OC.rule.surv {stoppingrule} | R Documentation |
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)
Arguments
rule |
A |
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 |
A |
Length of the enrollment period. Only required if |
s |
Shape parameter for the Weibull distribution used to simulate event times. Default is |
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.
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 total
follow up time are also included.
Examples
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)