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On optimal designs for clinical trials: An updated review
Optimization of clinical trial designs can help investigators achieve higher qualityresults for the given resource constraints. The present paper gives an overviewof optimal designs for various important problems that arise in different stages ofclinical drug development, including phase I dose–toxicity studies; phase I/II studiesthat consider early efficacy and toxicity outcomes simultaneously; phase IIdose–response studies driven by multiple comparisons (MCP), modeling techniques(Mod), or their combination (MCP–Mod); phase III randomized controlled multiarmmulti-objective clinical trials to test difference among several treatment groups;and population pharmacokinetics–pharmacodynamics experiments. We find thatmodern literature is very rich with optimal design methodologies that can be utilizedby clinical researchers to improve efficiency of drug development
Some procedures for computerized ability testing
For computerized test systems to be operational, the use of item response theory is a prerequisite. As opposed to classical test theory, in item response models the abilities of the examinees and the properties of the items are parameterized separately. Hence, when measuring the abilities of examinees, the model implicitly corrects for the item properties, and measurement on an item-independent scale is possible. In addition, item response theory offers the use of test and item information as local reliability indices defined on the ability scale. In this chapter, it is shown how the main features of item response theory have given rise to the development of promising procedures for computerized testing. Among the topics discussed are procedures for item bank calibration, automated test construction, adaptive test administration, generating norm distributions, and diagnosing test scores
Non-adaptive Group Testing on Graphs
Grebinski and Kucherov (1998) and Alon et al. (2004-2005) study the problem
of learning a hidden graph for some especial cases, such as hamiltonian cycle,
cliques, stars, and matchings. This problem is motivated by problems in
chemical reactions, molecular biology and genome sequencing.
In this paper, we present a generalization of this problem. Precisely, we
consider a graph G and a subgraph H of G and we assume that G contains exactly
one defective subgraph isomorphic to H. The goal is to find the defective
subgraph by testing whether an induced subgraph contains an edge of the
defective subgraph, with the minimum number of tests. We present an upper bound
for the number of tests to find the defective subgraph by using the symmetric
and high probability variation of Lov\'asz Local Lemma
Adapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm
From formal and practical analysis, we identify new challenges that
self-adaptive systems pose to the process of quality assurance. When tackling
these, the effort spent on various tasks in the process of software engineering
is naturally re-distributed. We claim that all steps related to testing need to
become self-adaptive to match the capabilities of the self-adaptive
system-under-test. Otherwise, the adaptive system's behavior might elude
traditional variants of quality assurance. We thus propose the paradigm of
scenario coevolution, which describes a pool of test cases and other
constraints on system behavior that evolves in parallel to the (in part
autonomous) development of behavior in the system-under-test. Scenario
coevolution offers a simple structure for the organization of adaptive testing
that allows for both human-controlled and autonomous intervention, supporting
software engineering for adaptive systems on a procedural as well as technical
level.Comment: 17 pages, published at ISOLA 201
Neural Networks for Modeling and Control of Particle Accelerators
We describe some of the challenges of particle accelerator control, highlight
recent advances in neural network techniques, discuss some promising avenues
for incorporating neural networks into particle accelerator control systems,
and describe a neural network-based control system that is being developed for
resonance control of an RF electron gun at the Fermilab Accelerator Science and
Technology (FAST) facility, including initial experimental results from a
benchmark controller.Comment: 21 p
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