25,550 research outputs found
Learning Bayesian Networks with the bnlearn R Package
bnlearn is an R package which includes several algorithms for learning the
structure of Bayesian networks with either discrete or continuous variables.
Both constraint-based and score-based algorithms are implemented, and can use
the functionality provided by the snow package to improve their performance via
parallel computing. Several network scores and conditional independence
algorithms are available for both the learning algorithms and independent use.
Advanced plotting options are provided by the Rgraphviz package.Comment: 22 pages, 4 picture
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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
Departures from cost-effectiveness recommendations: The impact of health system constraints on priority setting
The methods and application of cost-effectiveness analysis have reached an advanced stage of development. Many decision makers consider cost-effectiveness analysis to be a valid and feasible approach towards setting health priorities, and it has been extensively applied in evaluating interventions and developing evidence based clinical guidelines. However, the recommendations arising from cost-effectiveness analysis are often not implemented as intended. A fundamental reason for the failure to implement is that CEA assumes a single constraint, in the form of the budget constraint, whilst in reality decision-makers may be faced with numerous other constraints. The objective of this paper is to develop a typology of constraints that may act as barriers to implementation of cost-effectiveness recommendations. Six categories of constraints are considered: the design of the health system; costs of implementing change; system interactions between interventions; uncertainty in estimates of costs and benefits; weak governance; and political constraints. Where possible -and if applicable- for each class of constraint, the paper discusses ways in which these constraints can be taken into account by a decision maker wishing to pursue the principles of cost-effectiveness
Modeling competition between two pharmaceutical drugs using innovation diffusion models
The study of competition among brands in a common category is an interesting
strategic issue for involved firms. Sales monitoring and prediction of
competitors' performance represent relevant tools for management. In the
pharmaceutical market, the diffusion of product knowledge plays a special role,
different from the role it plays in other competing fields. This latent feature
naturally affects the evolution of drugs' performances in terms of the number
of packages sold. In this paper, we propose an innovation diffusion model that
takes the spread of knowledge into account. We are motivated by the need of
modeling competition of two antidiabetic drugs in the Italian market.Comment: Published at http://dx.doi.org/10.1214/15-AOAS868 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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