AIDS Research and Treatment 5 Table 2: Squared error, R detailed values and corresponding rank on Jaguar trial data for 10-fold, 4-fold, 3-fold, and 2-fold. Method LM(1) LM(2) Random Forest D/S/A CART LogicReg Super Learner-6 Method LM(1) LM(2) Random Forest D/S/A CART LogicReg Super Learner-6 Rank 1 7 2 4 3 6 5 7 3 6 4 5 1.5 Rank 1.5 10-fold 10-fold Mean 0.216 1.218 0.258 0.283 0.264 0.653 0.378 Mean 0.464 0.808 0.609 0.712 0.632 0.702 0.456 SqE with Logic Reg. Rank 4-fold Rank 3-fold 1 − R with Logic Reg. Rank 4-fold Rank 3-fold 2 7 1 3 4 6 5 3 7 2 4 5 6 1 2 7 1 3 4 6 5 3 7 2 4 5 6 1 Mean 0.238 1.650 0.235 0.255 0.258 0.652 0.499 Mean 0.534 0.686 0.532 0.607 0.611 0.684 0.485 Rank 2-fold Rank 2-fold 2 7 1 3 4 6 5 2 7 3 6 5 4 1 Mean 0.293 1.117 0.275 0.295 0.298 0.653 0.527 Mean 0.651 0.754 0.656 0.746 0.743 0.657 0.593 Mean rank 1.75 7 1.25 3.25 3.75 6 5 Mean rank 2.375 7 2.5 5 4.75 5.25 1.125 Mean 0.246 1.267 0.241 0.264 0.267 0.65 0.455 Mean 0.554 0.724 0.552 0.623 0.644 0.685 0.523 Value 0.319 0.540 0.271 0.193 0.299 0.540 0.539 Table 3: Squared Error, R, R2 and corresponding rank on Jaguar Trial full-dataset. Full Model Method LM (1) LM (2) Random Forest D/S/A CART Super Learner-5 Super Learner-6 Rank 5 1.5 4 7 6 3 1.5 SqE 1 − R/100 R2 Value Rank Value Rank 0.204 0.138 0.178 0.242 0.211 0.138 0.139 5 1.5 4 7 6 1.5 3 0.435 0.265 0.348 0.561 0.454 0.265 0.266 1.5 4 6 7 5 1.5 3 dataset. This was expected due to the relative small number of mutations compared with 1,000 trees generated by the Random Forest model The HIV-1 resistance study used either a continuous outcome (as HIV-1 RNA reduction from baseline to the time of interest) or a categorical outcome (classifying patients as achieving a virologic response at the time of interest). For example, virologic response can be deﬁned an HIV-1 reduc- tion of 1.5log10 copies/mL or more or having a viral load >50 copies/mL at the time of interest. Even if a continuous outcome is preferable as being more informative, the ﬁnal goal of determining the drug resistance mutations associated with a poorer virologic response is to classify patients as “sensible” or “resistant” to a speciﬁed drug. The former patients would receive the corresponding drug as a part of their regimen while the latter patients would not. We used two threshold values of −0.5 and −0.6log10 copies/mL to deﬁne virologic response. For both threshold values LM(2), Super Learner-5 and -6 provided the highest accuracy with approximately 80% of patients correctly classiﬁed. All the methods used in this work are usually applied to large or very large datasets. Simple linear regression model was ﬁtted on more than 5,000 genotype-phenotype paired datasets from the same database [6]. Investigation of logistic regression and nonlinear machine learning for predicting response to antiretroviral treatment was done on more than 3,000 treatment change episodes from the EuResist database [34]. All these analyses were made retrospectively mainly for comparing diﬀerent methods rather than for building rule- based algorithm. A major reason to apply the Super Learner methodology on the Jaguar trial is that often the ﬁrst version of an algo- rithm for a speciﬁc drug is based on a limited amount of data [35–37]. Such algorithms are updated later with publication of new data. Nonparametric methods are then often used on such a relative small amount of data [38, 39]. Parametric methods have the advantage of not only integrating two-way interactions terms but also adjusting for some other variables that improve the prediction. Randomized clinical trials, in treatment experienced patients, provide frequently the ﬁrst opportunity to investigate the impact of baseline mutations in the subsequent virologic response in those patients. It was then of interest to know whether the Super Learner meth- odology applied only on around one hundred of patients was able to produce the “best” learner on the basis of accuracy and prediction. The Jaguar trial which is an “add-on” study ensuring a good quality of relation between reverse trans- criptase mutations and eﬀect on the drug investigated, was a good opportunity for such investigation.