eselectsim {eselect} | R Documentation |
This function simulates trials with endpoint selection and sample size reassessment for composite binary endpoints based on blinded data. The composite endpoint is assumed to be a binary endpoint formed by a combination of two events (E1 and E2). We assume that the endpoint 1 is more relevant for the clinical question than endpoint 2. This function simulates a trial based on the design parameters and use the algorithm implemented in eselect() to select the primary endpoint and recalculate the sample size accordingly.
eselectsim(
ss_arm,
p0_e1,
OR1,
p0_e2,
OR2,
p0_ce,
p_init = 1,
criteria = "SS",
H0_e1 = FALSE,
H0_e2 = FALSE,
SS_r = TRUE,
alpha = 0.05,
beta = 0.2
)
ss_arm |
numeric parameter, sample size per arm |
p0_e1 |
numeric parameter, probability of occurrence E1 in the control group |
OR1 |
numeric parameter, Odds ratio for the endpoint 1 |
p0_e2 |
numeric parameter, probability of occurrence E2 in the control group |
OR2 |
numeric parameter, Odds ratio for the endpoint 2 |
p0_ce |
numeric parameter, probability of occurrence composite endpoint in the control group |
p_init |
numeric parameter, percentage of sample size used in the interim |
criteria |
decision criteria to choose between the composite endpoint or the endpoint 1 as primary endpoint ("SS": Ratio sample sizes, "ARE": Asymptotic Relative Efficiency). |
H0_e1 |
Simulate under true null hypothesis for the endpoint E1 (TRUE/FALSE). |
H0_e2 |
Simulate under true null hypothesis for the endpoint E2 (TRUE/FALSE). |
SS_r |
Sample size reassessment (TRUE/FALSE). If TRUE, in those cases where the sample size is less than the needed for achieving the pre-specified power, additional subjects are added after recalculating the sample size. If FALSE, no more subjects are added in the study. |
alpha |
Type I error. |
beta |
Type II error. |
This function returns the decision (Decision = 1, meaning the chosen endpoint is the composite endpoint; and Decision = 0, meaning the chosen endpoint is the relevant endpoint) and the statistic to test the primary hypothesis according to the decision.
Bofill Roig, M., Gómez Melis, G., Posch, M., & Koenig, F. (2022). Adaptive clinical trial designs with blinded selection of binary composite endpoints and sample size reassessment. Biostatistics (in press). arXiv e-prints, arXiv-2206 (https://doi.org/10.48550/arXiv.2206.09639).