The International Journal of Biostatistics - new articles (fwd)

["Clarice G.B. Demetrio" <clarice@carpa.ciagri.usp.br>]

---------- Forwarded message ----------
Date: Wed, 08 Mar 2006 11:41:06 -0800
From: Nicholas P. Jewell <mm-9547-647681@bepress.com>
To: clarice@carpa.ciagri.usp.br
Subject: The International Journal of Biostatistics - new articles



The Berkeley Electronic Press - together with editors Nicholas P. Jewell,
David Freedman, and Mark van der Laan of University of California, Berkeley;
Raymond Carroll of Texas A&M University; and James Robins of Harvard
University - is pleased to announce the following peer reviewed articles
recently published in the International Journal of Biostatistics (IJB).
To view any of the recently published articles, simply click on the
links below. Full citations and abstracts, as well as additional IJB details,
follow at bottom of message.

Robert L. Strawderman "A Regression Model for Dependent Gap Times".
http://www.bepress.com/ijb/vol2/iss1/1

Mark J. van der Laan "Statistical Inference for Variable Importance".
http://www.bepress.com/ijb/vol2/iss1/2

Pierre-Edouard Sottas, Neil Robinson, Sylvain Giraud, Franco Taroni, Matthias
Kamber, Patrice Mangin, and Martial Saugy "Statistical Classification of
Abnormal Blood Profiles in Athletes".
http://www.bepress.com/ijb/vol2/iss1/3

Daniel Commenges and Virginie Rondeau "Relationship between Derivatives
 of the Observed and Full Loglikelihoods and Application to Newton-Raphson
Algorithm".
http://www.bepress.com/ijb/vol2/iss1/4



__________________________
ABOUT INTERNATIONAL JOURNAL OF BIOSTATISTICS

The International Journal of Biostatistics (IJB) covers the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework, including advances in biostatistical computing. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems. For more details, or to submit your next paper, visit:

http://www.bepress.com/ijb


__________________________
ABSTRACTS & CITATIONS OF NEWLY PUBLISHED ARTICLES


Robert L. Strawderman (2006) "A Regression Model for Dependent Gap Times", The International Journal of Biostatistics: Vol. 2: No. 1, Article 1.
http://www.bepress.com/ijb/vol2/iss1/1

ABSTRACT:
A natural choice of time scale for analyzing recurrent event data is the "gap" (or soujourn) time between successive events. In many situations it is reasonable to assume correlation exists between the successive events experienced by a given subject. This paper looks at the problem of extending the accelerated failure time (AFT) model to the case of dependent recurrent event data via intensity modeling. Specifically, the accelerated gap times model of Strawderman (2005), a semiparametric intensity model for independent gap time data, is extended to the case of multiplicative gamma frailty. As argued in Aalen & Husebye (1991), incorporating frailty captures the heterogeneity between subjects and the "hazard" portion of the intensity model captures gap time variation within a subject. Estimators are motivated using semiparametric efficiency theory and lead to useful generalizations of the rank statistics considered in Strawderman (2005). Several interesting distinctions arise in comparison to the Cox-Andersen-Gill frailty model (e.g., Nielsen et al, 1992; Klein, 1992). The proposed methodology is illustrated by simulation and data analysis.


Mark J. van der Laan (2006) "Statistical Inference for Variable Importance", The International Journal of Biostatistics: Vol. 2: No. 1, Article 2.
http://www.bepress.com/ijb/vol2/iss1/2

ABSTRACT:
Many statistical problems involve the learning of an importance/effect of a variable for predicting an outcome of interest based on observing a sample of $n$ independent and identically distributed observations on a list of input variables and an outcome. For example, though prediction/machine learning is, in principle, concerned with learning the optimal unknown mapping from input variables to an outcome from the data, the typical reported output is a list of importance measures for each input variable. The approach in prediction has been to learn the unknown optimal predictor from the data and derive, for each of the input variables, the variable importance from the obtained fit. In this article we propose a new approach which involves for each variable separately 1) defining variable importance as a real valued parameter, 2) deriving the efficient influence curve and thereby optimal estimating function for this parameter in the assumed (possibly nonparametric) model, and 3) develop a corresponding double robust locally efficient estimator of this variable importance, obtained by substituting for the nuisance parameters in the optimal estimating function data adaptive estimators. We illustrate this methodology in the context of prediction, and obtain in this manner double robust locally optimal estimators of marginal variable importance, accompanied with p-values and confidence intervals. In addition, we present a model based and machine learning approach to estimate covariate-adjusted variable importance. Finally, we generalize this methodology to variable importance parameters for time-dependent variables.


Pierre-Edouard Sottas, Neil Robinson, Sylvain Giraud, Franco Taroni, Matthias Kamber, Patrice Mangin, and Martial Saugy (2006) "Statistical Classification of Abnormal Blood Profiles in Athletes", The International Journal of Biostatistics: Vol. 2: No. 1, Article 3.
http://www.bepress.com/ijb/vol2/iss1/3

ABSTRACT:
Blood doping has been challenging the scientific community since the early 1970's, where it was demonstrated that blood transfusion significantly improves physical performance. Here, we present through 3 applications how statistical classification techniques can assist the implementation of effective tests to deter blood doping in elite sports. In particular, we developed a new indirect and universal test of blood doping, called Abnormal Blood Profile Score (ABPS), based on the statistical classification of indirect biomarkers of altered erythropoiesis. Up to 601 hematological profiles have been compiled in a reference database. Twenty-one of them were obtained from blood samples withdrawn from professional athletes convicted of blood doping by other direct tests. Discriminative training algorithms were used jointly with cross-validation techniques to map these labeled reference profiles to target outputs. The strict cross-validation procedure facilitates the adherence to medico-legal standards mandated by the World Anti Doping Agency (WADA). The test has a sensitivity to recombinant erythropoietin (rhEPO) abuse up to 3 times better than current generative models, independently whether the athlete is currently taking rhEPO or has stopped the treatment. The test is also sensitive to any form of blood transfusion, autologous transfusion included. We finally conclude why a probabilistic approach should be encouraged for the evaluation of evidence in anti-doping area of investigation.


Daniel Commenges and Virginie Rondeau (2006) "Relationship between Derivatives of the Observed and Full Loglikelihoods and Application to Newton-Raphson Algorithm", The International Journal of Biostatistics: Vol. 2: No. 1, Article 4.
http://www.bepress.com/ijb/vol2/iss1/4

ABSTRACT:
In the case of incomplete data we give general relationships between the first and second derivatives of the loglikelihood relative to the full and the incomplete observation set-ups. In the case where these quantities are easy to compute for the full observation set-up we propose to compute their analogue for the incomplete observation set-up using the above mentioned relationships: this involves numerical integrations. Once we are able to compute these quantities, Newton-Raphson type algorithms can be applied to find the maximum likelihood estimators, together with estimates of their variances. We detail the application of this approach to parametric multiplicative frailty models and we show that the method works well in practice using both a real data and a simulated example. The proposed algorithm outperforms a Newton-Raphson type algorithm using numerical derivatives.






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