OC.rule.tite {stoppingrule} | R Documentation |
Operating Characteristics Function (TITE Method)
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.
Usage
OC.rule.tite(rule, ps, ps.compt = NULL, MC, tau, A, family = "power", s = 1)
Arguments
rule |
A |
ps |
Vector of cumulative incidence probabilities for toxicity at time |
ps.compt |
Vector of cumulative incidence probabilities for competing risks at time |
MC |
Number of Monte Carlo replicated datasets to simulate |
tau |
Length of observation period |
A |
Length of accrual period |
family |
Event time distribution, choices including power distribution ('power') and Weibull distribution ('weibull') |
s |
Shape parameter for Weibull distribution or power parameter for power distribution |
Details
Times are generated for each event cause so that its marginal distribution follows a Weibull or power family distribution as specified by the user.
For the Weibull distribution, the cumulative distribution function is 1- \exp(- \lambda t^s), t \ge 0
, where \lambda
is the rate parameter and s
is the shape parameter.
The power distribution has the cumulative distribution function k (t /\tau)^s, 0 \le t \le \tau k^{-1/s}
, where s
is the power parameter and k
is the value at t=\tau
.
For the toxicity event distribution, the Weibull rate parameter is \lambda = - \log(1-p) / \tau
and the power parameter is k = p
, where p
is the cumulative incidence of toxicity at t=\tau
. For competing risk events' distribution, \lambda = - 1 / \tau \log(1-p_c)
and k = p_c
, where p_c
is the cumulative incidence of competing events at t=\tau
.
Value
A matrix with columns containing the toxicity probabilities ps
, competing risk probability (0 for survival outcome), the corresponding rejection probabilities, and the corresponding expected number of events. If tau
and A
are also specified, the expected numbers of enrolled patients and the expected calendar time at the point of stopping/study end are also included.
Examples
## Not run:
# Bayesian beta-extended binomial method in 50 patient cohort at 10% level,
# expected toxicity probability of 20%
bb_rule = calc.rule.tite(n=50,p0=0.20,alpha=0.10,type="BB",param=c(2,8))
# Compute operating characteristics at toxicity probabilities of 20%, 25%, 30%, 35%, and 40%
OC.rule.tite(rule=bb_rule,ps=seq(0.2,0.4,0.05), MC =1000, tau=30,A=730, family = 'weibull', s = 2)
## End(Not run)