Fundamentals of Multiple Testing and Biotechnology with Applications
to Clinical Trials and Personalized Medicine
Jason C. Hsu (The Ohio State University) and
Xinping Cui (University of California Riverside)
Clinical trials have become more complex. And use of biomarkers to
target a patient subgroup (personalized medicine) is becoming increasingly
common. This course uses Partition Testing, a principle of multiple test
construction that can control a variety of error rates, to show applicable
statistical concepts remain fundamentally the same in various clinical
trials. Multiplicity issues raised in biomarker studies for personalized
medicine are also discussed.
- To demonstrate multiple test construction, we show that, when there are
multiple endpoints in combination with multiple doses, partition
testing can directly formulate null hypotheses to respects decision
paths, testing exponentially fewer hypotheses than closed testing
- Before discussing multiple testing in personalized medicine, some basics
of molecular biology and next generation sequencing technology
will be introduced.
- To demonstrate sound multiple testing principle for biomarker studies,
we first construct a rigorous test for a predictive biomarker,
testing whether a SNP mutation has a dominant, recessive, or
additive effect on clinical response (while controlling familywise
error rate FWER).
- Then, to discuss multiplicity issues in GWAS (genome-wide association studies)
for discovery of potential biomarkers, we consider how to adjust for
multiplicity across different SNPs and genes. Specifically, we
will examine critically the interpretation and implication of
controlling per family error rate, false discovery rate (FDR), and
local false discovery rate (Fdr).
Martin Posch, Franz König (Medical University of Vienna)
The goal of this course is to give an introduction to the key principles
and statistical methodologies of adaptive designs for clinical trials.
Adaptive (flexible) designs allow for mid-course design adaptations based
on interim data without compromising the overall type I error rate.
Examples of design adaptations are the adjustment of sample sizes or the
number and timing of interim analyses. These design parameters may be
adapted depending on interim estimates of the variance, the treatment effect
and safety parameters.
An important field of application of the adaptive design methodology are
clinical trials with several treatment arms, where promising treatments
can be selected at an interim analysis. Using adaptive multiple test
procedures the type I error rate can be controlled even if the selection
rule, the number of selected treatments or the final sample sizes are not
prefixed. Adaptive multiple testing procedures can also be used in adaptive
designs with the option of population enrichment. In such designs a sub
population may be selected in an interim analysis and further recruitment
of patients is restricted to the selected subgroup.
The course provides an overview of the current state of the art in design i
and analysis of adaptive clinical trials including the most recent developments.
Special emphasis is put on un-blinded sample size adjustment and multiple
hypotheses testing with adaptive designs. Furthermore, regulatory issues will be discussed.
Graphical approaches to multiple test problems
Frank Bretz, Ekkehard Glimm, Willi Maurer (Novartis)
Methods for addressing multiplicity are becoming increasingly more
important in clinical trials and other applications. In the recent
past several multiple test procedures have been developed that allow one
to map the relative importance of different study objectives as well as
their relation onto an appropriately tailored multiple test procedure, such
as fixed-sequence, fallback, and gatekeeping procedures. In this course we
focus on graphical approaches that can be applied to common multiple test
problems, such as comparing several treatments with a control, assessing the
benefit of a new drug for more than one endpoint, and combined non-inferiority
and superiority testing. Using graphical approaches, one can easily construct
and explore different test strategies and thus tailor the test procedure to
the given study objectives. The resulting multiple test procedures are
represented by directed, weighted graphs, where each node corresponds to
an elementary hypothesis, together with a simple algorithm to generate
such graphs while sequentially testing the individual hypotheses. The
class of procedures covered in this course include weighted Bonferroni
tests, weighted parametric tests accounting for the correlation between
the test statistics, and weighted Simes' tests. The approach is illustrated
with the visualization of several common gatekeeping strategies. We also
present several case studies to illustrate how the approach can be used
in clinical practice. In addition, we briefly consider power and sample
size calculation to optimize a multiple test procedure for given study
objectives. The presented methods will be illustrated using the graphical
user interface from the gMCP package in R, which is freely available on CRAN.
Gatekeeping procedures in clinical trials
Alex Dmitrienko (Quintiles)
This half-day course will focus on issues arising in clinical trials
with ordered multiple objectives (e.g., primary and secondary endpoints,
primary and secondary patient populations, etc.) using gatekeeping
procedures. The course will provide a detailed overview of novel
statistical approaches developed over the past ten years. There will be
a well-balanced coverage of theory and applications, regulatory
considerations and software implementation of gatekeeping procedures in
SAS and R. Examples from clinical trials will be used throughout the
discussion to illustrate the statistical approaches discussed in the
Text book: Dmitrienko, A., Tamhane, A.C., Bretz, F. (editors). (2009).
Multiple Testing Problems in Pharmaceutical Statistics.
Chapman and Hall/CRC Press, New York.
Web site: Supplementary material, including the bibliography, software
code and useful links, is available on the Multxpert web site:
Invited Speakers and
MCP-2011 - Washington
MCP-2009 - Japan
MCP-2007 - Vienna
MCP-2005 - Shanghai
MCP-2002 - Bethesda
MCP-2000 - Berlin
MCP-1996 - Tel Aviv