Hindawi Publishing Corporation AIDS Research and Treatment Volume 2012, Article ID 478467, 7 pages doi:10.1155/2012/478467 Research Article Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data Allal Houssa¨ıni,1, 2 Lambert Assoumou,1, 2 Anne Genevie` ve Marcelin,1, 2, 3 Jean Michel Molina,4 Vincent Calvez,1, 2, 3 and Philippe Flandre1, 2, 3 1 INSERM, UMR-S 943, 56 Boulevard Vincent Auriol, BP 335, 75625 Paris Cedex 13, France 2 UPMC Univ Paris 06, UMR S943, Paris, France 3 Service de Virologie, Hoˆpital Piti´e-Salpˆetri`ere, AP-HP, Paris, France 4 Service des Maladies Infectieuses, Hoˆpital Saint Louis, AP-HP, Paris, France Correspondence should be addressed to Philippe Flandre, pﬂandre@ccde.chups.jussieu.fr Received 13 May 2011; Revised 8 November 2011; Accepted 14 January 2012 Academic Editor: Christina Ramirez Kitchen Copyright © 2012 Allal Houssa¨ıni 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. 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 R2 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. 1. Introduction The eﬀectiveness of antiretroviral therapy has been limited by the development of human immunodeﬁciency virus type 1 (HIV-1) drug resistance. HIV-1 frequently develops resis- tance to the antiretroviral drugs used to treat it which may decrease both the magnitude and the duration of the response to treatment resulting in loss of viral suppression and therapeutic failure [1]. Moreover, there is a high level of cross-resistance within drug classes; a virus that has develop- ed resistance to one drug in a class may also be resistant to other drugs in the same class [2]. Current International AIDS Society USA and French report HIV-1 guidelines recom- mend resistance testing both before starting antiretroviral therapy (ART) and at treatment failure. Resistance testing has become an important part of choosing and optimizing com- bination therapy for treating HIV-infected individuals [3]. Selecting a “salvage” regimen for an HIV-infected patient who has developed resistance to his or her current regimen is not straightforward [4]. Genotypic or phenotypic assays are used for resistance testing each, assay having advantages and limitations. From those assays we used either the genotypic-phenotypic corre- lation, showing phenotypic eﬀect of mutations, or the geno- typic-virologic correlation, investigating the impact of muta- tions on the virological response to a subsequent treat- ment. The latter correlation is mainly used by the Agence Na- tionale de Recherches sur le SIDA to build rule-based algo- rithms (ANRS http://www.hivfrenchresistance.org/). The