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Your search keyword '"Thiebaut, Rodolphe"' showing total 367 results
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367 results on '"Thiebaut, Rodolphe"'

1. Running in circles: is practical application feasible for data fission and data thinning in post-clustering differential analysis?

Author

Hivert, Benjamin, Agniel, Denis, Thiébaut, Rodolphe, and Hejblum, Boris P.

Subjects
Statistics - Methodology
Abstract

The standard pipeline to analyse single-cell RNA sequencing (scRNA-seq) often involves two steps : clustering and Differential Expression Analysis (DEA) to annotate cell populations based on gene expression. However, using clustering results for data-driven hypothesis formulation compromises statistical properties, especially Type I error control. Data fission was introduced to split the information contained in each observation into two independent parts that can be used for clustering and testing. However, data fission was originally designed for non-mixture distributions, and adapting it for mixtures requires knowledge of the unknown clustering structure to estimate component-specific scale parameters. As components are typically unavailable in practice, scale parameter estimators often exhibit bias. We explicitly quantify how this bias affects subsequent post-clustering differential analysis Type I error rate despite employing data fission. In response, we propose a novel approach that involves modeling each observation as a realization of its distribution, with scale parameters estimated non-parametrically. Simulations study showcase the efficacy of our method when component are clearly separated. However, the level of separability required to reach good performance presents complexities in its application to real scRNA-seq data.

Published
2024

2. Clonal succession after prolonged antiretroviral therapy rejuvenates CD8+ T cell responses against HIV-1

Author

White, Eoghann, Papagno, Laura, Samri, Assia, Sugata, Kenji, Hejblum, Boris, Henry, Amy R., Rogan, Daniel C., Darko, Samuel, Recordon-Pinson, Patricia, Dudoit, Yasmine, Llewellyn-Lacey, Sian, Chakrabarti, Lisa A., Buseyne, Florence, Migueles, Stephen A., Price, David A., Andreola, Marie-Aline, Satou, Yorifumi, Thiebaut, Rodolphe, Katlama, Christine, Autran, Brigitte, Douek, Daniel C., and Appay, Victor

Published
2024
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3. Post-clustering difference testing: valid inference and practical considerations

Author

Hivert, Benjamin, Agniel, Denis, Thiébaut, Rodolphe, and Hejblum, Boris P

Subjects
Statistics - Methodology
Abstract

Clustering is part of unsupervised analysis methods that consist in grouping samples into homogeneous and separate subgroups of observations also called clusters. To interpret the clusters, statistical hypothesis testing is often used to infer the variables that significantly separate the estimated clusters from each other. However, data-driven hypotheses are considered for the inference process, since the hypotheses are derived from the clustering results. This double use of the data leads traditional hypothesis test to fail to control the Type I error rate particularly because of uncertainty in the clustering process and the potential artificial differences it could create. We propose three novel statistical hypothesis tests which account for the clustering process. Our tests efficiently control the Type I error rate by identifying only variables that contain a true signal separating groups of observations.

Published
2022

4. How to improve the quality of comparisons using external control cohorts in single-arm clinical trials?

Author

Lambert, Jérôme, Lengline, Etienne, Porcher, Raphaël, Thiébaut, Rodolphe, Zohar, Sarah, and Chevret, Sylvie

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

PURPOSE Providing rapid answers and early acces to patients to innovative treatments without randomized clinical trial (RCT) is growing, with benefit estimated from single-arm trials. This has become common in oncology, impacting the approval pathway of health technology assessment agencies. We aimed to provide some guidance for indirect comparison to external controls to improve the level of evidence following such uncontrolled designs. METHODS We used the illustrative example of blinatumomab, a bispecific antibody for the treatment of B-cell ALL in complete remission (CR) with persistent minimal residual disease (MRD). Its approval relied on a single-arm trial conducted in 86 adults with B-cell ALL in CR, with undetectable MRD after one cycle as the main endpoint. To maximize the validity of indirect comparisons, a 3-step process for incorporating external control data to such single-arm trial data is proposed and detailed, with emphasis on the example. RESULTS The first step includes the definition of estimand, i.e. the treatment effect reflecting the clinical question. The second step relies on the adequate selection of external controls, from previous RCT or real-world data (RWD) obtained from patient cohort, registries, or electronic patient files. The third step consists in chosing the statistical approach targeting the treatment effect of interest, either in the whole population or restricted to the single-arm trial or the external controls, and depending on the available individual-level or aggregrated external data. CONCLUSION Validity of treatment effect derived from indirect comparisons heavily depends on carefull methodological considerations that are included in the proposed 3-step procedure. Because the level of evidence of a well conducted RCT cannot be guaranteed, post-market authorization evaluation is even more important than in standard settings., Comment: 12 pages;1 table; 2figures

Published
2022

5. Evaluating the Use of Generalized Dynamic Weighted Ordinary Least Squares for Individualized HIV Treatment Strategies

Author

Dong, Larry, Moodie, Erica E. M., Villain, Laura, and Thiébaut, Rodolphe

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

Dynamic treatment regimes (DTR) are a statistical paradigm in precision medicine which aim to optimize patient outcomes by individualizing treatments. At its simplest, a DTR may require only a single decision to be made; this special case is called an individualized treatment rule (ITR) and is often used to maximize short-term rewards. Generalized dynamic weighted ordinary least squares (G-dWOLS), a DTR estimation method that offers theoretical advantages such as double robustness of parameter estimators in the decision rules, has been recently extended to now accommodate categorical treatments. In this work, G-dWOLS is applied to longitudinal data to estimate an optimal ITR, which is demonstrated in simulations. This novel method is then applied to a population affected by HIV whereby an ITR for the administration of Interleukin 7 (IL-7) is devised to maximize the duration where the CD4 load is above a healthy threshold (500 cells/$\mu$L) while preventing the administration of unnecessary injections.

Published
2021

6. Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report

Author

Nunes, Marta C., Thommes, Edward, Fröhlich, Holger, Flahault, Antoine, Arino, Julien, Baguelin, Marc, Biggerstaff, Matthew, Bizel-Bizellot, Gaston, Borchering, Rebecca, Cacciapaglia, Giacomo, Cauchemez, Simon, Barbier--Chebbah, Alex, Claussen, Carsten, Choirat, Christine, Cojocaru, Monica, Commaille-Chapus, Catherine, Hon, Chitin, Kong, Jude, Lambert, Nicolas, Lauer, Katharina B., Lehr, Thorsten, Mahe, Cédric, Marechal, Vincent, Mebarki, Adel, Moghadas, Seyed, Niehus, Rene, Opatowski, Lulla, Parino, Francesco, Pruvost, Gery, Schuppert, Andreas, Thiébaut, Rodolphe, Thomas-Bachli, Andrea, Viboud, Cecile, Wu, Jianhong, Crépey, Pascal, and Coudeville, Laurent

Published
2024
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10. A vaccine targeting antigen-presenting cells through CD40 induces protective immunity against Nipah disease

Author

Pastor, Yadira, Reynard, Olivier, Iampietro, Mathieu, Surenaud, Mathieu, Picard, Florence, El Jahrani, Nora, Lefebvre, Cécile, Hammoudi, Adele, Dupaty, Léa, Brisebard, Élise, Reynard, Stéphanie, Moureaux, Élodie, Moroso, Marie, Durand, Stéphanie, Gonzalez, Claudia, Amurri, Lucia, Gallouët, Anne-Sophie, Marlin, Romain, Baize, Sylvain, Chevillard, Eve, Raoul, Hervé, Hocini, Hakim, Centlivre, Mireille, Thiébaut, Rodolphe, Horvat, Branka, Godot, Véronique, Lévy, Yves, and Cardinaud, Sylvain

Published
2024
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12. Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: an optimal control approach

Author

Clairon, Quentin, Pasin, Chloé, Balelli, Irene, Thiébaut, Rodolphe, and Prague, Mélanie

Subjects
Statistics - Methodology
Abstract

We present a parameter estimation method for nonlinear mixed effect models based on ordinary differential equations (NLME-ODEs). The method presented here aims at regularizing the estimation problem in presence of model misspecifications, practical identifiability issues and unknown initial conditions. For doing so, we define our estimator as the minimizer of a cost function which incorporates a possible gap between the assumed model at the population level and the specific individual dynamic. The cost function computation leads to formulate and solve optimal control problems at the subject level. This control theory approach allows to bypass the need to know or estimate initial conditions for each subject and it regularizes the estimation problem in presence of poorly identifiable parameters. Comparing to maximum likelihood, we show on simulation examples that our method improves estimation accuracy in possibly partially observed systems with unknown initial conditions or poorly identifiable parameters with or without model error. We conclude this work with a real application on antibody concentration data after vaccination against Ebola virus coming from phase 1 trials. We use the estimated model discrepancy at the subject level to analyze the presence of model misspecification.

Published
2021

13. EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

Author

Colas, Cédric, Hejblum, Boris, Rouillon, Sébastien, Thiébaut, Rodolphe, Oudeyer, Pierre-Yves, Moulin-Frier, Clément, and Prague, Mélanie

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Quantitative Biology - Populations and Evolution
Abstract

Epidemiologists model the dynamics of epidemics in order to propose control strategies based on pharmaceutical and non-pharmaceutical interventions (contact limitation, lock down, vaccination, etc). Hand-designing such strategies is not trivial because of the number of possible interventions and the difficulty to predict long-term effects. This task can be cast as an optimization problem where state-of-the-art machine learning algorithms such as deep reinforcement learning, might bring significant value. However, the specificity of each domain -- epidemic modelling or solving optimization problem -- requires strong collaborations between researchers from different fields of expertise. This is why we introduce EpidemiOptim, a Python toolbox that facilitates collaborations between researchers in epidemiology and optimization. EpidemiOptim turns epidemiological models and cost functions into optimization problems via a standard interface commonly used by optimization practitioners (OpenAI Gym). Reinforcement learning algorithms based on Q-Learning with deep neural networks (DQN) and evolutionary algorithms (NSGA-II) are already implemented. We illustrate the use of EpidemiOptim to find optimal policies for dynamical on-off lock-down control under the optimization of death toll and economic recess using a Susceptible-Exposed-Infectious-Removed (SEIR) model for COVID-19. Using EpidemiOptim and its interactive visualization platform in Jupyter notebooks, epidemiologists, optimization practitioners and others (e.g. economists) can easily compare epidemiological models, costs functions and optimization algorithms to address important choices to be made by health decision-makers.

