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curesurv (version 0.1.2)

TTC_wei: 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

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)