates (on the righthand side of the ~) on the cure rate. A covariate may be used in both formula and cureform. The absence of cureform implies no cured patients. The default assumes no cured
patient.
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data:
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a data frame in which to interpret the variables named in the formulas.
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link:
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the link function relates the cure rate to covariates in cureform. The default is the logit link.
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model:
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the model to fit the effects of cobariates on the cure rate. It can be one of "glm" and "gam". The default is "glm". If there are additive effects of covariates in cureform, "gam" should be used.
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emmax:
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the number of iterations in the EM algorithm.
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eps:
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tolerance for convergence. Iteration stops once the relative change in
deviance is less than eps.
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tail:
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the method to complete the tail of the baseline survival function. It can be one of "zero" and "none". The default is "zero".
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debug:
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a logical variable. If TRUE, some debug information printed.
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savedata:
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a logical variable. If TRUE, the orginal data set combined with the estimated cure rate for each patient is saved into the final semicure object.
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VALUE
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an object of class semicure is returned. It can be examined by coef(), print(), summary() and predict().
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SEE ALSO
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coxph, glm, gam, SEMICURE main page.
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REFERENCES
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Peng, Y. (2003), Fitting semiparametric cure models, Computational Statistics and Data Analysis 41:481--490.
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EXAMPLES
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> semicure(Surv(time, cens) ~ transplant, ~ transplant, data = goldman.data)
Program is running ............. done.
Call:
semicure(formula = Surv(time, cens) ~ transplant, cureform = ~ transplant,
data = goldman.data)
Failure time distribution model:
Call:
coxph(formula = Surv(time, cens) ~ transplant + offset(log(uncureprob)),
subset = uncureprob != 0, method = "breslow")
coef exp(coef) se(coef) z p
transplant 0.317 1.37 0.13 2.43 0.015
Likelihood ratio test=5.96 on 1 df, p=0.0146 n= 80
Cure probability model:
Call:
glm(formula = uncureprob ~ transplant, family = binomial(link = "logit"))
Coefficients:
(Intercept) transplant
1.216835 0.1884882
Degrees of Freedom: 91 Total; 89 Residual
Residual Deviance: 91.62084
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