A. Kurowski, C. Luber, C. F. Merry, J. G. Perno, E. P. Gerber et al., Adaptive treatment strategies: An emerging approach for improving treatment effectiveness Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction « Understanding the slow depletion of memory CD4+ T cells in HIV infection « Longitudinal models for AIDS marker data Bivariate longitudinal model for the analysis of the evolution of HIV RNA and CD4 cell count in HIV infection taking into account left censoring of HIV RNA measures » Commenges, « Joint modelling of bivariate longitudinal data with informative dropout and left-censoring, with application to the evolution of CD4+ cell count and HIV RNA viral load in response to treatment of HIV infectionSpline Model for Multiple Longitudinal Biomarkers and Survival « HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time « Modeling antiretroviral drug response for HIV-1 infected patients using differential equation models Markowitz, et others, « Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection « The dependence of viral parameter estimates on the assumed viral life cycle: limitations of studies of viral load data « Two types of cytotoxic lymphocyte regulation explain kinetics of immune response to human immunodeficiency virus, Therapeutic drug monitoring in HIV infection: current status and future directions Therapeutic drug monitoring in the treatment of HIVinfection Journal of Clinical Virology Fitting dynamic models with forcing functions: Application to continuous glucose monitoring in insulin therapy Statistics in medicine Stochastic optimal therapy for enhanced immune response Mathematical biosciences PLoS medicine Viral dynamics in human immunodeficiency virus type 1 infection. », Nature18] D. S. Callaway et A. S. Perelson, « HIV-1 infection and low steady state viral loads19] A. S. Perelson et R. M. Ribeiro, « Estimating drug efficacy and viral dynamic parameters: HIV and HCV » Mathematical modeling of systems pharmacogenomics towards personalized drug delivery Virus dynamics and drug therapy Proceedings of the National Academy of Sciences Proceedings of the Royal Society of London. Series B: Biological Sciences Proceedings of the National Academy of Sciences of the United States of America Pre?existence and emergence of drug resistance in HIV?1 infection Proceedings of the Royal Society of London. Series B: Biological Sciences HIV population dynamics in vivo: implications for genetic variation, pathogenesis, and therapy27] E. Vergu, A. Mallet, et J. L. Golmard, « A modeling approach to the impact of HIV mutations on the immune system HIV dynamics: modeling, data analysis, and optimal treatment protocols Decay dynamics of HIV-1 depend on the inhibited stages of the viral life cycle Proceedings of the National Academy of Sciences30] R. J. Smith et L. M. Wahl, « Distinct effects of protease and reverse transcriptase inhibition in an immunological model of HIV-1 infection with impulsive drug effects Practical identifiability of HIV dynamics models32] X. Xia et C. H. Moog, « Identifiability of nonlinear systems with application to HIV/AIDS models », Automatic Control, pp.5-37, 1382.

D. M. Bortz, P. W. Nelson35-]-l, R. M. Wein, E. A. Amato, S. Perelson et al., A mathematical model of continuous HIV mutations eluding immune defence « Mathematical analysis of antiretroviral therapy aimed at HIV-1 eradication or maintenance of low viral loads [36] H. Wu, « Statistical methods for HIV dynamic studies in AIDS clinical trials », Statistical Methods in Medical Research De Wolf, « A Bayesian approach to parameter estimation in HIV dynamical models » « Hierarchical Bayesian methods for estimation of parameters in a longitudinal HIV dynamic system « Hierarchical Bayesian inference for HIV dynamic differential equation models incorporating multiple treatment factors, 39] J. Guedj, R. Thiébaut, et D. Commenges, « Maximum likelihood estimation in dynamical models of HIV41] E. Kuhn et M. Lavielle, « Maximum likelihood estimation in nonlinear mixed effects models » The ALBI trial: a randomized controlled trial comparing stavudine plus didanosine with zidovudine plus lamivudine and a regimen alternating both combinations in previously untreated patients infected with human immunodeficiency virus43] J. Drylewicz, D. Commenges, et R. Thiébaut, « Score tests for exploring complex models: application to HIV dynamics models, pp.2005-2025, 1998.

M. V. Kleist, S. Menz, E. W. Huisinga, M. Drug, D. Prague et al., A Program for Inference via Normal Approximation of the Posterior in Models with Random effects based on Ordinary Differential Equations « Performance Comparison of Various Maximum Likelihood Nonlinear Mixed-Effects Estimation Methods for Dose?Response Models », The AAPS journal, PLoS computational biology Biometrics, vol.648, issue.3, pp.1000720-1000721, 2010.

