Clinical Trial Methodology and Software
Introduction
The following table contains information on locally developed methodologies and software. The software is almost always an R package and will soon appear in a CRAN repository near you; the links below will, in due course, point to them. Initially however, we provide source packages and binaries for Mac and Windows platforms so that users may download and experiment. Installation instructions are also given.
List of Software
| Topic | Description | Software |
|---|---|---|
| A New Approach to Designing Phase I-II Cancer Trials for Cytotoxic Chemotherapies. Bartroff, Lai and Narasimhan, (submitted) |
Recently there has been much work on early phase cancer designs that incorporate both toxicity and efficacy data, called Phase
I-II designs because they combine elements of both. However, they do not explicitly address the Phase II hypothesis test of
H0: p = p0, where p is the probability of efficacy at the estimated maximum tolerated dose
(MTD) 
from Phase I and p0 is the baseline efficacy rate. Standard practice for Phase II remains to treat p as a
fixed, unknown parameter and to use Simon’s 2-stage design with all patients dosed at the estimate
 .
We propose a Phase I- II design that addresses the uncertainty in the estimate
p = p ( )
in H0 by using sequential generalized likelihood theory. Combining this with a Phase I design that
incorporates efficacy data, the Phase I-II design provides a common framework that can be used all the way from
the first dose of Phase I through the final accept/reject decision about H0 at the end of Phase II,
utilizing both toxicity and efficacy data throughout. Efficient group sequential testing is used in Phase II that allows
for early stopping to show treatment effect or futility. The proposed Phase I-II design thus removes the artificial barrier
between Phase I and Phase II, and fulfills the objectives of searching for the MTD and testing if the treatment has an acceptable
response rate to enter into a Phase III trial. |
An R package for computing the operating characteristics of the Phase I-II design called
sp12design is available. Further improvements are under development and a final version should soon appear on a
CRAN site near you. We have versions for
Unix,
Macintosh and
Windows. First, one must install the required isotone package the usual way using menus or whatever works for you before installing sp12design. On Unix, open up a terminal and type R CMD INSTALL sp12design_0.03.tar.gz On a Mac, open up a terminal and type R CMD INSTALL sp12design_0.03.tgz On Windows, using the Package menu, choose Install from local zip files and navigate to the downloaded zip file. Once the package is installed, typing library("sp12design"); example(iso.phaseII.oc) will run an example that computes the operating characteristics of the new design under monotonicity conditions discussed in the paper. To reproduce table 3 of the paper with some accuracy, a simulation size B =10000 should be used. |
| Clinical trial designs for testing biomarker-based personalized therapies. Tze Leung Lai, Philip W Lavori, Mei-Chiung I Shih, Branimir I Sikic. Published in Clinical trials, vol. 9, no. 2, 141-154, 2012. | Advances in molecular therapeutics in the past decade have opened up new possibilities for treating cancer patients with personalized therapies, using biomarkers to determine which treatments are most likely to benefit them, but there are difficulties and unresolved issues in the development and validation of biomarker-based personalized therapies. We develop a new clinical trial design to address some of these issues. The goal is to capture the strengths of the frequentist and Bayesian approaches to address this problem in the recent literature and to circumvent their limitations. We use generalized likelihood ratio tests of the intersection null and enriched strategy null hypotheses to derive a novel clinical trial design for the problem of advancing promising biomarker-guided strategies toward eventual validation. We also investigate the usefulness of adaptive randomization (AR) and futility stopping proposed in the recent literature. |
An R package for running the simulations in the paper is available. There is additional work that needs to be done to
make this a more general package than it is currently. We have versions for
Unix, Macintosh and Windows. On Unix, open up a terminal and type R CMD INSTALL bgct_1.01.tar.gz On a Mac, open up a terminal and type R CMD INSTALL bgct_1.01.tgz On Windows, using the Package menu, choose Install from local zip files and navigate to the downloaded zip file. Once installed, use library(bgct) example(runOvarianTrial) to run 10 simulations of the scenarios in the paper. |
| Sequential Design of Phase II-III Cancer Trials. (published in Statistics in Medicine, Volume 31, issue 18, p.1944-1960, 2012.) |
Whereas traditional phase II cancer trials are usually
single-arm, with tumor response as end-point, and phase III
trials are randomized and incorporate interim analyses with
progression- free survival or other failure time as endpoint, this
paper proposes a new approach that seamlessly expands a randomized
phase II study of response rate into a randomized phase III study
of time to failure. This approach is based on advances in group
sequential designs and joint modeling of the response rate and
time to event. The joint modeling is reflected in the primary and
secondary objectives of the trial, and the sequential design
allows the trial to adapt to increase in information on response
and survival patterns during the course of the trial, and to stop
early either for conclusive evidence on efficacy of the
experimental treatment or for the futility in continuing the trial
to demonstrate it, based on the data collected so far. Research and Software reported here was supported in part by the U.S. National Science Foundation under grant number DMS-0805879 and in part by Stanford NIH/NCRR CTSA award number UL1 RR025744. |
Section 4.3 of the paper describes the implementation of the
design and shows tables of the design characteristics against
specific alternatives. The package sp23design below is
a complete implementation that is meant to be used both in
designing the trial and in executing it in the field. Install the package as usual on R from your favorite CRAN repository by selecting the package sp23design. Or in an R session, type install.packages("sp23design") Once the package is installed, typing example(generateSP23Design) will run an example that simulates case C of Table 1 in the paper. Additional examples are located in the examples sub-folder of the package directory that replicate all the scenarios in tables 1 and 2. Answers should be in the ballpark. |
| Sequential Generalized Likelihood Ratio Tests for Vaccine Safety Evaluation (published in Statistics in Medicine, Volume 29, issue 26, p.2698-2708, 2010.) |
The evaluation of vaccine safety involves pre-clinical animal
studies, pre-licensure randomized clinical trials and
post-licensure safety studies. Sequential design and analysis
are of particular interest because they allow early termination
of the trial or quick detection that the vaccine exceeds a
prescribed bound on the adverse event rate. After a review of
recent developments in this area, we propose a new class of
sequential generalized likelihood ratio tests for evaluating
adverse event rates in two-armed pre-licensure clinical trials
and single-armed post-licensure studies. The proposed approach
is illustrated using data from the Rotavirus Efficacy and
Safety Trial (REST). Simulation studies of the performance of
the proposed approach and other methods are also given. Research and Software reported here was supported in part by the U.S. National Science Foundation under grant number DMS-0805879 and in part by Stanford NIH/NCRR CTSA award number UL1 RR025744. |
Section 4.2 of the paper describes the computation of the GLR
boundaries for designing pre-licensure vaccine trials. The
boundary depends on four fundamental parameters of the
model. The package includes a Shiny webapp that can be run with
library(sglr)
runApp("webapp", package="sglr")
Install the package as usual on R from your favorite CRAN repository by selecting the package sglr. Or in an R session, typeinstall.packages("sglr") Once the package is installed, typing example(glrSearch) will run a search example. You can plot the boundary for the REST trial with plotBoundary(b0=2.8, b1=2.4, p=c(0.5, 10/11), textXOffset=1, textYSkip=1) |
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