TTC_wei {curesurv}R Documentation

TTC_wei function

Description

calculates the probability Pi(t) of being cured at a given time t after diagnosis knowing that he/she was alive up to time t. In other words, Pi(t)=(probability of being cured and alive up to time t given xi)/ (probability of being alive up to time t given xi)

Note that this function is for mixture cure model with Weibull distribution considered for uncured patients.

Usage

TTC_wei(z_pcured = z_pcured, z_ucured = z_ucured, theta, epsilon = 0.05)

Arguments

z_pcured

covariates matrix acting on cure proportion

z_ucured

covariates matrix acting on survival function of uncured

theta

estimated parameters

epsilon

value fixed by user to estimate the TTC \text{Pi}(t)\geq (1-\epsilon). By default \epsilon = 0.05.

Author(s)

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. 2021 Dec;77(4):1289-1302. doi: 10.1111/biom.13361. Epub 2020 Sep 12. PMID: 32869288. (pubmed)

Boussari O, Romain G, Remontet L, Bossard N, Mounier M, Bouvier AM, Binquet C, Colonna M, Jooste V. A new approach to estimate time-to-cure from cancer registries data. Cancer Epidemiol. 2018 Apr;53:72-80. doi: 10.1016/j.canep.2018.01.013. Epub 2018 Feb 4. PMID: 29414635. (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)


[Package curesurv version 0.1.2 Index]