ClinSimon

Overview

ClinSimon provides methods to address enrollment challenges in Simon's Two-Stage Design for phase II clinical trials. The package offers tools to:

Adaptive threshold adjustment: Manage under-enrollment scenarios.
Sample size recalibration: Handle over-enrollment cases.
Post-inference analysis: Perform power calculations, confidence interval estimation, and hypothesis testing.

These methods help maintain statistical rigor while accommodating real-world enrollment variations in clinical trials.

Installation Instructions

You can install the ClinSimon package directly from GitHub using the following R code:

# Uncomment and run the following line if you don't already have devtools installed:
# install.packages("devtools")

# Install ClinSimon from GitHub:
devtools::install_github("YunheLiuMCMC/ClinSimon")

If you want to install the package with a vignette, you should install the clinfun package first.

# Install the package clinfun first:
install.packages("clinfun")

Then you can install it with the vignette built:

# Install ClinSimon with vignette support:
devtools::install_github("YunheLiuMCMC/ClinSimon", build_vignettes = TRUE)

Example

# Load the ClinSimon package
library(ClinSimon)

# Generate a Simon two stage design from package Clinfun
library(Clinfun)
# Specify the parameters and constraints
trial = ph2simon(0.25, 0.45, 0.1, 0.1)
# Print
trial

# Simon 2-stage Phase II design 

# Unacceptable response rate:  0.25 
# Desirable response rate:  0.45 
# Error rates: alpha =  0.1 ; beta =  0.1 

#            r1 n1  r  n EN(p0) PET(p0)   qLo   qHi
# Minimax     5 23 13 39  31.50  0.4685 0.752 1.000
# Admissible  3 15 13 40  28.47  0.4613 0.026 0.752
# Optimal     3 14 14 44  28.36  0.5213 0.000 0.026

Right now, one clinician decided to use Optimal deisgn to recruit patients. Suppose we currently have outcome data for only 11 patients in the $1^{st}$ stage and assume the $2^{nd}$ stage sample size for evaluable patients remains as planned, We can use ATS_Design() to provide updated thresholds for the interim analysis, without needing to wait for the number of evaluable patients to reach 14. This means the input n1_star is 11 and n_star is 41 (=11+30 instead of the original 14+30 as the total sample size), as the original optimal design specifies a sample size of 30 for the second stage.

ATS_Design(n1=14,n=44,n1_star=11,n_star=41,r1=3,r=14,p0=0.25,p1=0.45,alpha=0.1)

#                                 r1* r* n1* n* alpha(n*) Type I Power EN(p0) PET(p0)
# Adaptive Threshold Simon Design   2 14  11 41     0.088   0.06 0.854 27.344   0.455

The updated design parameters by ATS Simon design method are (2, 14). We also output actual sample sizes of stage 1 and the total sample size (11, 41). The type I error rate of this updated design is 0.06, which is below the original type I error constraint of 0.1. As we know, if alpha decreases, power will also decrease. Therefore, the current power of 85.4% is lower than the original design's 90% as expected, but still close to the original power. Additionally, the probability of early termination is 0.455, which is close to the original design's 0.521.

Vignette

For a detailed overview and examples, check out the ClinSimon vignette.

License

This package is free and open source software, licensed under GPL (>=3). See the LICENSE file for more details.