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Principal component models for sparse functional data: Biometrika, Vol. 87, No. 3. (1 September 2000), pp. 587-602.The elements of a multivariate dataset are often curves rather than single points. Functional principal components can be used to describe the modes of variation of such curves. If one has complete measurements for each individual curve or, as is more common, one has measurements on a fine grid taken at the same time points for all curves, then many standard techniques may be applied. However, curves are often measured at an irregular and sparse set of time points which can differ widely across individuals. We present a technique for handling this more difficult case using a reduced rank mixed effects framework. 10.1093/biomet /87.3.587GM James, TJ Hastie, CA Sugar

Source: Biometrika, Vol. 87, No. 3. (1 September 2000), pp. 587-602.
Canonical Correlation Analysis when the Data are Curves: It is not immediately straightforwar d to extend canonical correlation analysis to the context of functional data analysis, where the data are themselves curves or functions. The obvious approach breaks down, and it is necessary to use a method involving smoothing in some way. Such a method is introduced and discussed with reference to a data set on human gait. The breakdown of the unsmoothed method is illustrated in a practical context and is demonstrated theoretically. A consistency theorem for the smoothed method is proved.SE Leurgans, RA Moyeed, BW Silverman
Smoothed Functional Principal Components Analysis by Choice of Norm: The Annals of Statistics, Vol. 24, No. 1. (1996), pp. 1-24.Bernard Silverman

Source: The Annals of Statistics, Vol. 24, No. 1. (1996), pp. 1-24.
Paving the critical path: how can clinical pharmacology help achieve the vision?: Clinical pharmacology and therapeutics, Vol. 81, No. 2. (February 2007), pp. 170-177.It has been almost 3 years since the launch of the FDA critical path initiative following the publication of the paper "Innovation or Stagnation: Challenges and Opportunities on the Critical Path of New Medical Product Development." The initiative was intended to create an urgency with the drug development enterprise to address the so-called "productivity problem" in modern drug development. Clinical pharmacologist s are strategically aligned with solutions designed to reduce late phase clinical trial failures to show adequate efficacy and/or safety. This article reviews some of the ways that clinical pharmacologist s can lead and implement change in the drug development process. It includes a discussion of model-based, semi-mechanist ic drug development, drug/disease models that facilitate informed clinical trial designs and optimal dosing, the qualification process and criteria for new biomarkers and surrogate endpoints, approaches to streamlining clinical trials and new types of interaction between industry and FDA such as the end-of-phase 2A and voluntary genomic data submission meetings respectively.L J Lesko

Source: Clinical pharmacology and therapeutics, Vol. 81, No. 2. (February 2007), pp. 170-177.
FDA - a scalable evolutionary algorithm for the optimization of additively decomposed functions.: Evol Comput, Vol. 7, No. 4. (1999), pp. 353-376.The Factorized Distribution Algorithm (FDA) is an evolutionary algorithm which combines mutation and recombination by using a distribution. The distribution is estimated from a set of selected points. In general, a discrete distribution defined for n binary variables has 2(n) parameters. Therefore it is too expensive to compute. For additively decomposed discrete functions (ADFs) there exist algorithms which factor the distribution into conditional and marginal distributions. This factorization is used by FDA. The scaling of FDA is investigated theoretically and numerically. The scaling depends on the ADF structure and the specific assignment of function values. Difficult functions on a chain or a tree structure are solved in about O(n radical n) operations. More standard genetic algorithms are not able to optimize these functions. FDA is not restricted to exact factorizations . It also works for approximate factorizations as is shown for a circle and a grid structure. By using results from Bayes networks, FDA is extended to LFDA. LFDA computes an approximate factorization using only the data, not the ADF structure. The scaling of LFDA is compared to the scaling of FDA.H Muehlenbein, T Mahnig

Source: Evol Comput, Vol. 7, No. 4. (1999), pp. 353-376.
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence): (2006)Martin Pelikan, Kumara Sastry, Erick Cant&\#250 ;-Paz

Source: (2006)
EFNS guidelines on neuropathic pain assessment: European Journal of Neurology, Vol. 11, No. 3. (2004), pp. 153-162.In September 2001, a Task Force was set up under the auspices of the European Federation of Neurological Societies with the aim of evaluating the existing evidence about the methods of assessing neuropathic pain and its treatments. This review led to the development of guidelines to be used in the management of patients with neuropathic pain. In the clinical setting a neurological examination that includes an accurate sensory examination is often sufficient to reach a diagnosis. Nerve conduction studies and somatosensory- evoked potentials, which do not assess small fibre function, may demonstrate and localize a peripheral or central nervous lesion. A quantitative assessment of the nociceptive pathways is provided by quantitative sensory testing and laser-evoked potentials. To evaluate treatment efficacy in a patient and in controlled trials, the simplest psychometric scales and quality of life measures are probably the best methods. A laboratory measure of pain that by-passes the subjective report, and thus cognitive influences, is a hopeful aim for the future.G Cruccu, P Anand, N Attal, Garcia Larrea, M Haanpaa, E Jorum, J Serra, TS Jensen

Source: European Journal of Neurology, Vol. 11, No. 3. (2004), pp. 153-162.
Initial Severity and Antidepressant Benefits: A Meta-Analysis of Data Submitted to the Food and Drug Administration: PLoS Medicine, Vol. 5, No. 2. (1 February 2008), e45.Background Meta-analyses of antidepressant medications have reported only modest benefits over placebo treatment, and when unpublished trial data are included, the benefit falls below accepted criteria for clinical significance. Yet, the efficacy of the antidepressant s may also depend on the severity of initial depression scores. The purpose of this analysis is to establish the relation of baseline severity and antidepressant efficacy using a relevant dataset of published and unpublished clinical trials.Methods and FindingsWe obtained data on all clinical trials submitted to the US Food and Drug Administration (FDA) for the licensing of the four new-generation antidepressant s for which full datasets were available. We then used meta-analytic techniques to assess linear and quadratic effects of initial severity on improvement scores for drug and placebo groups and on drug?placebo difference scores. Drug?placebo differences increased as a function of initial severity, rising from virtually no difference at moderate levels of initial depression to a relatively small difference for patients with very severe depression, reaching conventional criteria for clinical significance only for patients at the upper end of the very severely depressed category. Meta-regressio n analyses indicated that the relation of baseline severity and improvement was curvilinear in drug groups and showed a strong, negative linear component in placebo groups.Conclus ionsDrug?place bo differences in antidepressant efficacy increase as a function of baseline severity, but are relatively small even for severely depressed patients. The relationship between initial severity and antidepressant efficacy is attributable to decreased responsiveness to placebo among very severely depressed patients, rather than to increased responsiveness to medication.Irv ing Kirsch, Brett Deacon, Tania Huedo-Medina, Alan Scoboria, Thomas Moore, Blair Johnson

Source: PLoS Medicine, Vol. 5, No. 2. (1 February 2008), e45.
FDA Drops Helsinki Rules: Science, Vol. 320, No. 5877. (9 May 2008), 731b.10.1126/s cience.320.587 7.731bJennifer Couzin

Source: Science, Vol. 320, No. 5877. (9 May 2008), 731b.
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction: Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 29, No. 1. (2007), pp. 40-51.Over the past few decades, a large family of algorithmsÂżsu pervised or unsupervised; stemming from statistics or geometry theoryÂżhas been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/ tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called Marginal Fisher Analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional Linear Discriminant Analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions.Shu icheng Yan, Dong Xu, Benyu Zhang, Hong-Jiang Zhang, Qiang Yang, S Lin

Source: Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 29, No. 1. (2007), pp. 40-51.

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