stoppingrule (version 0.6)

OC.rule.surv: Operating Characteristics Function (Survival Data)

Description

Compute operating characteristics for a stopping rule at a set of toxicity rates. Characteristics calculated include the overall rejection probability, the expected number of patients evaluated, and the expected number of events for time-to-event data.

Usage

OC.rule.surv(rule, ps, MC, A, s = 1)

Value

A matrix with columns containing the toxicity probabilities ps, the corresponding rejection probabilities, and the corresponding expected number of events. If MC is not NULL, the expected number of enrolled patients and expected calendar time at the point of stopping/study end are also included.

Arguments

rule

A rule.surv object calculated by calc.rule.surv() function

ps

A vector of toxicity probabilities at which the operating characteristics will be computed

MC

Number of Monte Carlo replicates to simulate for estimating operating characteristics. If MC = 0, a Poisson process assumption on the event process is used to compute operating characteristics.

A

Length of the enrollment period. Only required if MC > 0.

s

Shape parameter for the Weibull distribution used to simulate event times. Default is s = 1 (exponential). Only required if MC > 0.

Details

Operating characteristics are generated either by Monte Carlo estimation or computed directly under a Poisson process assumption for the event process over time. The Monte Carlo approach assumes a random uniform accrual distribution and a Weibull event time distribution with distribution function \(exp(-\lambda * t^s)\), so it requires specification of the enrollment period length and shape parameter of the event distribution.

Examples

if (FALSE)  poc_rule = calc.rule.surv(n=50,p0=0.20,alpha=0.10,type="Pocock",tau=100)
gp_rule = calc.rule.surv(n=50,p0=0.20,alpha=0.10,type="GP",tau=60,param=c(1,1000))
OC.rule.surv(rule=poc_rule,ps=seq(0.2,0.4,0.05),MC=0)
OC.rule.surv(rule=gp_rule,ps=seq(0.2,0.4,0.05),MC=0)

set.seed(82426499)
ps = seq(0.15,0.35,0.05)
wt_rule = calc.rule.surv(n=46,p0=0.15,alpha=0.10,type="WT",tau=100,param=0.25)
OC.rule.surv(rule=wt_rule,ps=ps,MC=1000,A=1095)

p1h = 0.3418071
sp_rule = calc.rule.surv(n=46,p0=0.15,alpha=0.10,type="SPRT",tau=100,param=p1h)
OC.rule.surv(rule=sp_rule,ps=ps,MC=1000,A=1095)

gp_rule = calc.rule.surv(n=46,p0=0.15,alpha=0.10,type="GP",tau=100,
                          param=11.5*c(-log(1-0.15),100))
OC.rule.surv(rule=gp_rule,ps=ps,MC=1000,A=1095)