getSolutions {OneArmPhaseTwoStudy} | R Documentation |
setupSimon
)getSolutions uses a "simon"-object to calculate two-stage designs as they were described by Simon.
getSolutions(simon = setupSimon(), useCurtailment = FALSE, curtail_All = FALSE, cut = 0, replications = 10000, upperBorder = 0)
simon |
a "simon"-object which will be used to calculate designs. |
useCurtailment |
boolean value determining whether (non-)stochastic curtailment is used. |
curtail_All |
boolean value; if true the effect of (non-)stochastic curtailment will be calculated for different cut points in 0.05 steps starting with the value of the parameter "cut". |
cut |
sets the "cut point" used to calculate the effect of (non-)stochastic curtailment. A study is stopped if the conditional power falls below the value of "cut". |
replications |
number of simulations to estimate the effect of (non-)stochastic curtailment. |
upperBorder |
maximal possible value for n. If set to sero (default) the programm will aproximate a upper border automaticly. |
Simon, R. (1989): Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials 10,1-10.
Kunz C.U., Kieser M (2012): Curtailment in single-arm two-stage phase II oncology trials. Biometrical Journal 54, 445-456
# Example 1: Using the default values designs <- getSolutions() designs <- designs$Solutions designs # Example 2: Setting up a "simon"-object, then calculate designs simon <- setupSimon(alpha = 0.1, beta = 0.2, p0 = 0.3, p1 = 0.5) designs <- getSolutions(simon)$Solutions designs # Esample 3: Calculating designs and simulating the influence of # stochastic curtailment for each design. simon <- setupSimon(alpha = 0.1, beta = 0.2, p0 = 0.3, p1 = 0.5) designs <- getSolutions(simon, useCurtailment = TRUE, curtail_All = TRUE, cut = 0.3) #List containing the found designs, the influence of stochastic curtailment # and the regarding stopping rules. designs