V. Picard, E. Angelini, A. Maillard, E. Race, F. Clavel et al., Comparison of Genotypic and Phenotypic Resistance Patterns of Human Immunodeficiency Virus Type 1 Isolates from Patients Treated with Stavudine and Didanosine or Zidovudine and Lamivudine, The Journal of Infectious Diseases, vol.184, issue.6, pp.781-784, 2001.
DOI : 10.1086/323088

G. Antonelli, O. T. Turriziani, J. R. Parkin, J. M. King, and I. Weidler, « Mathematical analysis of HIV-I: dynamics in vivo [51] H. Wu et A. A. Ding, « Population HIV-1 Dynamics In Vivo: Applicable Models and Inferential Tools for Virological Data from A novel antiviral intervention results in more accurate assessment of human immunodeficiency virus type 1 replication dynamics and T-cell decay in vivo, Antiviral therapy: old and current issues SIAM review, pp.3-44, 1999.

E. C. Ofotokun, M. Petropoulos, D. J. Boffito, T. F. Back, M. Blaschke et al., « Novel method to assess antiretroviral target trough concentrations using in vitro susceptibility data, Protein binding in antiretroviral therapies, pp.825-835, 2003.

C. Bazzoli, V. Jullien, C. Le-tiec, E. Rey, F. Mentré et al., Intracellular Pharmacokinetics of Antiretroviral Drugs in HIV-Infected Patients, and their Correlation with Drug Action, Clinical Pharmacokinetics, vol.41, issue.11, pp.17-45, 2010.
DOI : 10.1111/j.1468-1293.2008.00513.x

URL : https://hal.archives-ouvertes.fr/inserm-00461125

E. S. Perez-elías, C. Moreno-zhou, T. Alcock, D. Kiefer, J. Monie et al., « Novel single-cell-level phenotypic assay for residual drug susceptibility and reduced replication capacity of drug-resistant human immunodeficiency virus type 1 » et others, « Dose-response curve slope sets class-specific limits on inhibitory potential of anti-HIV drugs » Kuritzkes, « Instantaneous inhibitory potential is similar to inhibitory quotient at predicting HIV-1 response to antiretroviral therapy, Individualizing salvage regimens: the inhibitory quotient A novel method for determining the inhibitory potential of anti-HIV drugs Trends in pharmacological sciences Pharmacokinetics and pharmacodynamics of drug interactions involving HIV-1 protease inhibitors, pp.262-264, 2003.

J. B. Fitzgerald, B. Schoeberl, U. B. Nielsen, P. K. Sorger63-]-b, M. Jilek et al., « Systems biology and combination therapy in the quest for clinical efficacy « A quantitative basis for antiretroviral therapy for HIV-1 infection », Buss et N. Cammack, « Measuring the effectiveness of antiretroviral agents », Antiviral therapy, pp.458-466, 2001.

M. Barry, S. Gibbons, D. Back, F. M. Huang, E. P. Acosta et al., Pharmacodynamics of antiretroviral agents in HIV-1 infected patients: using viral dynamic models that incorporate drug susceptibility and adherence » « From in vitro EC 50 to in vivo dose?response for antiretrovirals using an HIV disease model. Part I: A framework Pierce, « A systematic review of the associations between dose regimens and medication compliance, Protease inhibitors in patients with HIV disease. Clinically important pharmacokinetic considerations. », Clinical pharmacokinetics Compliance in clinical trials Antiretroviral medication adherence and the development of class-specific antiretroviral resistance, pp.194-399, 1997.

J. R. Ickovics, A. W. Meisler, E. P. Huang, S. L. Acosta, D. R. Rosenkranz et al., « Modeling long-term HIV dynamics and antiretroviral response: effects of drug potency, pharmacokinetics, adherence, and drug resistance Adherence and drug resistance: predictions for therapy outcome Adherence to antiretroviral HIV drugs: how many doses can you miss before resistance emerges? The Lancet, AIDS clinical trials Proc. R. Soc. B Antiretroviral dynamics determines HIV evolution and predicts therapy outcome Emergence of HIV-1 drug resistance during antiretroviral treatment Genotypic-resistance assays and antiretroviral therapy, pp.385-391, 1445.

M. E. Quiñones-mateu, E. J. Arts-de-béthune, V. Miller, T. Ivens, P. Schel et al., « HIV-1 fitness: implications for drug resistance, disease progression, and global epidemic evolution », HIV sequence compendium, pp.134-170, 2001.

C. J. Petropoulos, N. T. Parkin, K. L. Limoli, Y. S. Lie, T. Wrin et al., et others, « Virtual inhibitory quotient predicts response to ritonavir boosting of indinavir-based therapy in human immunodeficiency virus-infected patients with ongoing viremia, Isolates from Patients Treated with Antiretroviral Drugs 920? 928, pp.269-281, 1998.