Published
2020
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14. Safety and immunogenicity of the two-dose heterologous Ad26.ZEBOV and MVA-BN-Filo Ebola vaccine regimen in infants: a phase 2, randomised, double-blind, active-controlled trial in Guinea and Sierra Leone

Author

Mooney, T, Conteh, L, Bangura, MS, Bangura, MA, Jalloh, H, Kamara, I, Kamara, M, Koroma, S, Sesay, M, Sesay, MT, Deen, AT, Kamara, A, Kamara, EL, Kamara, SLM, Koroma, AH, Mansaray, IS, Massaquoi, MJ, Nabie, A, Kowuor, D, Njie, Y, Odeny, L, Sheku, M, Jalloh, AB, Sow, A, Swaray, E, Mansaray, F, Sessie, T, Sunders, J-HC, Turay, SI-S, Weekes, J, Pessima, M, Wurie, A, Conteh, M, Jalloh, MI, Kamara, PBD, Kanneh, DP, Kanneh, M, Komeh, I, Koroma, M, Kuyateh, M, Mansaray, FF, Leigh, B, Watson-Jones, D, Samai, M, Deen, GF, Sesay, T, Piot, P, Greenwood, B, Lees, S, Larson, H, Afolabi, M, Ishola, D, Baiden, F, Faye, F, Tindanbil, D, Kamara, MM, Swaray, IB, Bangura, A, Kamara, AB, Morovia, FE, Kallon, JA, Murray, M, Sesay, F, Suma, F, Sesay, IG, Choi, EM, Manno, D, Foster, J, Rwezaula, R, Akhigbe, I, Adetola, H, Kamara, B, Lowe, B, Lawal, B, Kohn, B, Tuda, GO, Koroma, F, Bangura, G, Kroma, MT, Fofanah, L, Pessima, A, Rogers, M, Sheriff, O, Fangawa, J, Foday, S, Jabbie, I, Mansaray, HA, Sesay, K, Jakema, HB, Sheku, MF, Jalloh, KFN, Kabba, M, Kanjie, F, Kanu, AP, Kassa-Koroma, G, Jusu, M, Koroma, B, Borboh, P, Kallon, A, van Roey, K, Conteh, P, Samura, M, Gandie, V, Marrah, M, Kalokoh, J, Bangura, MI, Connor, N, Saidu, S, Turay, AS, Lahai, A, Johnson, CL, Kogba, DB, Vincent, W, Bangura, M, Tengbeh, A, Bangura, K, Kabia, R, Nyakoi, AM, Lee, S, Nyaberi, D, Ndingi, S, Nyallay, L, Bangura, AR, Idriss, B, Sillah, M, Mackay, W, Murray, T, Edem-Hotah, J, Fatorma, T, Bangura, S, Bonnie, E, Sannoh, M, Malcolm, S, Brown, J, Snowden, E, Howard, K, Ojugo, A, Massin-Shepherd, C, BEAVOGUI, AH, KEITA, CM, CAMARA, OK, GUILAVOGUI, JPY, BAH, H, SAMOURA, MA, MUAMBA, D, SEMAKUBA, B, CAMARA, AK, KABA, AS, BERERD-CAMARA, M, YARADOUNO, M, DECHENAUD, M, CAMARA, MT, TAMBALOU, J, HABA, M, DIALLO, SD, THEA, A, DOUMBOUYA, N, FOFANA, ML, BEAVOGUI, M, CAMARA, AA, BEAVOGUI, JT, DIOUF, W, AUGIER, A, BARTE DE SAINTE FARE, E, SIVAHERA MUYISA, B, SANI, S, VATRINET, R, HAMZE, B, LACARRA, B, D’ORTENZIO, E, ALE, B, BETARD, C, RICHERT, L, OULAI, D, KANTE, M, SOUTTHIPHONG, A-A, SCHWIMMER, C, THIÉBAUT, R, OTTAVI, A, COUFFIN-CADIERGUES, S, ESPEROU, H, Chai, SP, Buth, W, Offergeld, K, Menten, A, Hammoud, N, De Ridder, S, Sellecchia, R, in ’t Veld, R, Fogap, N, Anumendem, D, Stapleton, H, Reijns, T, Haydon, J, Roza, L, Sawyer, B, Hoda, S, Yee, J, De Cnodder, T, Hubin, E, Telen, L, Desai, J, Bennet, M, Pawlowski, M, van Gils, N, Boeykens, N, Kwasniak, A, Ligthart, M, Van Roey, G, Fernandez, E, Gaddah, A, van Dijck, W, Jingshuang, S, Randrasana, S, Artis, C, Akinbinu, A, Poretti, A, Van Ballaert, S, Harris, M, Van Looveren, M, Brandt, P, Robinson, C, Bockstal, V, McLean, C, Versteege, I, Ferrault, C, Kaminski, A, Vergauwen, H, Kerama, CI, Forcheh, CA, DiMondi, CV, Stewart, L, Meurer, J, Beounitis, A, Peeters, J, Su, C, Keshinro, B, Delport, C, Sharkie, E, Zhang, J, Du, C, Hu, K, Strydom, A, Bezem-Aviv, I, Wachnicka, A, Kumar, P, Cheng, S, Kang, K, Choi, Edward Man-Lik, Lacarra, Boris, Afolabi, Muhammed O, Ale, Boni Maxime, Baiden, Frank, Bétard, Christine, Foster, Julie, Hamzé, Benjamin, Schwimmer, Christine, Manno, Daniela, D’Ortenzio, Eric, Ishola, David, Keita, Cheick Mohamed, Keshinro, Babajide, Njie, Yusupha, van Dijck, Wim, Gaddah, Auguste, Anumendem, Dickson, Lowe, Brett, Vatrinet, Renaud, Lawal, Bolarinde Joseph, Otieno, Godfrey T, Samai, Mohamed, Deen, Gibrilla Fadlu, Swaray, Ibrahim Bob, Kamara, Abu Bakarr, Kamara, Michael Morlai, Diagne, Mame Aminata, Kowuor, Dickens, McLean, Chelsea, Leigh, Bailah, Beavogui, Abdoul Habib, Leyssen, Maarten, Luhn, Kerstin, Robinson, Cynthia, Douoguih, Macaya, Greenwood, Brian, Thiébaut, Rodolphe, and Watson-Jones, Deborah

Published
2023
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16. Neuropsychiatric manifestations and sleep disturbances with dolutegravir-based antiretroviral therapy versus standard of care in children and adolescents: a secondary analysis of the ODYSSEY trial

