37,780 research outputs found
Bayesian model selection in logistic regression for the detection of adverse drug reactions
Motivation: Spontaneous adverse event reports have a high potential for
detecting adverse drug reactions. However, due to their dimension, exploring
such databases requires statistical methods. In this context,
disproportionality measures are used. However, by projecting the data onto
contingency tables, these methods become sensitive to the problem of
co-prescriptions and masking effects. Recently, logistic regressions have been
used with a Lasso type penalty to perform the detection of associations between
drugs and adverse events. However, the choice of the penalty value is open to
criticism while it strongly influences the results. Results: In this paper, we
propose to use a logistic regression whose sparsity is viewed as a model
selection challenge. Since the model space is huge, a Metropolis-Hastings
algorithm carries out the model selection by maximizing the BIC criterion.
Thus, we avoid the calibration of penalty or threshold. During our application
on the French pharmacovigilance database, the proposed method is compared to
well established approaches on a reference data set, and obtains better rates
of positive and negative controls. However, many signals are not detected by
the proposed method. So, we conclude that this method should be used in
parallel to existing measures in pharmacovigilance.Comment: 7 pages, 3 figures, submitted to Biometrical Journa
Asymptotic inference for semiparametric association models
Association models for a pair of random elements and (e.g., vectors)
are considered which specify the odds ratio function up to an unknown parameter
\bolds\theta. These models are shown to be semiparametric in the sense that
they do not restrict the marginal distributions of and . Inference for
the odds ratio parameter \bolds\theta may be obtained from sampling either
conditionally on or vice versa. Generalizing results from Prentice and
Pyke, Weinberg and Wacholder and Scott and Wild, we show that asymptotic
inference for \bolds\theta under sampling conditional on is the same as
if sampling had been conditional on . Common regression models, for example,
generalized linear models with canonical link or multivariate linear,
respectively, logistic models, are association models where the regression
parameter \bolds\beta is closely related to the odds ratio parameter
\bolds\theta. Hence inference for \bolds\beta may be drawn from samples
conditional on using an association model.Comment: Published in at http://dx.doi.org/10.1214/07-AOS572 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
On the Bayes-optimality of F-measure maximizers
The F-measure, which has originally been introduced in information retrieval,
is nowadays routinely used as a performance metric for problems such as binary
classification, multi-label classification, and structured output prediction.
Optimizing this measure is a statistically and computationally challenging
problem, since no closed-form solution exists. Adopting a decision-theoretic
perspective, this article provides a formal and experimental analysis of
different approaches for maximizing the F-measure. We start with a Bayes-risk
analysis of related loss functions, such as Hamming loss and subset zero-one
loss, showing that optimizing such losses as a surrogate of the F-measure leads
to a high worst-case regret. Subsequently, we perform a similar type of
analysis for F-measure maximizing algorithms, showing that such algorithms are
approximate, while relying on additional assumptions regarding the statistical
distribution of the binary response variables. Furthermore, we present a new
algorithm which is not only computationally efficient but also Bayes-optimal,
regardless of the underlying distribution. To this end, the algorithm requires
only a quadratic (with respect to the number of binary responses) number of
parameters of the joint distribution. We illustrate the practical performance
of all analyzed methods by means of experiments with multi-label classification
problems
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