Shourt Courses
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Gatekeeping procedures in clinical trials
Alex Dmitrienko (Eli Lilly and Company)
Ajit Tamhane (Northwestern University)
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 five 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
course.
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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. 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 cinical 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.
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Multiple Comparisons in Complex Clinical Trial Designs
H.M. James Hung
Sue-Jane Wang (FDA)
In the last decade, the methodology for clinical development became
increasingly complex. For example, active control designs are
increasingly used. In a clinical program for assessing cardiovascular
risks, multiple trials may be combined to assess a mortality endpoint
while each trial is planned to assess a different endpoint. In many of
such cases, particularly under regulatory application settings, the
statistical framework of inference and the conventional notion of
experimentwise type I error are often unclear. This short course will be
devoted to emerging multiplicity issues in a number of complex design
settings including flexible design strategies and suggest a number of
approaches. Case examples will be presented to facilitate discussion.
The topics to be covered include:
- 1. Family of combinable clinical trials
- Multiple doses
- Multiple endpoints
- Composite endpoint
- 2. Active-controlled trials
- With or without a placebo arm
- Non-inferiority and superiority analyses
- 3. Adaptive or flexible design trials
- Early phase trials
- Pivotal trials
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Simultaneous confidence bands in regression
Wei Liu, University of Southampton
Using simultaneous confidence bands to bound an unknown function or the
differences between unknown functions is a direct generalization of
using confidence intervals to bound an unknown parameter or the
differences between unknown parameters. Simultaneous confidence bands
are intuitive, informative and can make useful statistical inferences
(such as one-sided comparison of two models) that are beyond the
standard statistical methods. This course will focus on the construction
and applications of simultaneous confidence bands for various
inferential purposes in parametric regression analysis, including linear
and generalized linear regression models. It aims to provide an overview
of the methods available, and demonstrates with examples the
implementation of the methods using computer software MATLAB.
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Adaptive Designs for Clinical Trials
Martin Posch (European Medicines Agency, London), Franz Koenig (Medical University of Vienna)
Adaptive 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 or the number of selected
treatments is 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 methods from the published literature
including the most recent developments. Special emphasis is put on
sample size adjustment and multiple hypotheses testing with adaptive
designs. Furthermore, regulatory issues will be discussed.
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Concepts and Techniques of Multiple Testing in Clinical and Biomarker Studies
Jason C. Hsu, The Ohio State University
This course covers fundamental concepts and techniques of multiple testing.
Error rates:
- FWER (Familywise Error Rate)
- gFWER (generalized Familywise Error Rate)
- FDR, Fdr (False Discovery Rate)
How incorrect decision-making rate is impacted by issues of true, average,
or worst case scenario control, number or proportion of incorrect rejections
control, conditional or unconditional error rate, tail probability or
expectation error rate, will be discussed.
Techniques:
- Closed Testing
- Partition Testing
Holm's and Hochberg's tests, both simple examples of partition testing,
ignore joint distribution in computing critical values. Using testing
for efficacy in genomic subgroup as an example, it will be shown that
when joint distribution is taken into account, step-down methods are
often more powerful than Hochberg's step-up method.
Applications:
- Multiple endpoints
- Bioinformatics
Partition Testing and Closed (Gatekeeping) Testing will be compared in
the setting of Multiple Endpoints, in terms of simplicity/complexity and power.
In the setting of association studies between biomarkers and drug response,
we will discuss assumption needed on the joint distributions of gene
expression levels or SNP alleles for permutation tests to be valid.
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