Author

Mehar (nee Abdulla), Amina Farhana, Abraham, Pattamukkil, Abrams, Elaine, Acero, Judith, Agaba, Gerald Muzorah, Ahimbisibwe, Grace, Ainebyoona, Barbara, Akobye, Winnie, Akhalwaya, Yasmeen, Akoojee, Nazim, Ali, Shabinah S., Amuge, Pauline, Andrea, Catherine, Muñoz Fernandez, Maria Angeles, Ankunda, Rogers, Rutebarika, Diana Antonia, Anugulruengkitt, Suvaporn, Apollo, Tsitsi, Archary, Moherndran, Arendze, Ronelle, Ategeka, Juliet, Atim, Eunice, Atwine, Lorna, Babiker, Abdel, Babirye, Sarah, Babu, Enock, Bagirigomwa, Edward, Baita, Angella, Balamusani, David, Baliram, Patsy, Baliruno, David, Ball, Colin, Balwa, Henry, Bamford, Alasdair, Bandi, Srini, Barker, Dominique, Barlow-Mosha, Linda, Bbuye, Dickson, Begum, Shazia, Behuhuma, Osee, Bernays, Sarah, Besigye, Rogers, Bester, Maria, Bhiri, Joyline, Bilardi, Davide, Bird, Kristien, Bollen, Pauline, Borg, Chiara, Borges Da Silva, Anne-Marie, Brown, Jackie, Bruno, Elena, Bunupuradah, Torsak, Burger, David, Buthelezi, Nomzamo, Bwakura-Dangarembizi, Mutsa, Byaruhanga, Africanus, Calvert, Joanna, Casey, Petronelle, Cassim, Haseena, Cebekhulu, Sphiwee, Chailert, Sanuphong, Chalermpantmetagul, Suwalai, Chamjamrat, Wanna, Chan, Man, Chandiwana, Precious, Chankun, Thannapat, Chanthaburanun, Sararut, Chanto, Nuttawut, Chidziva, Ennie, Chikowore, Minenhle, Chimanzi, Joy, Chinwong, Dujrudee, Chitongo, Stuart, Chitsamatanga, Moses, Choga, Joshua, Chutima, Duangrat, Clayden, Polly, Coelho, Alexandra, Colbers, Angela, Compagnucci, Alexandra, Constança Mendes, Ana, Conway, Magda, Cotton, Mark F, Crawley, Jane, Cressey, Tim R, Crisp, Jacky, Matos, Ana Cristina, Dadan, Sumaya, Daglish, Jacqui, Danaviah, Siva, Daniel, Tseleng, De Rossi, Anita, Denjanta, Sukanda, Dobbels, Els, Dowie, Maria, Dube, Prosper, Dube, Benedictor, Dudakia, Nimisha, Elwana, Alice, Epalza, Cristina, Eram, David, Erasmus, Juan, Erim, Peter, Escosa Garcia, Luis, Essack, Zaakirah, Estepa, Carolina, Etima, Monica, Fernandes, Alexandre, Fernandez, Maite, Fitzgerald, Felicity, Flynn, Jacquie, Ford, Deborah, Fortuny Guasch, Claudia, Foster, Caroline, Fourie, George, Fourie, Yolandie, Foxall, Sophie, Frank, Derusha, Gandhi, Kate, Garcia, India, Gartner, Kathleen, Gasa, Joshua, Gasa, Gugu, Giaquinto, Carlo, Gibb, Diana M, Gomez Rico, Coral, Gomez-Pena, Daniel, Gondo, Secrecy, Goodman, Anna, Gorreti Nakalema, Maria, Gozhora, Winnie, Greetanukroh, Pisut, Gregorio Maranon, Biobanco, Grossele, Tiziana, Gwande, Shamiso, Gwaze, Tapiwa, Gwenzi, Tsitsi, Hakim, James, Hakiza, Emmanuel, Kaka, Abdul Hamid, Harley, Ashley, Isaacs, Mornay, Isabirye, Richard, Ishemunyoro, Wilber, Jacobs, Tom, Jafta, Lungile, Jamil, Nasir, Janse van Rensburg, Anita Janse, Jeaven, Vinesh, Mellado Peña, Maria José, Jourdain, Gonzague, Juliet, Katabalwa, Jumpimai, Thidarat, Junkaew, Raungwit, Jupimai, Thidarat, Kaahwa, Winfred, Kabasonga, Mildred, Kaboggoza, Olivia, Kadhuba, Rose Jacqueline, Kaewbundit, Ampika, Kaewmamueng, Kanyanee, Kafufu, Bosco, Kakayi, Brenda, Kamboua, Phakamas, Kanjanavanit, Suparat, Kasangaki, Gladys, Kasipong, Naruporn, Kasozi, Miriam, Kataike, Hajira, Katemba, Chrispus, Kaudha, Elizabeth, Kekane, Nkata, Kekitiinwa, Adeodata R, Keminyeto, Edridah, Khamduang, Woottichai, Khamjakkaew, Warunee, Khamkon, Jiraporn, Khannak, Sasipass, Khatngam, Orapin, Khayanchoomnoom, Tassawan, Khumalo, Busi, Khunene, Mirriam, Khusuwan, Suwimon, Kibalama, Phionah, Kibenge, Robinah, Kirabira, Anthony, Kityo, Cissy M, Kiyimba, Lameck, Klein, Nigel, Klinprung, Soraya, Kobbe, Robin, Kobusingye, Olivia, Kobusungye, Josephine, Kongponoi, Areerat, Königs, Christoph, Koole, Olivier, Kouakam, Christelle, Krueduangkam, Nitinart, Kruenual, Namthip, Kunjaroenrut, Nuananong, Kyambadde, Raymonds, Kyobutungi, Priscilla, Kyomuhendo, Flavia, Kyomukama, Erinah, Lakha, Reshma, Langa, Cleopatra, Laomanit, Laddawan, Lebotsa, Emily, Leenasirimakul, Prattana, Lekku, Lawrence, Lensen, Sarah, Leroy, Valériane, Li, Jin, Liberty, Afaaf, Limplertjareanwanich, Juthamas, Little, Emma, Lugemwa, Abbas, Lutalo, Ezra, Jimenez, Jose Luis, Lyall, Hermione, MacDonald, Candice, Machache, Gladness, Madlala, Penelope, Madonsela, Tryphina, Maduna, Nomfundo, Maena, Joel, Mahanontharit, Apicha, Makanga, Collin, Makola, Candice, Makumbi, Shafic, Malgraaf, Lucille, Mamiane, Angelous, Mantkowski, Felicia, Mapfumo, Wendy, Marques, Laura, Mugagga, Agnes Mary, Maseko, Lindiwe, Masienyane, Tshepiso, Mathiba, Ruth, Matimba, Farai, Mawlana, Sajeeda, Mayanja, Emmanuel, Mayat, Fatima, Mbabazi, Ritah, Mbadaliga, Nokuthula, Mbasani, Faith, McClaughlin, Kathleen, McIlleron, Helen, Meethaisong, Watchara, Mendez Garcia, Patricia, Miwanda, Annet, Miranda, Carlota, Mkhize, Siphiwe, Mmolawa, Kgosimang, Mngqibisa, Rosie, Mohamed, Fatima, Moloantoa, Tumelo, Monametsi, Maletsatsi, Montero, Samuel, Moore, Cecilia L, Mosia, Rejoice, Moyo, Columbus, Mthethwa, Mumsy, Mudzingwa, Shepherd, Mudzviti, Tawona, Mujuru, Hilda, Mujyambere, Emmanuel, Mukanganiki, Trust, Mukisa Williams, Cynthia, Mulder, Mark, Mulima, Disan, Mulindwa, Alice, Mumbiro, Vivian, Mupambireyi, Zivai, Murciano Cabeza, Alba, Murungi, Herbert, Murungu, Dorothy, Musarurwa, Sandra, Musiime, Victor, Musiime, Alex V, Musisi, Maria, Musoke, Philippa, Musoke Nakirya, Barbara, Musoro, Godfrey, Musumba, Sharif, Mustafa, Sobia, Mutsai, Shirley, Mwesigwa Rubondo, Phyllis, Naabalamba, Mariam, Nagawa, Immaculate, Naidoo, Allemah, Nakabuye, Shamim, Nakabuye, Sarah, Nakalanzi, Sarah, Nalubwama, Justine, Nalugo, Annet, Nalusiba, Stella, Namajja, Clementine, Namanda, Sylvia, Namayanja, Paula, Nambi, Esther, Namuddu, Rachael Kikabi, Namukwaya, Stella, Namuli, Florence, Namusanje, Josephine, Namwanje, Rosemary, Nanan-kanjee, Anusha, Nanduudu, Annet, Nankunda, Charity, Baddokwaya, Joanita Nankya, Nannungi, Maria, Nansamba, Winnie, Nanthapisal, Kesdao, Nanyonjo, Juliet, Na-Rajsima, Sathaporn, Nasaazi, Claire, Nascimento, Helena, Nastouli, Eleni, Songtaweesin, Wipaporn Natalie, Nathoo, Kusum, Natuhurira, Ian, Nazzinda, Rashidah, Ncgaba, Thabisa, Ndigendawani, Milly, Ndlovu, Makhosonke, Nentsa, Georgina, Ngampiyaskul, Chaiwat, Ngcobo, Ntombenhle, Ngo Giang Huong, Nicole, Ngwaru, Pia, Nhema, Ruth, Ninsiima, Emily, Ninsiima, Gloria, Nkalo Phiri, Misheck, Noguera Julian, Antoni, Nolan, Monica, Noppakaorattanamanee, Thornthun, Nsibuka Kisekka, Muzamil, Nsirim, Eniola, Nundlal, Rashina, Nunes, Rosita, Nyantsa, Lungile, Nyati, Mandisa, O'Riordan, Sean, Ocitti Labeja, Paul, Odoch, Denis, Oguntimehin, Rachel, Ojok, Martin, Onen, Geoffrey, Orange, Wilma, Ounchanum, Pradthana, Ouma, Benson, Padrao, Andreia, Pako, Deborah, Parker, Anna, Pasko-Szcech, Malgorzata, Patel, Reena, Peongjakta, Rukchanok, Petpranee, Turian, Phillips, Tasmin, Philps, Jackie, Picault, Laura, Pieterse, Sonja, Pinheiro, Helena, Pongprapass, Supawadee, Pozniak, Anton, Prendergast, Andrew, Prieto Tato, Luis, Puangmalai, Patcharee, Puthanakit, Thanyawee, Rakgokong, Modiehi, Ramos, Helena, Ramsagar, Nastassja, Rau, Cornelius, Riault, Yoann, Rojo Conejo, Pablo, Clark, Basiimwa Roy, Rubanga, Eddie, Rubinga, Baker, Ruklao, Chutima, Runarassamee, Pattira, Saenjum, Chalermpong, Saewtrakool, Chayakorn, Saidi, Yacine, Sainz Costa, Talia, Saisaengjan, Chutima, Sakwa, Rebecca, Sarfati, Tatiana, Sbisi, Noshalaza, Scheppers, Dihedile, Schultze-Strasser, Stephan, Schulze-Sturm, Ulf, Scott, Karen, Seeley, Janet, Serunjogi, Robert, Sewnarain, Leora, Shakeshaft, Clare, Sidhoo, Subashinie, Shibemba, Mercy, Shingadia, Delane, Singh, Sheleika, Sirirungsi, Wasna, Sithebe, Sibongile, Smit, Theresa, Smith, Kurt, Smuts, Marlize, Spyer, Moira, Sripaoraya, Worathip, Srirompotong, Ussanee, Srisuk, Warunee, Ssenyonga, Mark, Sudsaard, Patamawadee, Sukrakanchana, Praornsuda, Tearsansern, Pathanee, Teixeira, Carla, Than-in-at, Kanchana, Thapwai, Thitiwat, Thaweesombat, Yupawan, Thewsoongnoen, Jutarat, Thiébaut, Rodolphe, Thomason, Margaret, Thrasyvoulou, Laura, Thungkham, Khanungnit, Tikabibamu, Judith, Tinago, Gloria, Trairat, Ketmookda, Tudor-Williams, Gareth, Tukamushaba, Mercy, Tukwasibwe, Deogratiuos, Tumusiime, Julius, Tuna, Joana, Turkova, Anna, Turner, Rebecca, Udomvised, Arttasid, Vadee, Aasia, Van Huyssteen, Hesti, Van Looy, Nadine, Variava, Ebrahim, Vaughan-Gordon, Yvonne, Vecchia, Giulio, Violari, Avy, Vowden, Richard, Waalewijn, Hylke, Wampamba, Rebecca, Welch, Steve, Weller, Ian, Weza, Sibusisiwe, White, Ellen, White, Ian, Widuch, Kaja, Wilkes, Helen, Wimonklang, Sookpanee, Wynne, Ben, Yingyong, Pacharaporn, Nakawungu, Zaam Zinda, Zuidewind, Peter, Ahimbisibwe, Grace Miriam, Mosia, Nozibusiso Rejoice, Fortuny, Clàudia, Bwakura-Dangarembizi, Mutsa F, Mujuru, Hilda A, Kityo, Cissy, Ferrand, Rashida A, and Rojo, Pablo

Published
2023
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18. Fr\'echet random forests for metric space valued regression with non euclidean predictors

Author

Capitaine, Louis, Bigot, Jérémie, Thiébaut, Rodolphe, and Genuer, Robin

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology
Abstract

Random forests are a statistical learning method widely used in many areas of scientific research because of its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forest approaches are not flexible enough to handle heterogeneous data such as curves, images and shapes. In this paper, we introduce Fr\'echet trees and Fr\'echet random forests, which allow to handle data for which input and output variables take values in general metric spaces. To this end, a new way of splitting the nodes of trees is introduced and the prediction procedures of trees and forests are generalized. Then, random forests out-of-bag error and variable importance score are naturally adapted. A consistency theorem for Fr\'echet regressogram predictor using data-driven partitions is given and applied to Fr\'echet purely uniformly random trees. The method is studied through several simulation scenarios on heterogeneous data combining longitudinal, image and scalar data. Finally, one real dataset about air quality is used to illustrate the use of the proposed method in practice.

Published
2019

19. Random forests for high-dimensional longitudinal data

Author

Capitaine, Louis, Genuer, Robin, and Thiébaut, Rodolphe

Subjects
Statistics - Methodology
Abstract

Random forests is a state-of-the-art supervised machine learning method which behaves well in high-dimensional settings although some limitations may happen when $p$, the number of predictors, is much larger than the number of observations $n$. Repeated measurements can help by offering additional information but no approach has been proposed for high-dimensional longitudinal data. Random forests have been adapted to standard (i.e., $n > p$) longitudinal data by using a semi-parametric mixed-effects model, in which the non-parametric part is estimated using random forests. We first propose a stochastic extension of the model which allows the covariance structure to vary over time. Furthermore, we develop a new method which takes intra-individual covariance into consideration to build the forest. Simulations reveal the superiority of our approach compared to existing ones. The method has been applied to an HIV vaccine trial including 17 HIV infected patients with 10 repeated measurements of 20000 gene transcripts and the blood concentration of human immunodeficiency virus RNA at the time of antiretroviral interruption. The approach selected 21 gene transcripts for which the association with HIV viral load was fully relevant and consistent with results observed during primary infection.

Published
2019

20. Supervised Learning for Multi-Block Incomplete Data

Author

Lorenzo, Hadrien, Saracco, Jérôme, and Thiébaut, Rodolphe

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Applications
Abstract

In the supervised high dimensional settings with a large number of variables and a low number of individuals, one objective is to select the relevant variables and thus to reduce the dimension. That subspace selection is often managed with supervised tools. However, some data can be missing, compromising the validity of the sub-space selection. We propose a Partial Least Square (PLS) based method, called Multi-block Data-Driven sparse PLS mdd-sPLS, allowing jointly variable selection and subspace estimation while training and testing missing data imputation through a new algorithm called Koh-Lanta. This method was challenged through simulations against existing methods such as mean imputation, nipals, softImpute and imputeMFA. In the context of supervised analysis of high dimensional data, the proposed method shows the lowest prediction error of the response variables. So far this is the only method combining data imputation and response variable prediction. The superiority of the supervised multi-block mdd-sPLS method increases with the intra-block and inter-block correlations. The application to a real data-set from a rVSV-ZEBOV Ebola vaccine trial revealed interesting and biologically relevant results. The method is implemented in a R-package available on the CRAN and a Python-package available on pypi., Comment: 44 pages

Published
2019

21. Immune response of a two-dose heterologous Ebola vaccine regimen: summary of three African clinical trials using a single validated Filovirus Animal Nonclinical Group enzyme-linked immunosorbent assay in a single accredited laboratory

Author

McLean, Chelsea, Barry, Houreratou, Kieh, Mark, Anywaine, Zacchaeus, Tapima Rogers, Baimba, Doumbia, Seydou, Sirima, Sodiomon B., Serry-Bangura, Alimamy, Habib Beavogui, Abdoul, Gaddah, Auguste, Katwere, Michael, Hendriks, Jenny, Keshinro, Babajide, Eholie, Serge, Kibuuka, Hannah, Kennedy, Stephen B., Anzala, Omu, Samai, Mohamed, D'Ortenzio, Eric, Leigh, Bailah, Sow, Samba, Thiébaut, Rodolphe, Greenwood, Brian, Watson-Jones, Deborah, Douoguih, Macaya, Luhn, Kerstin, and Robinson, Cynthia

Published
2023
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22. Controlling IL-7 injections in HIV-infected patients

Author

Pasin, Chloé, Dufour, François, Villain, Laura, Zhang, Huilong, and Thiébaut, Rodolphe

Subjects
Mathematics - Optimization and Control, Quantitative Biology - Quantitative Methods
Abstract

Immune interventions consisting in repeated injection are broadly used as they are thought to improve the quantity and the quality of the immune response. However, they also raised several questions that remains unanswered, in particular the number of injections to make or the delay to respect between different injections to acheive this goal. Practical and financial considerations add constraints to these questions, especially in the framework of human studies. We specifically focus here on the use of interleukine-7 (IL-7) injections in HIV-infected patients under antiretroviral treatment, but still unable to restore normal levels of CD4+ T lymphocytes. Clinical trials have already shown that repeated cycles of injections of IL-7 could help maintaining CD4+ T lymphocytes levels over the limit of 500 cells per microL, by affecting proliferation and survival of CD4+ T cells. We then aim at answering the question : how to maintain a patient's level of CD4+ T lymphocytes by using a minimum number of injections (ie optimizing the strategy of injections) ? Based on mechanistic models that were previously developed for the dynamics of CD4+ T lymphocytes in this context, we model the process by a piecewise deterministic Markov model. We then address the question by using some recently established theory on impulse control problem in order to develop a numerical tool determining the optimal strategy. Results are obtained on a reduced model, as a proof of concept : the method allows to defined an optimal strategy for a given patient. This method could applied to optimize injections schedules in clinical trials.

