stoppingrule (version 0.6)

calc.rule.surv: Stopping Rule Calculation (Survival Data)

Description

Calculate a stopping rule for safety monitoring for time-to-event data

Usage

calc.rule.surv(n, p0, alpha, type, tau, maxInf = "expected", param = NULL)

Value

A rule.surv object, which is a list with the following elements: Rule, a two-column matrix with total follow-up times for each stage and their corresponding rejection boundaries; n; p0; alpha; type; tau; param; and cval, the boundary parameter for the rule

Arguments

n

Maximum sample size for safety monitoring

p0

The probability of a toxicity occurring in tau units of time under the null hypothesis

alpha

The nominal type I error/false positive rate for the stopping rule, under an assumption that the cumulative number of events follows a Poisson process over the study duration.

type

The method used for constructing the stopping rule. Choices including a Pocock test ("Pocock"), a O'Brien-Fleming test ("OBF"), a Wang-Tsiatis test ("WT"), the Bayesian Gamma-Poisson method ("GP"), a truncated sequential probability ratio test ("SPRT"), and a maximized SPRT ("MaxSPRT")

tau

Length of observation period

maxInf

Specification of the maximum information (maximum exposure time) used for designing the stopping rule. Options include the expected exposure time for n patients used H0 ("expected") and the maximum possible exposure time ("maximum"). Default is "expected" (expected exposure time in cohort).

param

A vector of the extra parameter(s) needed for certain stopping rule methods. For Wang-Tsiatis tests, this is the Delta parameter. For truncated SPRT, this is the targeted alternative toxicity probability p1. For Bayesian Gamma-Poisson model, this is the vector of hyperparameters (shape,rate) for the gamma prior on the toxicity event rate.

References

Martens, M. J., Lian, Q., Geller, N. L., Leifer, E. S., and Logan, B. L. (2025). Sequential monitoring of time-to-event safety endpoints in clinical trials. Clinical Trials, 22(3), 267–278.

Kashiwabara, K., Matsuyama, Y., and Ohashi, Y. (2014). A Bayesian stopping rule for sequential monitoring of serious adverse events. Therapeutic Innovation & Regulatory Science, 48, 444–452.

Kulldorff, M., Davis, R. L., Kolczak, M., Lewis, E., Lieu, T., and Platt, R. (2011). A maximized sequential probability ratio test for drug and vaccine safety surveillance. Sequential Analysis, 30(1), 58–78.

Zacks, S. and Mukhopadhyay, N. (2006). Exact risks of sequential point estimators of the exponential parameter. Sequential Analysis, 25(2), 203–226.

Examples

if (FALSE) # Survival Pocock test in 50 patient cohort at 10% level, expected toxicity
# probability of 20%, 100 day observation period
calc.rule.surv(n=50,p0=0.20,alpha=0.10,type="Pocock",tau=100)

# Survival Wang-Tsiatis test with Delta = 0.25 in 50 patient cohort at 10% level,
# expected toxicity probability of 20%, 100 day observation period
calc.rule.surv(n=50,p0=0.20,alpha=0.10,type="WT",tau=100,param=0.25)

# Gamma-Poisson test with hyperparameters (1, 1000) in 100 patient cohort at 5% level,
# expected toxicity probability of 10%, 60 day observation period
calc.rule.surv(n=100,p0=0.10,alpha=0.05,type="GP",tau=60,param=c(1,1000))

# Truncated exponential SPRT with p1 = 0.3 in 100 patient cohort at 5% level,
# expected toxicity probability of 10%, 60 day observation period
calc.rule.surv(n=100,p0=0.10,alpha=0.05,type="SPRT",tau=60,param=0.3)