curesurv (version 0.1.2)

print.curesurv: print a curesurv object

Description

Print an object of class "curesurv"

Usage

# S3 method for curesurv
print(x, digits = max(1L, getOption("digits") - 3L), signif.stars = FALSE, ...)

Value

an object of class "curesurv" representing the fit. See curesurv for details.

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

Author

Juste Goungounga, Judith Breaud, Olayide Boussari, Laura Botta, 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)