Published
2018

23. Viral rebound in semen after antiretroviral treatment interruption in an HIV therapeutic vaccine double-blind trial

Author

Palich, Romain, Ghosn, Jade, Chaillon, Antoine, Boilet, Valérie, Nere, Marie-Laure, Chaix, Marie-Laure, Delobel, Pierre, Molina, Jean-Michel, Lucht, Frédéric, Bouchaud, Olivier, Rieux, Véronique, Thiebaut, Rodolphe, Levy, Yves, Delaugerre, Constance, and Lelievre, Jean-Daniel

Subjects
HIV/AIDS, Clinical Trials and Supportive Activities, Pediatric, Immunization, Genetics, Clinical Research, Infectious Diseases, Vaccine Related, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Infection, Good Health and Well Being, Adult, Anti-Retroviral Agents, Blood, HIV Infections, HIV-1, Humans, Male, Middle Aged, Randomized Controlled Trials as Topic, Semen, Viral Load, Withholding Treatment, compartmentalization, HIV-DNA, HIV-RNA, male genital tract, phylogenic analysis, therapeutic vaccine trial, ultradeep sequencing, VRI02/ANRS149 LIGHT Vaccine Trial Group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Virology
Abstract

ObjectivesThis study aimed to determine the timing and level of HIV rebound in blood and seminal plasma and to characterize the HIV rebounding populations after antiretroviral treatment interruption (ATI) in HIV-1-infected participants enrolled in a therapeutic vaccine trial.DesignA 12-week (W) ATI period was proposed at W36 to patients enrolled in the VRI02/ANRS149-LIGHT trial. Paired blood and semen samples were collected before (W32 or W36) and during ATI (W38, W40, W42, W44, and W48).MethodsHIV-RNA and HIV-DNA were quantified sequentially from blood and semen samples. Ultradeep sequencing (UDS; Roche/454) of partial env HIV-DNA/RNA (C2V3) was performed in both compartments.ResultsHIV-RNA rebounded in blood plasma and seminal plasma of all ten participants after ATI [median peak of 5.12 log10 cp/ml (range: 4.61-6.35) and 4.26 log10 cp/ml (3.20-4.67), respectively]. HIV-RNA rebound was detected in blood plasma as soon as W38 in 8/10 patients, and in seminal plasma between W38 and W40 in 8/10 patients. Phylogenetic approaches showed intermingled HIV-RNA populations from plasma and semen during ATI, suggesting a lack of viral compartmentalization between blood and semen.ConclusionOur data demonstrate rapid and high HIV rebound in semen after ATI, raising concerns about high risk of HIV sexual transmission during HIV cure trials.

Published
2019

24. Dolutegravir twice-daily dosing in children with HIV-associated tuberculosis: a pharmacokinetic and safety study within the open-label, multicentre, randomised, non-inferiority ODYSSEY trial

Author

Mehar (nee Abdulla), Amina Farhana, Abraham, Pattamukkil, Abrams, Elaine, Acero, Judith, Agaba, Gerald Muzorah, Ahimbisibwe, Grace, Ainebyoona, Barbara, Akobye, Winnie, Akhalwaya, Yasmeen, Akoojee, Nazim, Ali, Shabinah S., Amuge, Pauline, Andrea, Catherine, Muñoz Fernandez, Maria Angeles, Ankunda, Rogers, Rutebarika, Diana Antonia, Anugulruengkitt, Suvaporn, Apollo, Tsitsi, Archary, Moherndran, Arendze, Ronelle, Ategeka, Juliet, Atim, Eunice, Atwine, Lorna, Babiker, Abdel, Babirye, Sarah, Babu, Enock, Bagirigomwa, Edward, Baita, Angella, Balamusani, David, Baliram, Patsy, Baliruno, David, Ball, Colin, Balwa, Henry, Bamford, Alasdair, Bandi, Srini, Barker, Dominique, Barlow-Mosha, Linda, Bbuye, Dickson, Begum, Shazia, Behuhuma, Osee, Bernays, Sarah, Besigye, Rogers, Bester, Maria, Bhiri, Joyline, Bilardi, Davide, Bird, Kristien, Bollen, Pauline, Borg, Chiara, Borges Da Silva, Anne-Marie, Brown, Jackie, Bruno, Elena, Bunupuradah, Torsak, Burger, David, Buthelezi, Nomzamo, Bwakura-Dangarembizi, Mutsa, Byaruhanga, Africanus, Calvert, Joanna, Casey, Petronelle, Cassim, Haseena, Cebekhulu, Sphiwee, Chailert, Sanuphong, Chalermpantmetagul, Suwalai, Chamjamrat, Wanna, Chan, Man, Chandiwana, Precious, Chankun, Thannapat, Chanthaburanun, Sararut, Chanto, Nuttawut, Chidziva, Ennie, Chikowore, Minenhle, Chimanzi, Joy, Chinwong, Dujrudee, Chitongo, Stuart, Chitsamatanga, Moses, Choga, Joshua, Chutima, Duangrat, Clayden, Polly, Coelho, Alexandra, Colbers, Angela, Compagnucci, Alexandra, Constança Mendes, Ana, Conway, Magda, Cotton, Mark F., Crawley, Jane, Cressey, Tim R., Crisp, Jacky, Matos, Ana Cristina, Dadan, Sumaya, Daglish, Jacqui, Danaviah, Siva, Daniel, Tseleng, De Rossi, Anita, Denjanta, Sukanda, Dobbels, Els, Dowie, Maria, Dube, Prosper, Dube, Benedictor, Dudakia, Nimisha, Elwana, Alice, Epalza, Cristina, Eram, David, Erasmus, Juan, Erim, Peter, Escosa Garcia, Luis, Essack, Zaakirah, Estepa, Carolina, Etima, Monica, Fernandes, Alexandre, Fernandez, Maite, Fitzgerald, Felicity, Flynn, Jacquie, Ford, Deborah, Fortuny Guasch, Claudia, Foster, Caroline, Fourie, George, Fourie, Yolandie, Foxall, Sophie, Frank, Derusha, Gandhi, Kate, Garcia, India, Gartner, Kathleen, Gasa, Joshua, Gasa, Gugu, Giaquinto, Carlo, Gibb, Diana M., Gomez Rico, Coral, Gomez-Pena, Daniel, Gondo, Secrecy, Goodman, Anna, Gorreti Nakalema, Maria, Gozhora, Winnie, Greetanukroh, Pisut, Gregorio Maranon, Biobanco, Grossele, Tiziana, Gwande, Shamiso, Gwaze, Tapiwa, Gwenzi, Tsitsi, Hakim, James, Hakiza, Emmanuel, Kaka, Abdul Hamid, Harley, Ashley, Isaacs, Mornay, Isabirye, Richard, Ishemunyoro, Wilber, Jacobs, Tom, Jafta, Lungile, Jamil, Nasir, Janse van Rensburg, Anita Janse, Jeaven, Vinesh, Mellado Peña, Maria José, Jourdain, Gonzague, Juliet, Katabalwa, Jumpimai, Thidarat, Junkaew, Raungwit, Jupimai, Thidarat, Kaahwa, Winfred, Kabasonga, Mildred, Kaboggoza, Olivia, Kadhuba, Rose Jacqueline, Kaewbundit, Ampika, Kaewmamueng, Kanyanee, Kafufu, Bosco, Kakayi, Brenda, Kamboua, Phakamas, Kanjanavanit, Suparat, Kasangaki, Gladys, Kasipong, Naruporn, Kasozi, Miriam, Kataike, Hajira, Katemba, Chrispus, Kaudha, Elizabeth, Kekane, Nkata, Kekitiinwa, Adeodata R., Keminyeto, Edridah, Khamduang, Woottichai, Khamjakkaew, Warunee, Khamkon, Jiraporn, Khannak, Sasipass, Khatngam, Orapin, Khayanchoomnoom, Tassawan, Khumalo, Busi, Khunene, Mirriam, Khusuwan, Suwimon, Kibalama, Phionah, Kibenge, Robinah, Kirabira, Anthony, Kityo, Cissy M., Kiyimba, Lameck, Klein, Nigel, Klinprung, Soraya, Kobbe, Robin, Kobusingye, Olivia, Kobusungye, Josephine, Kongponoi, Areerat, Königs, Christoph, Koole, Olivier, Kouakam, Christelle, Krueduangkam, Nitinart, Kruenual, Namthip, Kunjaroenrut, Nuananong, Kyambadde, Raymonds, Kyobutungi, Priscilla, Kyomuhendo, Flavia, Kyomukama, Erinah, Lakha, Reshma, Langa, Cleopatra, Laomanit, Laddawan, Lebotsa, Emily, Leenasirimakul, Prattana, Lekku, Lawrence, Lensen, Sarah, Leroy, Valériane, Li, Jin, Liberty, Afaaf, Limplertjareanwanich, Juthamas, Little, Emma, Lugemwa, Abbas, Lutalo, Ezra, Jimenez, Jose Luis, Lyall, Hermione, MacDonald, Candice, Machache, Gladness, Madlala, Penelope, Madonsela, Tryphina, Maduna, Nomfundo, Maena, Joel, Mahanontharit, Apicha, Makanga, Collin, Makola, Candice, Makumbi, Shafic, Malgraaf, Lucille, Mamiane, Angelous, Mantkowski, Felicia, Mapfumo, Wendy, Marques, Laura, Mugagga, Agnes Mary, Maseko, Lindiwe, Masienyane, Tshepiso, Mathiba, Ruth, Matimba, Farai, Mawlana, Sajeeda, Mayanja, Emmanuel, Mayat, Fatima, Mbabazi, Ritah, Mbadaliga, Nokuthula, Mbasani, Faith, McClaughlin, Kathleen, McIlleron, Helen, Meethaisong, Watchara, Mendez Garcia, Patricia, Miwanda, Annet, Miranda, Carlota, Mkhize, Siphiwe, Mmolawa, Kgosimang, Mngqibisa, Rosie, Mohamed, Fatima, Moloantoa, Tumelo, Monametsi, Maletsatsi, Montero, Samuel, Moore, Cecilia L., Mosia, Rejoice, Moyo, Columbus, Mthethwa, Mumsy, Mudzingwa, Shepherd, Mudzviti, Tawona, Mujuru, Hilda, Mujyambere, Emmanuel, Mukanganiki, Trust, Mukisa Williams, Cynthia, Mulder, Mark, Mulima, Disan, Mulindwa, Alice, Mumbiro, Vivian, Mupambireyi, Zivai, Murciano Cabeza, Alba, Murungi, Herbert, Murungu, Dorothy, Musarurwa, Sandra, Musiime, Victor, Musiime, Alex V., Musisi, Maria, Musoke, Philippa, Musoke Nakirya, Barbara, Musoro, Godfrey, Musumba, Sharif, Mustafa, Sobia, Mutsai, Shirley, Mwesigwa Rubondo, Phyllis, Naabalamba, Mariam, Nagawa, Immaculate, Naidoo, Allemah, Nakabuye, Shamim, Nakabuye, Sarah, Nakalanzi, Sarah, Nalubwama, Justine, Nalugo, Annet, Nalusiba, Stella, Namajja, Clementine, Namanda, Sylvia, Namayanja, Paula, Nambi, Esther, Namuddu, Rachael Kikabi, Namukwaya, Stella, Namuli, Florence, Namusanje, Josephine, Namwanje, Rosemary, Nanan-kanjee, Anusha, Nanduudu, Annet, Nankunda, Charity, Baddokwaya, Joanita Nankya, Nannungi, Maria, Nansamba, Winnie, Nanthapisal, Kesdao, Nanyonjo, Juliet, Na-Rajsima, Sathaporn, Nasaazi, Claire, Nascimento, Helena, Nastouli, Eleni, Songtaweesin, Wipaporn Natalie, Nathoo, Kusum, Natuhurira, Ian, Nazzinda, Rashidah, Ncgaba, Thabisa, Ndigendawani, Milly, Ndlovu, Makhosonke, Nentsa, Georgina, Ngampiyaskul, Chaiwat, Ngcobo, Ntombenhle, Ngo Giang Huong, Nicole, Ngwaru, Pia, Nhema, Ruth, Ninsiima, Emily, Ninsiima, Gloria, Nkalo Phiri, Misheck, Noguera Julian, Antoni, Nolan, Monica, Noppakaorattanamanee, Thornthun, Nsibuka Kisekka, Muzamil, Nsirim, Eniola, Nundlal, Rashina, Nunes, Rosita, Nyantsa, Lungile, Nyati, Mandisa, O'Riordan, Sean, Ocitti Labeja, Paul, Odoch, Denis, Oguntimehin, Rachel, Ojok, Martin, Onen, Geoffrey, Orange, Wilma, Ounchanum, Pradthana, Ouma, Benson, Padrao, Andreia, Pako, Deborah, Parker, Anna, Pasko-Szcech, Malgorzata, Patel, Reena, Peongjakta, Rukchanok, Petpranee, Turian, Phillips, Tasmin, Philps, Jackie, Picault, Laura, Pieterse, Sonja, Pinheiro, Helena, Pongprapass, Supawadee, Pozniak, Anton, Prendergast, Andrew, Prieto Tato, Luis, Puangmalai, Patcharee, Puthanakit, Thanyawee, Rakgokong, Modiehi, Ramos, Helena, Ramsagar, Nastassja, Rau, Cornelius, Riault, Yoann, Rojo Conejo, Pablo, Clark, Basiimwa Roy, Rubanga, Eddie, Rubinga, Baker, Ruklao, Chutima, Runarassamee, Pattira, Saenjum, Chalermpong, Saewtrakool, Chayakorn, Saidi, Yacine, Sainz Costa, Talia, Saisaengjan, Chutima, Sakwa, Rebecca, Sarfati, Tatiana, Sbisi, Noshalaza, Scheppers, Dihedile, Schultze-Strasser, Stephan, Schulze-Sturm, Ulf, Scott, Karen, Seeley, Janet, Serunjogi, Robert, Sewnarain, Leora, Shakeshaft, Clare, Sidhoo, Subashinie, Shibemba, Mercy, Shingadia, Delane, Singh, Sheleika, Sirirungsi, Wasna, Sithebe, Sibongile, Smit, Theresa, Smith, Kurt, Smuts, Marlize, Spyer, Moira, Sripaoraya, Worathip, Srirompotong, Ussanee, Srisuk, Warunee, Ssenyonga, Mark, Sudsaard, Patamawadee, Sukrakanchana, Praornsuda, Tearsansern, Pathanee, Teixeira, Carla, Than-in-at, Kanchana, Thapwai, Thitiwat, Thaweesombat, Yupawan, Thewsoongnoen, Jutarat, Thiébaut, Rodolphe, Thomason, Margaret, Thrasyvoulou, Laura, Thungkham, Khanungnit, Tikabibamu, Judith, Tinago, Gloria, Trairat, Ketmookda, Tudor-Williams, Gareth, Tukamushaba, Mercy, Tukwasibwe, Deogratiuos, Tumusiime, Julius, Tuna, Joana, Turkova, Anna, Turner, Rebecca, Udomvised, Arttasid, Vadee, Aasia, Van Huyssteen, Hesti, Van Looy, Nadine, Variava, Ebrahim, Vaughan-Gordon, Yvonne, Vecchia, Giulio, Violari, Avy, Vowden, Richard, Waalewijn, Hylke, Wampamba, Rebecca, Welch, Steve, Weller, Ian, Weza, Sibusisiwe, White, Ellen, White, Ian, Widuch, Kaja, Wilkes, Helen, Wimonklang, Sookpanee, Wynne, Ben, Yingyong, Pacharaporn, Nakawungu, Zaam Zinda, Zuidewind, Peter, Chan, Man K, Bollen, Pauline D J, Bwakura-Dangarembizi, Mutsa F, Kekitiinwa, Adeodata R, Cotton, Mark F, Ahimbisibwe, Grace Miriam, Ali, Shabinah, Kityo, Cissy M, Ferrand, Rashida A, Gibb, Diana M, and Burger, David M

