OptInterim (version 3.0.1)

plot.OptimDes: Plot efficiency of optimal two-stage or three-stage designs as a function of the total sample size or study length

Description

Output from function OptimDes is used to display the ETSL, ES and EDA for a two-stage or three-stage design relative to a single-stage design as a function of the combined-stage sample size or study length.

Usage

"plot"(x, xscale = "t", l.type = 1:5, l.col =
c("blue", "green", "purple", "red", "dark red"), CMadj=F,...)

Arguments

x

Output from function OptimDes.

xscale

Scale of the x-axis. "t" for combined-stage study length. "n" for combined sample size. Default is t.

l.type

Line types for the plot. Default is 1-5.

l.col

Line colors for the plot. Default is "blue" for ETSL, "green" for EDA, "purple" for ES, "red" for t1 and "dark red" for t2 if it's a three-stage design.

CMadj

If true, the sample sizes and times are adjusted by the ratio of the exact binomial to asymptotic normal sample size for the single stage design, as in Case and Morgan (2003). Proportional adjustment of times and sample sizes are made even if the accrual rates are not constant. This adjustment is valid for two-stage 1-group designs. Default is false.

...

Additional graphical parameters passed to function plot.

Details

The plot displays the tradeoff between ETSL, EDA and ES as a function of the combined sample size or study length. Robustness of the optimal design to deviations from the target sample size can be explored. The plots often suggest compromised designs achieving near-optimal results for both EDA and ETSL may be a better choice. Test boundary values (C1L, C1U, etc), and numerical values of other design parameters, can be obtained for a design selected from the plots using function np.OptimDes.

The plot also includes the times of the interim analysis (t1, t1) as a ratio to the time for a corresonding single-stage analysis.

References

Huang B., Talukder E. and Thomas N. Optimal two-stage Phase II designs with long-term endpoints. Statistics in Biopharmaceutical Research, 2(1), 51--61. Case M. D. and Morgan T. M. (2003) Design of Phase II cancer trials evaluating survival probabilities. BMC Medical Research Methodology, 3, 7.

Lin D. Y., Shen L., Ying Z. and Breslow N. E. (1996) Group seqential designs for monitoring survival probabilities. Biometrics, 52, 1033--1042.

Simon R. (1989) Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials, 10, 1--10.

See Also

print.OptimDes, OptimDes, np.OptimDes