print.curesurv {curesurv} | R Documentation |
print a curesurv object
Description
Print an object of class "curesurv"
Usage
## S3 method for class 'curesurv'
print(x, digits = max(1L, getOption("digits") - 3L), signif.stars = FALSE, ...)
Arguments
x |
an object of class "curesurv". |
digits |
minimum number of significant digits to be used for most numbers. |
signif.stars |
logical; if TRUE, P-values are additionally encoded visually as "significance stars" in order to help scanning of long coefficient tables. |
... |
additional options |
Value
an object of class "curesurv" representing the fit. See curesurv
for details.
Author(s)
Juste Goungounga, Judith Breaud, Eugenie Blandin, Olayide Boussari, Valerie Jooste
References
Boussari O, Bordes L, Romain G, Colonna M, Bossard N, Remontet L, Jooste V. Modeling excess hazard with time-to-cure as a parameter. Biometrics. 2020 Aug 31. doi: 10.1111/biom.13361. Epub ahead of print. PMID: 32869288. (pubmed)
Phillips N, Coldman A, McBride ML. Estimating cancer prevalence using mixture models for cancer survival. Stat Med. 2002 May 15;21(9):1257-70. doi: 10.1002/sim.1101. PMID: 12111877. (pubmed)
De Angelis R, Capocaccia R, Hakulinen T, Soderman B, Verdecchia A. Mixture models for cancer survival analysis: application to population-based data with covariates. Stat Med. 1999 Feb 28;18(4):441-54. doi: 10.1002/(sici)1097-0258(19990228)18:4<441::aid-sim23>3.0.co;2-m. PMID: 10070685. (pubmed)
See Also
predict.curesurv()
, curesurv()
, browseVignettes("curesurv")
Examples
library("curesurv")
library("survival")
# overall survival setting
# Mixture cure model with Weibull function for the uncured patients survival:
# no covariate
fit_ml0 <- curesurv(Surv(time_obs, event) ~ 1 | 1,
model = "mixture", dist = "weib",
data = testiscancer,
method_opt = "L-BFGS-B")
print(fit_ml0)