Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data
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 predict...
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doaj-a7f229e5f65844afbec403b107a2c2372020-11-24T22:15:27ZengHindawi LimitedAIDS Research and Treatment2090-12402090-12592012-01-01201210.1155/2012/478467478467Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial DataAllal Houssaïni0Lambert Assoumou1Anne Geneviève Marcelin2Jean Michel Molina3Vincent Calvez4Philippe Flandre5INSERM, UMR-S 943, 56 Boulevard Vincent Auriol, BP 335, 75625 Paris Cedex 13, FranceINSERM, UMR-S 943, 56 Boulevard Vincent Auriol, BP 335, 75625 Paris Cedex 13, FranceINSERM, UMR-S 943, 56 Boulevard Vincent Auriol, BP 335, 75625 Paris Cedex 13, FranceService des Maladies Infectieuses, Hôpital Saint Louis, AP-HP, Paris, FranceINSERM, UMR-S 943, 56 Boulevard Vincent Auriol, BP 335, 75625 Paris Cedex 13, FranceINSERM, UMR-S 943, 56 Boulevard Vincent Auriol, BP 335, 75625 Paris Cedex 13, FranceBackground. 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.http://dx.doi.org/10.1155/2012/478467 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Allal Houssaïni Lambert Assoumou Anne Geneviève Marcelin Jean Michel Molina Vincent Calvez Philippe Flandre |
spellingShingle |
Allal Houssaïni Lambert Assoumou Anne Geneviève Marcelin Jean Michel Molina Vincent Calvez Philippe Flandre Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data AIDS Research and Treatment |
author_facet |
Allal Houssaïni Lambert Assoumou Anne Geneviève Marcelin Jean Michel Molina Vincent Calvez Philippe Flandre |
author_sort |
Allal Houssaïni |
title |
Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data |
title_short |
Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data |
title_full |
Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data |
title_fullStr |
Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data |
title_full_unstemmed |
Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data |
title_sort |
investigation of super learner methodology on hiv-1 small sample: application on jaguar trial data |
publisher |
Hindawi Limited |
series |
AIDS Research and Treatment |
issn |
2090-1240 2090-1259 |
publishDate |
2012-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2012/478467 |
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