eselectsim: Simulation trials with endpoint selection and sample size...

In eselect: Adaptive Clinical Trial Designs with Endpoint Selection and Sample Size Reassessment

Simulation trials with endpoint selection and sample size reassessment for composite endpoints based on blinded data

Description

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.

Usage

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
)

Arguments

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.

Value

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.

References

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).

eselect documentation built on Feb. 16, 2023, 8:11 p.m.