Published
2022
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25. Once-daily dolutegravir-based antiretroviral therapy in infants and children living with HIV from age 4 weeks: results from the below 14 kg cohort in the randomised ODYSSEY trial

Author

Mehar (nee Abdulla), Amina Farhana, Abraham, Pattamukkil, Abrams, Elaine, Acero, Judith, Agaba, Gerald Muzorah, Ahimbisibwe, Grace, Ainebyoona, Barbara, Akobye, Winnie, Akhalwaya, Yasmeen, Akoojee, Nazim, Ali, Shabinah S., Amuge, Pauline, Andrea, Catherine, Muñoz Fernandez, Maria Angeles, Ankunda, Rogers, Rutebarika, Diana Antonia, Anugulruengkitt, Suvaporn, Apollo, Tsitsi, Archary, Moherndran, Arendze, Ronelle, Ategeka, Juliet, Atim, Eunice, Atwine, Lorna, Babiker, Abdel, Babirye, Sarah, Babu, Enock, Bagirigomwa, Edward, Baita, Angella, Balamusani, David, Baliram, Patsy, Baliruno, David, Ball, Colin, Balwa, Henry, Bamford, Alasdair, Bandi, Srini, Barker, Dominique, Barlow-Mosha, Linda, Bbuye, Dickson, Begum, Shazia, Behuhuma, Osee, Bernays, Sarah, Besigye, Rogers, Bester, Maria, Bhiri, Joyline, Bilardi, Davide, Bird, Kristien, Bollen, Pauline, Borg, Chiara, Borges Da Silva, Anne-Marie, Brown, Jackie, Bruno, Elena, Bunupuradah, Torsak, Burger, David, Buthelezi, Nomzamo, Bwakura-Dangarembizi, Mutsa, Byaruhanga, Africanus, Calvert, Joanna, Casey, Petronelle, Cassim, Haseena, Cebekhulu, Sphiwee, Chailert, Sanuphong, Chalermpantmetagul, Suwalai, Chamjamrat, Wanna, Chan, Man, Chandiwana, Precious, Chankun, Thannapat, Chanthaburanun, Sararut, Chanto, Nuttawut, Chidziva, Ennie, Chikowore, Minenhle, Chimanzi, Joy, Chinwong, Dujrudee, Chitongo, Stuart, Chitsamatanga, Moses, Choga, Joshua, Chutima, Duangrat, Clayden, Polly, Coelho, Alexandra, Colbers, Angela, Compagnucci, Alexandra, Constança Mendes, Ana, Conway, Magda, Cotton, Mark F., Crawley, Jane, Cressey, Tim R., Crisp, Jacky, Matos, Ana Cristina, Dadan, Sumaya, Daglish, Jacqui, Danaviah, Siva, Daniel, Tseleng, De Rossi, Anita, Denjanta, Sukanda, Dobbels, Els, Dowie, Maria, Dube, Prosper, Dube, Benedictor, Dudakia, Nimisha, Elwana, Alice, Epalza, Cristina, Eram, David, Erasmus, Juan, Erim, Peter, Escosa Garcia, Luis, Essack, Zaakirah, Estepa, Carolina, Etima, Monica, Fernandes, Alexandre, Fernandez, Maite, Fitzgerald, Felicity, Flynn, Jacquie, Ford, Deborah, Fortuny Guasch, Claudia, Foster, Caroline, Fourie, George, Fourie, Yolandie, Foxall, Sophie, Frank, Derusha, Gandhi, Kate, Garcia, India, Gartner, Kathleen, Gasa, Joshua, Gasa, Gugu, Giaquinto, Carlo, Gibb, Diana M., Gomez Rico, Coral, Gomez-Pena, Daniel, Gondo, Secrecy, Goodman, Anna, Gorreti Nakalema, Maria, Gozhora, Winnie, Greetanukroh, Pisut, Gregorio Maranon, Biobanco, Grossele, Tiziana, Gwande, Shamiso, Gwaze, Tapiwa, Gwenzi, Tsitsi, Hakim, James, Hakiza, Emmanuel, Kaka, Abdul Hamid, Harley, Ashley, Isaacs, Mornay, Isabirye, Richard, Ishemunyoro, Wilber, Jacobs, Tom, Jafta, Lungile, Jamil, Nasir, Janse van Rensburg, Anita Janse, Jeaven, Vinesh, Mellado Peña, Maria José, Jourdain, Gonzague, Juliet, Katabalwa, Jumpimai, Thidarat, Junkaew, Raungwit, Jupimai, Thidarat, Kaahwa, Winfred, Kabasonga, Mildred, Kaboggoza, Olivia, Kadhuba, Rose Jacqueline, Kaewbundit, Ampika, Kaewmamueng, Kanyanee, Kafufu, Bosco, Kakayi, Brenda, Kamboua, Phakamas, Kanjanavanit, Suparat, Kasangaki, Gladys, Kasipong, Naruporn, Kasozi, Miriam, Kataike, Hajira, Katemba, Chrispus, Kaudha, Elizabeth, Kekane, Nkata, Kekitiinwa, Adeodata R., Keminyeto, Edridah, Khamduang, Woottichai, Khamjakkaew, Warunee, Khamkon, Jiraporn, Khannak, Sasipass, Khatngam, Orapin, Khayanchoomnoom, Tassawan, Khumalo, Busi, Khunene, Mirriam, Khusuwan, Suwimon, Kibalama, Phionah, Kibenge, Robinah, Kirabira, Anthony, Kityo, Cissy M., Kiyimba, Lameck, Klein, Nigel, Klinprung, Soraya, Kobbe, Robin, Kobusingye, Olivia, Kobusungye, Josephine, Kongponoi, Areerat, Königs, Christoph, Koole, Olivier, Kouakam, Christelle, Krueduangkam, Nitinart, Kruenual, Namthip, Kunjaroenrut, Nuananong, Kyambadde, Raymonds, Kyobutungi, Priscilla, Kyomuhendo, Flavia, Kyomukama, Erinah, Lakha, Reshma, Langa, Cleopatra, Laomanit, Laddawan, Lebotsa, Emily, Leenasirimakul, Prattana, Lekku, Lawrence, Lensen, Sarah, Leroy, Valériane, Li, Jin, Liberty, Afaaf, Limplertjareanwanich, Juthamas, Little, Emma, Lugemwa, Abbas, Lutalo, Ezra, Jimenez, Jose Luis, Lyall, Hermione, MacDonald, Candice, Machache, Gladness, Madlala, Penelope, Madonsela, Tryphina, Maduna, Nomfundo, Maena, Joel, Mahanontharit, Apicha, Makanga, Collin, Makola, Candice, Makumbi, Shafic, Malgraaf, Lucille, Mamiane, Angelous, Mantkowski, Felicia, Mapfumo, Wendy, Marques, Laura, Mugagga, Agnes Mary, Maseko, Lindiwe, Masienyane, Tshepiso, Mathiba, Ruth, Matimba, Farai, Mawlana, Sajeeda, Mayanja, Emmanuel, Mayat, Fatima, Mbabazi, Ritah, Mbadaliga, Nokuthula, Mbasani, Faith, McClaughlin, Kathleen, McIlleron, Helen, Meethaisong, Watchara, Mendez Garcia, Patricia, Miwanda, Annet, Miranda, Carlota, Mkhize, Siphiwe, Mmolawa, Kgosimang, Mngqibisa, Rosie, Mohamed, Fatima, Moloantoa, Tumelo, Monametsi, Maletsatsi, Montero, Samuel, Moore, Cecilia L., Mosia, Rejoice, Moyo, Columbus, Mthethwa, Mumsy, Mudzingwa, Shepherd, Mudzviti, Tawona, Mujuru, Hilda, Mujyambere, Emmanuel, Mukanganiki, Trust, Mukisa Williams, Cynthia, Mulder, Mark, Mulima, Disan, Mulindwa, Alice, Mumbiro, Vivian, Mupambireyi, Zivai, Murciano Cabeza, Alba, Murungi, Herbert, Murungu, Dorothy, Musarurwa, Sandra, Musiime, Victor, Musiime, Alex V., Musisi, Maria, Musoke, Philippa, Musoke Nakirya, Barbara, Musoro, Godfrey, Musumba, Sharif, Mustafa, Sobia, Mutsai, Shirley, Mwesigwa Rubondo, Phyllis, Naabalamba, Mariam, Nagawa, Immaculate, Naidoo, Allemah, Nakabuye, Shamim, Nakabuye, Sarah, Nakalanzi, Sarah, Nalubwama, Justine, Nalugo, Annet, Nalusiba, Stella, Namajja, Clementine, Namanda, Sylvia, Namayanja, Paula, Nambi, Esther, Namuddu, Rachael Kikabi, Namukwaya, Stella, Namuli, Florence, Namusanje, Josephine, Namwanje, Rosemary, Nanan-kanjee, Anusha, Nanduudu, Annet, Nankunda, Charity, Baddokwaya, Joanita Nankya, Nannungi, Maria, Nansamba, Winnie, Nanthapisal, Kesdao, Nanyonjo, Juliet, Na-Rajsima, Sathaporn, Nasaazi, Claire, Nascimento, Helena, Nastouli, Eleni, Songtaweesin, Wipaporn Natalie, Nathoo, Kusum, Natuhurira, Ian, Nazzinda, Rashidah, Ncgaba, Thabisa, Ndigendawani, Milly, Ndlovu, Makhosonke, Nentsa, Georgina, Ngampiyaskul, Chaiwat, Ngcobo, Ntombenhle, Ngo Giang Huong, Nicole, Ngwaru, Pia, Nhema, Ruth, Ninsiima, Emily, Ninsiima, Gloria, Nkalo Phiri, Misheck, Noguera