M. E. Sampah, L. Shen, B. L. Jilek, and R. F. Siliciano, Dose-response curve slope is a missing dimension in the analysis of HIV-1 drug resistance, Proceedings of the National Academy of Sciences, pp.7613-7618, 2011.
DOI : 10.1128/JVI.78.4.1718-1729.2004

G. Pellegrin, F. Chêne, R. Dabis, M. A. Thiébaut, R. M. Nowak et al., « The evolutionary dynamics of HIV-1 quasispecies and the development of immunodeficiency disease « Dynamic multidrug therapies for HIV: a control theoretic approach « Modeling within-host HIV-1 dynamics and the evolution of drug resistance: trade-offs between viral enzyme function and drug susceptibility, Alternative methods to analyse the impact of HIV mutations on virological response to antiviral therapy Production of resistant HIV mutants during antiretroviral therapy Proceedings of the National Academy of Sciences, pp.68-1095, 1990.

C. Montagne, J. Boucher, E. P. Schapiro, and . Dellamonica, « Drug-resistance genotyping in HIV-1 therapy: the VIRAD APT randomi sed controlled trial », The Lancet, pp.9171-2195, 1999.

E. Domingo, J. J. Holland, D. Turner, B. Brenner, M. A. Huang et al., Multiple Effects of the M184V Resistance Mutation in the Reverse Transcriptase of Human Immunodeficiency Virus Type 1 « Modeling and estimation of replication fitness of human immunodeficiency virus type 1 in vitro experiments by using a growth competition assay « Differential equation modeling of HIV viral fitness experiments: model identification, model selection, and multimodel inference The dual role of pharmacogenetics in HIV treatment: mutations and polymorphisms regulating antiretroviral drug resistance and disposition, RNA virus mutations and fitness for survival Annual Reviews in Microbiology Effect of Adherence as Measured by MEMS, Ritonavir Boosting, and CYP3A5 Genotype on Atazanavir Pharmacokinetics in Treatment-Naive HIV-Infected Patients Pharmacogenetics of Anti-HIV Drugs Influence of pharmacogenetics on indinavir disposition and short-term response in HIV patients initiating HAART », European journal of clinical pharmacology Verstuyft, P. D. Leger, F. Mentré, A. M. Taburet, et D. W. Haas, « Multiple genetic variants predict steady-state nevirapine clearance in HIV-infected Cambodians, pp.151-178, 1997.

M. Hecker, S. Lambeck, S. Toepfer, E. Van-someren, and E. R. Guthke, Gene regulatory network inference: Data integration in dynamic models???A review, Biosystems, vol.96, issue.1, pp.86-103, 2009.
DOI : 10.1016/j.biosystems.2008.12.004

T. Lu, H. Liang, H. Li, and E. H. Wu, High-Dimensional ODEs Coupled With Mixed-Effects Modeling Techniques for Dynamic Gene Regulatory Network Identification, Journal of the American Statistical Association, vol.106, issue.496, pp.496-1242, 2011.
DOI : 10.1198/jasa.2011.ap10194

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3509540

M. Egger, M. May, G. Chêne, A. N. Phillips, B. Ledergerber et al., Prognosis of HIV-1-infected patients starting highly active antiretroviral therapy: a collaborative analysis of prospective studies, The Lancet, vol.360, issue.9327, pp.9327-119, 2002.
DOI : 10.1016/S0140-6736(02)09411-4

R. Thiébaut, G. Chêne, H. Jacqmin-gadda, P. Morlat, P. Mercié et al., Time-Updated CD4+ T Lymphocyte Count and HIV RNA as Major Markers of Disease Progression in Naive HIV-1-Infected Patients Treated With a Highly Active Antiretroviral Therapy, JAIDS Journal of Acquired Immune Deficiency Syndromes, vol.33, issue.3, pp.380-386, 1996.
DOI : 10.1097/00126334-200307010-00013

M. D. Hughes, M. J. Daniels, M. A. Fischl, S. Kim, and R. T. Schooley, CD4 cell count as a surrogate endpoint in HIV clinical trials, AIDS, vol.12, issue.14, pp.14-1823, 1998.
DOI : 10.1097/00002030-199814000-00014

H. Surrogate, . Collaborative-group, and . Human, Immunodeficiency Virus Type 1 RNA Level and CD4 Count as Prognostic Markers and Surrogate End Points: A Meta-Analysis, AIDS Research and Human Retroviruses, vol.16, issue.12, pp.1123-1133, 2000.

