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AIDS Research and Treatment
Volume 2012 (2012), Article ID 478467, 7 pages
http://dx.doi.org/10.1155/2012/478467
Research Article

Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data

1INSERM, UMR-S 943, 56 Boulevard Vincent Auriol, BP 335, 75625 Paris Cedex 13, France
2UPMC Univ Paris 06, UMR S943, Paris, France
3Service de Virologie, Hôpital Pitié-Salpêtrière, AP-HP, Paris, France
4Service des Maladies Infectieuses, Hôpital Saint Louis, AP-HP, Paris, France

Received 13 May 2011; Revised 8 November 2011; Accepted 14 January 2012

Academic Editor: Christina Ramirez Kitchen

Copyright © 2012 Allal Houssaïni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. Many statistical models have been tested to predict phenotypic or virological response from genotypic data. A statistical framework called Super Learner has been introduced either to compare different methods/learners (discrete Super Learner) or to combine them in a Super Learner prediction method. Methods. The Jaguar trial is used to apply the Super Learner framework. The Jaguar study is an “add-on” trial comparing the efficacy of adding didanosine to an on-going failing regimen. Our aim was also to investigate the impact on the use of different cross-validation strategies and different loss functions. Four different repartitions between training set and validations set were tested through two loss functions. Six statistical methods were compared. We assess performance by evaluating values and accuracy by calculating the rates of patients being correctly classified. Results. Our results indicated that the more recent Super Learner methodology of building a new predictor based on a weighted combination of different methods/learners provided good performance. A simple linear model provided similar results to those of this new predictor. Slight discrepancy arises between the two loss functions investigated, and slight difference arises also between results based on cross-validated risks and results from full dataset. The Super Learner methodology and linear model provided around 80% of patients correctly classified. The difference between the lower and higher rates is around 10 percent. The number of mutations retained in different learners also varys from one to 41. Conclusions. The more recent Super Learner methodology combining the prediction of many learners provided good performance on our small dataset.