Julian, Antoni, Nolan, Monica, Noppakaorattanamanee, Thornthun, Nsibuka Kisekka, Muzamil, Nsirim, Eniola, Nundlal, Rashina, Nunes, Rosita, Nyantsa, Lungile, Nyati, Mandisa, O'Riordan, Sean, Ocitti Labeja, Paul, Odoch, Denis, Oguntimehin, Rachel, Ojok, Martin, Onen, Geoffrey, Orange, Wilma, Ounchanum, Pradthana, Ouma, Benson, Padrao, Andreia, Pako, Deborah, Parker, Anna, Pasko-Szcech, Malgorzata, Patel, Reena, Peongjakta, Rukchanok, Petpranee, Turian, Phillips, Tasmin, Philps, Jackie, Picault, Laura, Pieterse, Sonja, Pinheiro, Helena, Pongprapass, Supawadee, Pozniak, Anton, Prendergast, Andrew, Prieto Tato, Luis, Puangmalai, Patcharee, Puthanakit, Thanyawee, Rakgokong, Modiehi, Ramos, Helena, Ramsagar, Nastassja, Rau, Cornelius, Riault, Yoann, Rojo Conejo, Pablo, Clark, Basiimwa Roy, Rubanga, Eddie, Rubinga, Baker, Ruklao, Chutima, Runarassamee, Pattira, Saenjum, Chalermpong, Saewtrakool, Chayakorn, Saidi, Yacine, Sainz Costa, Talia, Saisaengjan, Chutima, Sakwa, Rebecca, Sarfati, Tatiana, Sbisi, Noshalaza, Scheppers, Dihedile, Schultze-Strasser, Stephan, Schulze-Sturm, Ulf, Scott, Karen, Seeley, Janet, Serunjogi, Robert, Sewnarain, Leora, Shakeshaft, Clare, Sidhoo, Subashinie, Shibemba, Mercy, Shingadia, Delane, Singh, Sheleika, Sirirungsi, Wasna, Sithebe, Sibongile, Smit, Theresa, Smith, Kurt, Smuts, Marlize, Spyer, Moira, Sripaoraya, Worathip, Srirompotong, Ussanee, Srisuk, Warunee, Ssenyonga, Mark, Sudsaard, Patamawadee, Sukrakanchana, Praornsuda, Tearsansern, Pathanee, Teixeira, Carla, Than-in-at, Kanchana, Thapwai, Thitiwat, Thaweesombat, Yupawan, Thewsoongnoen, Jutarat, Thiébaut, Rodolphe, Thomason, Margaret, Thrasyvoulou, Laura, Thungkham, Khanungnit, Tikabibamu, Judith, Tinago, Gloria, Trairat, Ketmookda, Tudor-Williams, Gareth, Tukamushaba, Mercy, Tukwasibwe, Deogratiuos, Tumusiime, Julius, Tuna, Joana, Turkova, Anna, Turner, Rebecca, Udomvised, Arttasid, Vadee, Aasia, Van Huyssteen, Hesti, Van Looy, Nadine, Variava, Ebrahim, Vaughan-Gordon, Yvonne, Vecchia, Giulio, Violari, Avy, Vowden, Richard, Waalewijn, Hylke, Wampamba, Rebecca, Welch, Steve, Weller, Ian, Weza, Sibusisiwe, White, Ellen, White, Ian, Widuch, Kaja, Wilkes, Helen, Wimonklang, Sookpanee, Wynne, Ben, Yingyong, Pacharaporn, Nakawungu, Zaam Zinda, Zuidewind, Peter, Mujuru, Hilda A, Kityo, Cissy M, Turner, Rebecca M, Ali, Shabinah, Nathoo, Kusum J, Mosala, Modehei, Kekitiinwa, Adeodata R, Rojo, Pablo, and Gibb, Diana M

Published
2022
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26. Inhaled ciclesonide for outpatient treatment of COVID-19 in adults at risk of adverse outcomes: a randomised controlled trial (COVERAGE)

Author

Duvignaud, Alexandre, Lhomme, Edouard, Onaisi, Racha, Sitta, Rémi, Gelley, Ambre, Chastang, Julie, Piroth, Lionel, Binquet, Christine, Dupouy, Julie, Makinson, Alain, Lefèvre, Benjamin, Naccache, Jean-Marc, Roussillon, Caroline, Landman, Roland, Wallet, Cédrick, Karcher, Sophie, Journot, Valérie, Nguyen, Duc, Pistone, Thierry, Bouchet, Stéphane, Lafon, Marie-Edith, Molimard, Mathieu, Thiébaut, Rodolphe, de Lamballerie, Xavier, Joseph, Jean-Philippe, Richert, Laura, Saint-Lary, Olivier, Djabarouti, Sarah, Wittkop, Linda, Anglaret, Xavier, and Malvy, Denis

Published
2022
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27. Incidence of cytomegalovirus infection in seropositive kidney transplant recipients treated with everolimus: A randomized, open-label, multicenter phase 4 trial

Author

Kaminski, Hannah, Kamar, Nassim, Thaunat, Olivier, Bouvier, Nicolas, Caillard, Sophie, Garrigue, Isabelle, Anglicheau, Dany, Rérolle, Jean-Philippe, Le Meur, Yannick, Durrbach, Antoine, Bachelet, Thomas, Savel, Hélène, Coueron, Roxane, Visentin, Jonathan, Del Bello, Arnaud, Pellegrin, Isabelle, Déchanet-Merville, Julie, Merville, Pierre, Thiébaut, Rodolphe, and Couzi, Lionel

Published
2022
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29. A 4-days-on and 3-days-off maintenance treatment strategy for adults with HIV-1 (ANRS 170 QUATUOR): a randomised, open-label, multicentre, parallel, non-inferiority trial

Author

LAMAURY, Isabelle, BANI-SADR, Firouzé, FORCE, Gilles, CHABROL, Amélie, CABY, Fabienne, PATEY, Olivier, FRESARD, Anne, GAGNEUX-BRUNON, Amandine, CHIROUZE, Catherine, DUVIVIER, Claudine, LOURENCO, Jérémie, TOLSMA, Violaine, JANSSEN, Cécile, LEROLLE, Nathalie, CATALAN, Pilartxo, RAMI, Agathe, DE PONTHAUD, Lucile, PICHANCOURT, Gilles, NASRI, Safa, LANDOWSKI, Stéphanie, BOTTERO, Julie, MFUTILA KAYKAY, Flory, PIALOUX, Gilles, BOUCHAUD, Olivier, ABGRALL, Sophie, GATEY, Caroline, WEISS, Laurence, PAVIE, Juliette, SALMON-CERON, Dominique, ZUCMAN, David, LELIEVRE, Jean-Daniel, PALICH, Romain, SIMON, Anne, MEYOHAS, Marie-Caroline, GRAS, Julien, CABIE, André, PIRCHER, Mathilde, MORLAT, Philippe, HESSAMFAR, Mojgam, NEAU, Didier, CAZENAVE, Charles, GENET, Claire, FAUCHER, Jean-François, MAKHLOUFI, Djamila, BOIBIEUX, André, BREGIGEON-RONOT, Sylvie, LAROCHE, Hélène, SAUTEREAU, Aurélie, REYNES, Jacques, MAKINSON, Alain, RAFFI, François, BOLLENGIER-STRAGIER, Olivier, NAQVI, Alissa, CUA, Eric, ROSENTHAL, Eric, ARVIEUX, Cédrick, BUZELE, Rodolphe, REY, David, BATARD, Marie-Laure, BERNARD, Louis, DELOBEL, Pierre, PIFFAUT, Marie, VERDON, Renaud, PIROTH, Lionel, BLOT, Mathieu, LECLERCQ, Pascale, SIGNORI-SCHMUCK, Anne, HULEUX, Thomas, MEYBECK, Agnès, MAY, Thierry, MIAILHES, Patrick, PERPOINT, Thomas, GREDER-BELAN, Alix, ELHARRAR, Brigitte, KHUONG, Marie-Aude, POUPARD, Marie, BLUM, Laurent, MICHAU, Christophe, PRAZUCK, Thierry, PHILIBERT, Patrick, SLAMA, Laurence, HIKOMBO, Hitoto, DARASTEANU, Iuliana, GIRARD, Pierre-Marie, ALVAREZ, Jean-Claude, MATHEZ, Dominique, DE TRUCHIS, Pierre, LANDMAN, Roland, MEYNARD, Jean-Luc, MORAND-JOUBERT, Laurence, LAMBERT, Sidonie, LE DU, Damien, PERRONNE, Christian, ASSOUMOU, Lambert, COSTAGLIOLA, Dominique, MELCHIOR, Jean-Claude, DURACINSKI, Martin, PETROV-SANCHEZ, Ventzislava, AMAT, Karine, BENALYCHERIF, Aïda, SYLLA, Babacar, GELLEY, Ambre, GIBOWSKI, Séverine, LE MEUT, Guillaume, THIEBAUT, Rodolphe, CLUMECK, Nathan, LECLERCQ, Vincent, CECCHERINI-SILBERSTEIN, Francesca, DECOSTER, Laurent, Landman, Roland, de Truchis, Pierre, Assoumou, Lambert, Lambert, Sidonie, Bellet, Jonathan, Amat, Karine, Lefebvre, Bénédicte, Allavena, Clotilde, Katlama, Christine, Yazdanpanah, Yazdan, Molina, Jean-Michel, Petrov-Sanchez, Ventzislava, Gibowski, Séverine, Alvarez, Jean-Claude, Leibowitch, Jacques, Capeau, Jacqueline, Fellahi, Soraya, Duracinsky, Martin, Morand-Joubert, Laurence, Costagliola, Dominique, and Girard, Pierre-Marie