C. Lewden, T. May, E. Rosenthal, C. Burty, F. Bonnet et al., Changes in Causes of Death Among Adults Infected by HIV Between 2000 and 2005: The ???Mortalit?? 2000 and 2005??? Surveys (ANRS EN19 and Mortavic), The? Mortalite 2000 and 2005? surveys (ANRS EN19 and Mortavic), pp.590-598, 2000.
DOI : 10.1097/QAI.0b013e31817efb54

D. Abrams, Y. Lévy, M. H. Losso, A. Babiker, G. Collins et al., Interleukin-2 therapy in patients with HIV infection, The New England journal of medicine, vol.361, pp.16-1548, 2009.
URL : https://hal.archives-ouvertes.fr/inserm-00426386

V. , D. Gruttola, and X. M. Tu, « Modelling progression of CD4-lymphocyte count and its relationship to survival time, Biometrics, pp.1003-1014, 1994.

A. A. Tsiatis and M. Davidian, « Joint modeling of longitudinal and time-to-event data: an overview », Statistica Sinica, vol.14, issue.3, pp.809-834, 2004.

X. Song, M. Davidian, and A. A. , A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time-to-Event Data, Biometrics, vol.57, issue.4, pp.742-753, 2004.
DOI : 10.1111/j.0006-341X.2001.00795.x

N. Pantazis, G. Touloumi, A. S. Walker, and A. G. Babiker, Bivariate modelling of longitudinal measurements of two human immunodeficiency type 1 disease progression markers in the presence of informative drop-outs, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.45, issue.2, pp.405-423, 2005.
DOI : 10.1002/1521-4036(200203)44:2<175::AID-BIMJ175>3.0.CO;2-3

L. Wu, X. J. Hu, and E. H. Wu, Joint inference for nonlinear mixed-effects models and time to event at the presence of missing data, Biostatistics, vol.9, issue.2, pp.308-320, 2008.
DOI : 10.1093/biostatistics/kxm029

J. Guedj and R. Thiébaut, Joint Modeling of the Clinical Progression and of the Biomarkers' Dynamics Using a Mechanistic Model, Biometrics, vol.70, issue.1, pp.59-66, 2011.
DOI : 10.1007/s11538-007-9279-9

J. H. Stein, Cardiovascular Risks of Antiretroviral Therapy, New England Journal of Medicine, vol.356, issue.17, pp.1773-1775, 2007.
DOI : 10.1056/NEJMe078037

E. S. Rosenberg, M. Davidian, and H. T. Banks, Using mathematical modeling and control to develop structured treatment interruption strategies for HIV infection, Drug and Alcohol Dependence, vol.88, pp.41-51, 2007.
DOI : 10.1016/j.drugalcdep.2006.12.024

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2001151

Y. Lévy, C. Durier, A. S. Lascaux, V. Meiffrédy, H. Gahéry-ségard et al., Sustained control of viremia following therapeutic immunization in chronically HIV-1-infected individuals, AIDS, vol.20, issue.3, pp.405-413, 2006.
DOI : 10.1097/01.aids.0000206504.09159.d3

Y. Lévy, R. Thiébaut, M. L. Gougeon, J. M. Molina, L. Weiss et al., Effect of intermittent interleukin-2 therapy on CD4+ T-cell counts following antiretroviral cessation in patients with HIV, AIDS, vol.26, issue.6, p.711, 2012.
DOI : 10.1097/QAD.0b013e3283519214

W. M. El-sadr, J. D. Lundgren, J. D. Neaton, F. Gordin, D. Abrams et al., « CD4+ count-guided interruption of antiretroviral treatment », New Engl J Med, vol.355, pp.22-2283, 2006.

D. Kirschner, S. Lenhart, and E. S. Serbin, Optimal control of the chemotherapy of HIV, Journal of Mathematical Biology, vol.35, issue.7, pp.775-792, 1997.
DOI : 10.1007/s002850050076

D. J. Austin, N. J. White, and R. M. Anderson, The Dynamics of Drug Action on the Within-host Population Growth of Infectious Agents: Melding Pharmacokinetics with Pathogen Population Dynamics, Journal of Theoretical Biology, vol.194, issue.3, pp.313-339, 1998.
DOI : 10.1006/jtbi.1997.0438

M. J. Van-der-laan and M. L. Petersen, Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules, The International Journal of Biostatistics, vol.3, issue.1, pp.1-53, 2007.
DOI : 10.2202/1557-4679.1022

X. Duval, F. Mentré, E. Rey, S. Auleley, G. Peytavin et al., Benefit of therapeutic drug monitoring of protease inhibitors in HIV-infected patients depends on PI used in HAART regimen - ANRS 111 trial, Fundamental & Clinical Pharmacology, vol.42, issue.Suppl. 1, pp.491-500, 2009.
DOI : 10.5414/CPP43154

