| Title: | Design of Clinical Trials with Survival Endpoints Based on Binary Responses |
|---|---|
| Description: | Sample size and effect size calculations for survival endpoints based on mixture survival-by-response model. The methods implemented can be found in Bofill, Shen & Gómez (2021) <arXiv:2008.12887>. |
| Authors: | Marta Bofill Roig [aut, cre], Guadalupe Gomez Melis [ctb], Yu Shen [ctb] |
| Maintainer: | Marta Bofill Roig <[email protected]> |
| License: | GPL-3 |
| Version: | 1.3 |
| Built: | 2025-02-20 04:13:55 UTC |
| Source: | https://github.com/martabofillroig/survmixer |
The function 'survm_effectsize' calculates the effect size in terms of the difference of restricted mean survival times (RMST) according to the information on responders and non-responders.
survm_effectsize( ascale0_r, ascale0_nr, delta_p, p0, bshape0 = 1, bshape1 = 1, ascale1_r, ascale1_nr, tau, Delta_r = NULL, Delta_0 = NULL, Delta_nr = NULL, anticipated_effects = FALSE )survm_effectsize( ascale0_r, ascale0_nr, delta_p, p0, bshape0 = 1, bshape1 = 1, ascale1_r, ascale1_nr, tau, Delta_r = NULL, Delta_0 = NULL, Delta_nr = NULL, anticipated_effects = FALSE )
ascale0_r |
scale parameter for the Weibull distribution in the control group for responders |
ascale0_nr |
scale parameter for the Weibull distribution in the control group for non-responders |
delta_p |
effect size for the response rate |
p0 |
event rate for the response |
bshape0 |
shape parameter for the Weibull distribution in the control group |
bshape1 |
shape parameter for the Weibull distribution in the intervention group |
ascale1_r |
scale parameter for the Weibull distribution in the intervention group for responders |
ascale1_nr |
scale parameter for the Weibull distribution in the intervention group for non-responders |
tau |
follow-up |
Delta_r |
RMST difference between intervention and control groups for responders |
Delta_0 |
RMST difference between responders and non-responders in the control group |
Delta_nr |
RMST difference between intervention and control groups for non-responders |
anticipated_effects |
Logical parameter. If it is TRUE then the effect size is computed based on previous information on the effect sizes on response rate and survival-by-responses (that is, based on Delta_r, Delta_0, Delta_nr); otherwise is based on the distributional parameters (ascale0_r, ascale0_nr, ascale1_r, ascale1_nr, bshape0, bshape1). |
This function returns the overall mean survival improvement (RMST difference between groups) and it also includes the mean survival improvement that would be assumed for each responders and non-responders.
Marta Bofill Roig.
Design of phase III trials with long-term survival outcomes based on short-term binary results. Marta Bofill Roig, Yu Shen, Guadalupe Gomez Melis. arXiv:2008.12887
survm_effectsize(ascale0_r=8,ascale0_nr=5.6,ascale1_r=36,ascale1_nr=5.6,delta_p=0.2,p0=0.2,tau=5)survm_effectsize(ascale0_r=8,ascale0_nr=5.6,ascale1_r=36,ascale1_nr=5.6,delta_p=0.2,p0=0.2,tau=5)
The function 'survm_samplesize' calculates the sample size according to the distributional parameters of the responders and non-responders.
survm_samplesize( ascale0_r, ascale0_nr, ascale1_r, ascale1_nr, delta_p, p0, m0_r, m0_nr, diffm_r, diffm_nr, S0_r, S0_nr, diffS_r, diffS_nr, Delta_r, Delta_nr, ascale_cens, tau, bshape0 = 1, bshape1 = 1, all_ratio = 0.5, alpha = 0.025, beta = 0.2, set_param = 0 )survm_samplesize( ascale0_r, ascale0_nr, ascale1_r, ascale1_nr, delta_p, p0, m0_r, m0_nr, diffm_r, diffm_nr, S0_r, S0_nr, diffS_r, diffS_nr, Delta_r, Delta_nr, ascale_cens, tau, bshape0 = 1, bshape1 = 1, all_ratio = 0.5, alpha = 0.025, beta = 0.2, set_param = 0 )
ascale0_r |
scale parameter for the Weibull distribution in the control group for responders |
ascale0_nr |
scale parameter for the Weibull distribution in the control group for non-responders |
ascale1_r |
scale parameter for the Weibull distribution in the intervention group for responders |
ascale1_nr |
scale parameter for the Weibull distribution in the intervention group for non-responders |
delta_p |
effect size for the response rate |
p0 |
event rate for the response |
m0_r |
survival mean for responders in the control group |
m0_nr |
survival mean for non-responders in the control group |
diffm_r |
difference in survival means between groups for responders |
diffm_nr |
difference in survival means between groups for responders |
S0_r |
tau-year survival rates for responders in the control group |
S0_nr |
tau-year survival rates for non-responders in the control group |
diffS_r |
difference in tau-year survival rates for responders |
diffS_nr |
difference in tau-year survival rates for non-responders |
Delta_r |
restricted mean survival times (RMST) difference between intervention and control groups for responders |
Delta_nr |
RMST difference between intervention and control groups for non-responders |
ascale_cens |
distributional parameter for the exponential distribution for the censoring |
tau |
follow-up |
bshape0 |
shape parameter for the Weibull distribution in the control group |
bshape1 |
shape parameter for the Weibull distribution in the intervention group |
all_ratio |
allocation ratio. The ratio of numbers of participants allocated in the control group. By default is assumed 1:1 (i.e., all_ratio=0.5) |
alpha |
type I error |
beta |
type II error |
set_param |
Set of parameters to be used for the responders/non-responders survival functions If the set of parameters is =1, then the sample size is computed using the survival means (m0_r,m0_nr,diffm _r,diffm_nr); if set_param=2, it is computed using the tau-year survival rates (S0_r,S0_nr,diffS_r,diffS_nr); if set_param=2, it is computed using the RMSTs and survival rates (Delta_r,Delta_nr,S0_r,S0_nr). If set_param=0, the computation is based on the distributional parameters (ascale0_r, ascale0_nr, ascale1_r, ascale1_nr). |
This function returns the total sample size needed and the expected effect size for overall survival (RMST difference between groups).
Marta Bofill Roig.
Design of phase III trials with long-term survival outcomes based on short-term binary results. Marta Bofill Roig, Yu Shen, Guadalupe Gomez Melis. arXiv:2008.12887
The function 'survmixture_f' computes the survival distribution as a mixture of responders and non-responders. The responders and non-responders distributions are assumed to be Weibull distributions.
survmixture_f(t, ascale_r, ascale_nr, bshape = 1, p)survmixture_f(t, ascale_r, ascale_nr, bshape = 1, p)
t |
time at which the survival distribution is evaluated |
ascale_r |
scale parameter for the Weibull distribution for responders |
ascale_nr |
scale parameter for the Weibull distribution for non-responders |
bshape |
shape parameter for the Weibull distribution |
p |
event rate for the response |
This function returns the survival function evaluated at t based on a mixture model of responders and non-responders.
Marta Bofill Roig.
Design of phase III trials with long-term survival outcomes based on short-term binary results. Marta Bofill Roig, Yu Shen, Guadalupe Gomez Melis. arXiv:2008.12887
survmixture_f(t=0.2,ascale_r=8,ascale_nr=5.6,p=0.2)survmixture_f(t=0.2,ascale_r=8,ascale_nr=5.6,p=0.2)