Published
2022
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30. Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry Data

Author

Hejblum, Boris P., Alkhassim, Chariff, Gottardo, Raphael, Caron, François, and Thiébaut, Rodolphe

Subjects
Statistics - Machine Learning, 62H30, 62P10, 62L12
Abstract

Flow cytometry is a high-throughput technology used to quantify multiple surface and intracellular markers at the level of a single cell. This enables to identify cell sub-types, and to determine their relative proportions. Improvements of this technology allow to describe millions of individual cells from a blood sample using multiple markers. This results in high-dimensional datasets, whose manual analysis is highly time-consuming and poorly reproducible. While several methods have been developed to perform automatic recognition of cell populations, most of them treat and analyze each sample independently. However, in practice, individual samples are rarely independent (e.g. longitudinal studies). Here, we propose to use a Bayesian nonparametric approach with Dirichlet process mixture (DPM) of multivariate skew $t$-distributions to perform model based clustering of flow-cytometry data. DPM models directly estimate the number of cell populations from the data, avoiding model selection issues, and skew $t$-distributions provides robustness to outliers and non-elliptical shape of cell populations. To accommodate repeated measurements, we propose a sequential strategy relying on a parametric approximation of the posterior. We illustrate the good performance of our method on simulated data, on an experimental benchmark dataset, and on new longitudinal data from the DALIA-1 trial which evaluates a therapeutic vaccine against HIV. On the benchmark dataset, the sequential strategy outperforms all other methods evaluated, and similarly, leads to improved performance on the DALIA-1 data. We have made the method available for the community in the R package NPflow., Comment: 39 pages, 11 figures

Published
2017
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31. CD177, a specific marker of neutrophil activation, is associated with coronavirus disease 2019 severity and death

Author

Lévy, Yves, Wiedemann, Aurélie, Hejblum, Boris P., Durand, Mélany, Lefebvre, Cécile, Surénaud, Mathieu, Lacabaratz, Christine, Perreau, Matthieu, Foucat, Emile, Déchenaud, Marie, Tisserand, Pascaline, Blengio, Fabiola, Hivert, Benjamin, Gauthier, Marine, Cervantes-Gonzalez, Minerva, Bachelet, Delphine, Laouénan, Cédric, Bouadma, Lila, Timsit, Jean-François, Yazdanpanah, Yazdan, Pantaleo, Giuseppe, Hocini, Hakim, and Thiébaut, Rodolphe

Published
2021
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32. Modeling CD4+ T cells dynamics in HIV-infected patients receiving repeated cycles of exogenous Interleukin 7

Author

Jarne, Ana, Commenges, Daniel, Prague, Mélanie, Levy, Yves, and Thiébaut, Rodolphe

Subjects
Statistics - Applications
Abstract

Combination Antiretroviral Therapy (cART) succeeds to control viral replication in most HIV infected patients. This is normally followed by a reconstitution of the CD4$^+$ T cells pool; however, this does not happen for a substantial proportion of patients. For these patients, an immunotherapy based on injections of Interleukin 7 (IL-7) has been recently proposed as a co-adjutant treatment in the hope of obtaining long-term reconstitution of the T cells pool. Several questions arise as to the long-term efficiency of this treatment and the best protocol to apply. We develop a model based on a system of ordinary differential equations and a statistical model of variability and measurement. We can estimate key parameters of this model using the data from INSPIRE, INSPIRE 2 $\&$ INSPIRE 3 trials. In all three studies, cycles of three injections have been administered; in the last two studies, for the first time, repeated cycles of exogenous IL-7 have been administered. Our aim was to estimate the possible different effects of successive injections in a cycle, to estimate the effect of repeated cycles and to assess different protocols. The use of dynamical models together with our complex statistical approach allow us to analyze major biological questions. We found a strong effect of IL-7 injections on the proliferation rate; however, the effect of the third injection of the cycle appears to be much weaker than the first ones. Also, despite a slightly weaker effect of repeated cycles with respect to the initial one, our simulations show the ability of this treatment of maintaining adequate CD4$^+$ T cells count for years. We were also able to compare different protocols, showing that cycles of two injections should be sufficient in most cases. %Finally, we also explore the possibility of adaptive protocols.

Published
2016

33. Safety and immunogenicity of a two-dose heterologous Ad26.ZEBOV and MVA-BN-Filo Ebola vaccine regimen in adults in Europe (EBOVAC2): a randomised, observer-blind, participant-blind, placebo-controlled, phase 2 trial

Author

McShane, Christopher, Callendret, Benoit, Dincq, Stephanie, Ferrault, Camille, Chai, Siew Pin, Gyselen, Maire Paule, van Looveren, Marleen, van Ballert, Sylvia, de Cnodder, Tinne, Roza, Len, Forcheh, Chiara, Stevens, Kate, Mastrandrea, Carmela, de Ridder, Sanne, Gundluru, Rachana, Swales, Nathalie, Errijegers, Vanessa, Willems, Wouter, Roorda, Veronika, Orzabal, Nicola, Assenberg, Magdalena, Vialatte, Karine, Remblier, Frédéric, Porcar, Elodie, Ottavi, Anton, Destandau, Eugénie, Schwimmer, Christine, Moinot, Laetitia, Wallet, Cédrick, Allais, Florence, Savel, Hélène, Nedjaai, Naouel, Maugard, Anaïs, Lenzi, Nehza, Loulergue, Pierre, Bahuaud, Mathilde, Lainé, Fabrice, Laviolle, Bruno, Boissel, Nolwenn, Thébault, Elise, Vallée, David, Nicolas, Jean-François, Gilbert, Sophie, Dahel, Karima, Sagorny, Karen, Lucht, Frédéric, Paul, Stéphane, Haccourt Chanavat, Alice, Charra, Florent, Mutter, Catherine, Lambour, Monique, Muller, Caroline, Hutt-Clauss, Anne, Aranda, Olivia, Bernard, Louis, Gissot, Valérie, Hallouin-Bernard, Marie-Charlotte, Goudeau, Alain, Suzzoni, Steve, Auostin, Eva, Brick, Lysiane, Lopez-Zaragoza, Jose-Luis, Melic, Giovanna, Carvalho, Murial, Chesnel, Chrystel, Hocini, Hakim, Wiedemann, Aurélie, Hanot, Laurent, Rieux, Véronique, Puri, Adeep, Adeloye, Temitope, Boyce, Malcolm, Dennison, Jeremy, Loewenstein, Inge, Sahgal, Omar, van den Berg, Frans, Calvert, Wendy, Faldon, Mary, McClain, Bruce, Newell, Marie-Lousie, Molenberghs, Geert, Pollard, Andrew J, Launay, Odile, Lelievre, Jean-Daniel, Lacabaratz, Christine, Grande, Sophie, Goldstein, Neil, Robinson, Cynthia, Gaddah, Auguste, Bockstal, Viki, Wiedemann, Aurelie, Leyssen, Maarten, Luhn, Kerstin, Richert, Laura, Bétard, Christine, Gibani, Malick M, Clutterbuck, Elizabeth A, Snape, Matthew D, Levy, Yves, Douoguih, Macaya, and Thiebaut, Rodolphe

Published
2021
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34. Prospective Evaluation of the First Option, Second-Line Therapy in Childhood Chronic Immune Thrombocytopenia: Splenectomy or Immunomodulation

Author

Ducassou, Stéphane, Fernandes, Helder, Savel, Hélène, Bertrand, Yves, Leblanc, Thierry, Abou Chahla, Wadih, Pasquet, Marlène, Leverger, Guy, Barlogis, Vincent, Thomas, Caroline, Bayart, Sophie, Pellier, Isabelle, Armari-Alla, Corinne, Guitton, Corinne, Cheikh, Nathalie, Kherfellah, Djamel, Vassal, Gilles, Thiébaut, Rodolphe, Laghouati, Salim, and Aladjidi, Nathalie

Published
2021
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35. Universal test and treat and the HIV epidemic in rural South Africa: a phase 4, open-label, community cluster randomised trial

Author

Bärnighausen, Till, Herbst, Kobus, Iwuji, Collins, Makowa, Thembisa, Naidu, Kevi, Newell, Marie-Louise, Okesola, Nonhlanhla, de Oliveira, Tulio, Pillay, Deenan, Rochat, Tamsen, Tanser, Frank, Viljoen, Johannes, Zuma, Thembelihle, McGrath, Nuala, Balestre, Eric, Dabis, François, Karcher, Sophie, Orne-Gliemann, Joanna, Plazy, Melanie, Prague, Mélanie, Thiébaut, Rodolphe, Tiendrebeogo, Thierry, Boyer, Sylvie, Donfouet, Hermann, Gosset, Andrea, March, Laura, Protopopescu, Camelia, Spire, Bruno, Calmy, Alexandra, Larmarange, Joseph, Inghels, Maxime, Diallo, Hassimiou, Calvez, Vincent, Derache, Anne, Marcelin, Anne-Geneviève, Dray-Spira, Rosemary, Lert, France, El Farouki, Kamal, Lessells, Richard, Freedberg, Kenneth, Imrie, John, Chaix, Marie-Laure, Newell, Colin, Hontelez, Jan, Bazin, Brigitte, Rekacewicz, Claire, Iwuji, Collins C, Thiebaut, Rodolphe, Dreyer, Jaco, and De Oliveira, Tulio

Published
2018
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36. PRIMVAC vaccine adjuvanted with Alhydrogel or GLA-SE to prevent placental malaria: a first-in-human, randomised, double-blind, placebo-controlled study

Author

Sirima, Sodiomon B, Richert, Laura, Chêne, Arnaud, Konate, Amadou T, Campion, Cécilia, Dechavanne, Sébastien, Semblat, Jean-Philippe, Benhamouda, Nadine, Bahuaud, Mathilde, Loulergue, Pierre, Ouédraogo, Alphonse, Nébié, Issa, Kabore, Moïse, Kargougou, Désiré, Barry, Aïssata, Ouattara, San Maurice, Boilet, Valérie, Allais, Florence, Roguet, Gwenaelle, Havelange, Nicolas, Lopez-Perez, Elvira, Kuppers, Alexis, Konaté, Eleine, Roussillon, Caroline, Kanté, Myriam, Belarbi, Linda, Diarra, Amidou, Henry, Noelie, Soulama, Issiaka, Ouédraogo, Amidou, Esperou, Helene, Leroy, Odile, Batteux, Frederic, Tartour, Eric, Viebig, Nicola K, Thiebaut, Rodolphe, Launay, Odile, and Gamain, Benoît

Published
2020
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38. Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data

