13,880 research outputs found
Subgroup identification in dose-finding trials via model-based recursive partitioning
An important task in early phase drug development is to identify patients,
which respond better or worse to an experimental treatment. While a variety of
different subgroup identification methods have been developed for the situation
of trials that study an experimental treatment and control, much less work has
been done in the situation when patients are randomized to different dose
groups. In this article we propose new strategies to perform subgroup analyses
in dose-finding trials and discuss the challenges, which arise in this new
setting. We consider model-based recursive partitioning, which has recently
been applied to subgroup identification in two arm trials, as a promising
method to tackle these challenges and assess its viability using a real trial
example and simulations. Our results show that model-based recursive
partitioning can be used to identify subgroups of patients with different
dose-response curves and improves estimation of treatment effects and minimum
effective doses, when heterogeneity among patients is present.Comment: 23 pages, 6 figure
On the consistency of Fr\'echet means in deformable models for curve and image analysis
A new class of statistical deformable models is introduced to study
high-dimensional curves or images. In addition to the standard measurement
error term, these deformable models include an extra error term modeling the
individual variations in intensity around a mean pattern. It is shown that an
appropriate tool for statistical inference in such models is the notion of
sample Fr\'echet means, which leads to estimators of the deformation parameters
and the mean pattern. The main contribution of this paper is to study how the
behavior of these estimators depends on the number n of design points and the
number J of observed curves (or images). Numerical experiments are given to
illustrate the finite sample performances of the procedure
Clustering Via Nonparametric Density Estimation: the R Package pdfCluster
The R package pdfCluster performs cluster analysis based on a nonparametric
estimate of the density of the observed variables. After summarizing the main
aspects of the methodology, we describe the features and the usage of the
package, and finally illustrate its working with the aid of two datasets
Good, great, or lucky? Screening for firms with sustained superior performance using heavy-tailed priors
This paper examines historical patterns of ROA (return on assets) for a
cohort of 53,038 publicly traded firms across 93 countries, measured over the
past 45 years. Our goal is to screen for firms whose ROA trajectories suggest
that they have systematically outperformed their peer groups over time. Such a
project faces at least three statistical difficulties: adjustment for relevant
covariates, massive multiplicity, and longitudinal dependence. We conclude
that, once these difficulties are taken into account, demonstrably superior
performance appears to be quite rare. We compare our findings with other recent
management studies on the same subject, and with the popular literature on
corporate success. Our methodological contribution is to propose a new class of
priors for use in large-scale simultaneous testing. These priors are based on
the hypergeometric inverted-beta family, and have two main attractive features:
heavy tails and computational tractability. The family is a four-parameter
generalization of the normal/inverted-beta prior, and is the natural conjugate
prior for shrinkage coefficients in a hierarchical normal model. Our results
emphasize the usefulness of these heavy-tailed priors in large multiple-testing
problems, as they have a mild rate of tail decay in the marginal likelihood
---a property long recognized to be important in testing.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS512 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The Third Gravitational Lensing Accuracy Testing (GREAT3) Challenge Handbook
The GRavitational lEnsing Accuracy Testing 3 (GREAT3) challenge is the third
in a series of image analysis challenges, with a goal of testing and
facilitating the development of methods for analyzing astronomical images that
will be used to measure weak gravitational lensing. This measurement requires
extremely precise estimation of very small galaxy shape distortions, in the
presence of far larger intrinsic galaxy shapes and distortions due to the
blurring kernel caused by the atmosphere, telescope optics, and instrumental
effects. The GREAT3 challenge is posed to the astronomy, machine learning, and
statistics communities, and includes tests of three specific effects that are
of immediate relevance to upcoming weak lensing surveys, two of which have
never been tested in a community challenge before. These effects include
realistically complex galaxy models based on high-resolution imaging from
space; spatially varying, physically-motivated blurring kernel; and combination
of multiple different exposures. To facilitate entry by people new to the
field, and for use as a diagnostic tool, the simulation software for the
challenge is publicly available, though the exact parameters used for the
challenge are blinded. Sample scripts to analyze the challenge data using
existing methods will also be provided. See http://great3challenge.info and
http://great3.projects.phys.ucl.ac.uk/leaderboard/ for more information.Comment: 30 pages, 13 figures, submitted for publication, with minor edits
(v2) to address comments from the anonymous referee. Simulated data are
available for download and participants can find more information at
http://great3.projects.phys.ucl.ac.uk/leaderboard
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