eselectsim_ub {eselect}

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

Description

This function simulates trials with endpoint selection and sample size reassessment for composite binary endpoints based on unblinded 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_ub(
  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 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.