URL : https://hal.archives-ouvertes.fr/inserm-00416211

D. J. Back, S. H. Khoo, S. E. Gibbons, and E. C. Merry, The role of therapeutic drug monitoring in treatment of HIV infection, British Journal of Clinical Pharmacology, vol.13, issue.Suppl 4, pp.301-308, 2001.
DOI : 10.7326/0003-4819-124-12-199606150-00003

S. H. Khoo, J. Lloyd, M. Dalton, A. Bonington, E. Hart et al., Pharmacologic Optimization of Protease Inhibitors and Nonnucleoside Reverse Transcriptase Inhibitors (POPIN)-A Randomized Controlled Trial of Therapeutic Drug Monitoring and Adherence Support, JAIDS Journal of Acquired Immune Deficiency Syndromes, vol.41, issue.4, pp.461-467, 2006.
DOI : 10.1097/01.qai.0000218345.65434.21

J. D. Baxter, D. L. Mayers, D. N. Wentworth, J. D. Neaton, M. L. Hoover et al., A randomized study of antiretroviral management based on plasma genotypic antiretroviral resistance testing in patients failing therapy, AIDS, vol.14, issue.9, pp.83-93, 2000.
DOI : 10.1097/00002030-200006160-00001

C. J. Cohen, S. Hunt, M. Sension, C. Farthing, M. Conant et al., A randomized trial assessing the impact of phenotypic resistance testing on antiretroviral therapy, AIDS, vol.16, issue.4, pp.579-588, 2002.
DOI : 10.1097/00002030-200203080-00009

J. L. Meynard, M. Vray, L. Morand-joubert, E. Race, D. Descamps et al., Phenotypic or genotypic resistance testing for choosing antiretroviral therapy after treatment failure: a randomized trial, AIDS, vol.16, issue.5, pp.727-736, 2002.
DOI : 10.1097/00002030-200203290-00008

URL : http://pdfs.journals.lww.com/aidsonline/2002/03290/Phenotypic_or_genotypic_resistance_testing_for.8.pdf?token=method|ExpireAbsolute;source|Journals;ttl|1503958763533;payload|mY8D3u1TCCsNvP5E421JYK6N6XICDamxByyYpaNzk7FKjTaa1Yz22MivkHZqjGP4kdS2v0J76WGAnHACH69s21Csk0OpQi3YbjEMdSoz2UhVybFqQxA7lKwSUlA502zQZr96TQRwhVlocEp/sJ586aVbcBFlltKNKo+tbuMfL73hiPqJliudqs17cHeLcLbV/CqjlP3IO0jGHlHQtJWcICDdAyGJMnpi6RlbEJaRheGeh5z5uvqz3FLHgPKVXJzdN2ziiHhY6wPF9tfA80junv9SdemaIlw6K7U2cr6kytw=;hash|eDJECyutCTfr0OtF2/OHWw==

S. A. Murphy, An experimental design for the development of adaptive treatment strategies, Statistics in Medicine, vol.26, issue.10, pp.1455-1481, 2005.
DOI : 10.1002/9780470316481

B. M. Best, M. Goicoechea, M. D. Witt, L. Miller, E. S. Daar et al., A Randomized Controlled Trial of Therapeutic Drug Monitoring in Treatment-Naive and -Experienced HIV-1-Infected Patients, JAIDS Journal of Acquired Immune Deficiency Syndromes, vol.46, issue.4, pp.433-442, 2007.
DOI : 10.1097/QAI.0b013e318156f029

Y. Lévy, I. Sereti, G. Tambussi, J. P. Routy, J. D. Lelièvre et al., Effects of Recombinant Human Interleukin 7 on T-Cell Recovery and Thymic Output in HIV-Infected Patients Receiving Antiretroviral Therapy: Results of a Phase I/IIa Randomized, Placebo-Controlled, Multicenter Study, Effects of Recombinant Human Interleukin 7 on T-Cell Recovery and Thymic Output in HIV-Infected Patients Receiving Antiretroviral Therapy: Results of a Phase I/IIa Randomized, Placebo-Controlled, pp.291-300, 2012.
DOI : 10.1093/cid/cis383

M. A. Mcmahon, L. Shen, and R. F. Siliciano, New approaches for quantitating the inhibition of HIV-1 replication by antiviral drugs in vitro and in vivo, Current Opinion in Infectious Diseases, vol.22, issue.6, pp.574-582, 2009.
DOI : 10.1097/QCO.0b013e328332c54d