Author

Altman, Matthew C., Rinchai, Darawan, Baldwin, Nicole, Toufiq, Mohammed, Whalen, Elizabeth, Garand, Mathieu, Syed Ahamed Kabeer, Basirudeen, Alfaki, Mohamed, Presnell, Scott R., Khaenam, Prasong, Ayllón-Benítez, Aaron, Mougin, Fleur, Thébault, Patricia, Chiche, Laurent, Jourde-Chiche, Noemie, Phillips, J. Theodore, Klintmalm, Goran, O’Garra, Anne, Berry, Matthew, Bloom, Chloe, Wilkinson, Robert J., Graham, Christine M., Lipman, Marc, Lertmemongkolchai, Ganjana, Bedognetti, Davide, Thiebaut, Rodolphe, Kheradmand, Farrah, Mejias, Asuncion, Ramilo, Octavio, Palucka, Karolina, Pascual, Virginia, Banchereau, Jacques, and Chaussabel, Damien

Published
2021
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39. Temporal trends of population viral suppression in the context of Universal Test and Treat: the ANRS 12249 TasP trial in rural South Africa

Author

Larmarange, Joseph, Diallo, Mamadou H., Mcgrath, Nuala, Iwuji, Collins, Plazy, Melanie, Thiebaut, Rodolphe, Tanser, Frank, Barnighausen, Till, Orne-Gliemann, Joanna, Pillay, Deenan, and Dabis, Francois

Subjects
Highly active antiretroviral therapy -- Analysis, HIV -- Analysis, Antiretroviral agents -- Analysis, Health
Abstract

Introduction: The universal test-and-treat (UTT) strategy aims to maximize population viral suppression (PVS), that is, the proportion of all people living with HIV (PLHIV) on antiretroviral treatment (ART) and virally suppressed, with the goal of reducing HIV transmission at the population level. This article explores the extent to which temporal changes in PVS explain the observed lack of association between universal treatment and cumulative HIV incidence seen in the ANRS 12249 TasP trial conducted in rural South Africa. Methods: The TasP cluster-randomized trial (2012 to 2016) implemented six-monthly repeat home-based HIV counselling and testing (RHBCT) and referral of PLHIV to local HIV clinics in 2 x 11 clusters opened sequentially. ART was initiated according to national guidelines in control clusters and regardless of CD4 count in intervention clusters. We measured residency status, HIV status, and HIV care status for each participant on a daily basis. PVS was computed per cluster among all resident PLHIV ([greater than or equal to]16, including those not in care) at cluster opening and daily thereafter. We used a mixed linear model to explore time patterns in PVS, adjusting for sociodemographic changes at the cluster level. Results: 8563 PLHIV were followed. During the course of the trial, PVS increased significantly in both arms (23.5% to 46.2% in intervention, +22.8, p < 0.001; 26.0% to 44.6% in control, +18.6, p < 0.001). That increase was similar in both arms (p = 0.514). In the final adjusted model, PVS increase was most associated with increased RHBCT and the implementation of oca trial clinics (measured by time since cluster opening). Contextual changes (measured by calendar time) also contributed slightly. The effect of universal ART (trial arm) was positive but limited. Conclusions: PVS was improved significantly but similarly in both trial arms, explaining partly the null effect observed in terms of cumulative HIV incidence between arms. The PVS gains due to changes in ART-initiation guidelines alone are relatively small compared to gains obtained by strategies to maximize testing and linkage to care. The achievement of the 90-90-90 targets will not be met if the operational and implementational challenges limiting access to care and treatment, often context-specific, are not properly addressed. Clinical trial number: NCT01509508 (clinicalTrials.gov)/DOH-27-0512-3974 (South African National Clinical Trials Register). Keywords: HIV; antiretroviral therapy; sustained viral suppression; retention in care; population health; South Africa, 1 | INTRODUCTION Antiretroviral treatment (ART), when taken early has several benefits, both in terms of morbidity and mortality [1,2] and in terms of reduction in HIV sexual transmission [3]. [...]

Published
2019
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40. Heterogeneous Viral Environment in a HIV Spatial Model

Author

Brauner, Claude-Michel, Jolly, Danaelle, Lorenzi, Luca, and Thiebaut, Rodolphe

Subjects
Mathematics - Dynamical Systems, 35K55
Abstract

We consider the basic model of virus dynamics in the modeling of Human Immunodeficiency Virus (HIV), in a 2D heterogenous environment. It consists of two ODEs for the non-infected and infected $CD_4^+$ $T$ lymphocytes, $T$ and $I$, and a parabolic PDE for the virus $V$. We define a new parameter $\lambda_0$ as an eigenvalue of some Sturm-Liouville problem, which takes the heterogenous reproductive ratio into account. For $\lambda_0<0$ the trivial non-infected solution is the only equilibrium. When $\lambda_0>0$, the former becomes unstable whereas there is only one positive infected equilibrium. Considering the model as a dynamical system, we prove the existence of a universal attractor. Finally, in the case of an alternating structure of viral sources, we define a homogenized limiting environment. The latter justifies the classical approach via ODE systems.

Published
2009

42. Mixed models for longitudinal left-censored repeated measures

Author

Thiébaut, Rodolphe and Jacqmin-Gadda, Hélène

Subjects
Statistics - Applications
Abstract

Longitudinal studies could be complicated by left-censored repeated measures. For example, in Human Immunodeficiency Virus infection, there is a detection limit of the assay used to quantify the plasma viral load. Simple imputation of the limit of the detection or of half of this limit for left-censored measures biases estimations and their standard errors. In this paper, we review two likelihood-based methods proposed to handle left-censoring of the outcome in linear mixed model. We show how to fit these models using SAS Proc NLMIXED and we compare this tool with other programs. Indications and limitations of the programs are discussed and an example in the field of HIV infection is shown.

Published
2007
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43. Bivariate linear mixed models using SAS proc MIXED

Author

Thiébaut, Rodolphe, Jacqmin-Gadda, Hélène, Chêne, Geneviève, Leport, Catherine, and Commenges, Daniel

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers. In this paper, we present a bivariate linear mixed model including random effects or first-order auto-regressive process and independent measurement error for both markers. Codes and tricks to fit these models using SAS Proc MIXED are provided. Limitations of this program are discussed and an example in the field of HIV infection is shown. Despite some limitations, SAS Proc MIXED is a useful tool that may be easily extendable to multivariate response in longitudinal studies.

Published
2007

46. Innate gene signature distinguishes humoral versus cytotoxic responses to influenza vaccination

Author

Gonsalves, Elena, Bonduelle, Olivia, Soria, Angele, Loulergue, Pierre, Rousseau, Alexandra, Cachanado, Marine, Bonnabau, Henri, Thiebaut, Rodolphe, Tchitchek, Nicolas, Behillil, Sylvie, van der Werf, Sylvie, Vogt, Annika, Simon, Tabassome, Launay, Odile, and Combatiere, Behazine

Subjects
Clinical trials -- Analysis, Vaccines -- Usage -- Safety and security measures, Medical research, Immune response -- Research, Biological markers -- Research, Influenza vaccines, Genetic research, T cells, Genes, Influenza, Lectins, Vaccination, Regression analysis, Health care industry
Abstract

BACKGROUND. Systems vaccinology allows cutting-edge analysis of innate biomarkers of vaccine efficacy. We explored a strategy to shape the adaptive immune response by targeting innate immune cells through novel immunization routes. METHODS. This randomized phase I/II clinical study (n = 60 healthy subjects aged 18-45 years old) used transcriptomic analysis to discover early biomarkers of immune response quality after transcutaneous (t.c.), intradermal (i.d.), and intramuscular (i.m.) administration of a trivalent influenza vaccine (TIV, season 2012-2013, 1:1:1 ratio). Safety and immunogenicity (hemagglutinin inhibition [HI], microneutralization [MN] antibodies, and CD4+ and CD8+ effector T cells) were measured at baseline day 0 (d0) and at d21. Blood transcriptome was analyzed at d0 and d1. RESULTS. TIV-specific CD8+ granzyme B+ (GRZ) T cells appeared in more individuals immunized by the t.c. and i.d. routes, whereas immunization by the i.d. and i.m. routes prompted high levels of HI antibody titers and MN against A/H1N1 and A/H3N2 influenza viral strains. The early innate gene signature anticipated immunological outcome by discriminating 2 clusters of individuals with either distinct humoral or CD8 cytotoxic responses. Several pathways explained this dichotomy and confirmed that 9 genes and the serum level of CXCL10 were correlated with either TIV-specific cytotoxic CD8+GRZ+ T cell or antibody responses. A logistic regression analysis demonstrated that these 9 genes and the serum levels of CXCL10 at d1/d0 best predicted TIV-specific CD8+GRZ+ T cell and antibody responses at d21. CONCLUSION. This study provides new insight into the impact of immunization routes and innate signature in the quality of adaptive immune responses. TRIAL REGISTRATION. This study has been registered at ClinicalTrials.gov (NCT01707602). FUNDING. This work was supported by grants from the French Ministry of Health PHRCN 2012--RCT 12061, INSERM-DGOS, the Fondation pour la Recherche Medicale, and the Societe Frangaise de Dermatologie (to AS). These funding sources had no direct role in any aspect of the research or article., Introduction Because innate immunity plays an important role in adaptive immune response to vaccination, researchers have been exploring novel strategies to shape the adaptive immune response by targeting innate immune [...]

Published
2019
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48. Universal definition of loss to follow-up in HIV treatment programs: a statistical analysis of 111 facilities in Africa, Asia, and Latin America.

Author

Chi, Benjamin H, Yiannoutsos, Constantin T, Westfall, Andrew O, Newman, Jamie E, Zhou, Jialun, Cesar, Carina, Brinkhof, Martin WG, Mwango, Albert, Balestre, Eric, Carriquiry, Gabriela, Sirisanthana, Thira, Mukumbi, Henri, Martin, Jeffrey N, Grimsrud, Anna, Bacon, Melanie, Thiebaut, Rodolphe, and International Epidemiologic Databases to Evaluate AIDS Collaboration

Subjects
International Epidemiologic Databases to Evaluate AIDS Collaboration, Humans, HIV, HIV Infections, Antiretroviral Therapy, Highly Active, Cohort Studies, Follow-Up Studies, Patient Compliance, Adolescent, Adult, Delivery of Health Care, Africa, Latin America, Asia, Terminology as Topic, Lost to Follow-Up, Antiretroviral Therapy, Highly Active, General & Internal Medicine, Medical and Health Sciences
Abstract

BackgroundAlthough patient attrition is recognized as a threat to the long-term success of antiretroviral therapy programs worldwide, there is no universal definition for classifying patients as lost to follow-up (LTFU). We analyzed data from health facilities across Africa, Asia, and Latin America to empirically determine a standard LTFU definition.Methods and findingsAt a set "status classification" date, patients were categorized as either "active" or "LTFU" according to different intervals from time of last clinic encounter. For each threshold, we looked forward 365 d to assess the performance and accuracy of this initial classification. The best-performing definition for LTFU had the lowest proportion of patients misclassified as active or LTFU. Observational data from 111 health facilities-representing 180,718 patients from 19 countries-were included in this study. In the primary analysis, for which data from all facilities were pooled, an interval of 180 d (95% confidence interval [CI]: 173-181 d) since last patient encounter resulted in the fewest misclassifications (7.7%, 95% CI: 7.6%-7.8%). A secondary analysis that gave equal weight to cohorts and to regions generated a similar result (175 d); however, an alternate approach that used inverse weighting for cohorts based on variance and equal weighting for regions produced a slightly lower summary measure (150 d). When examined at the facility level, the best-performing definition varied from 58 to 383 d (mean=150 d), but when a standard definition of 180 d was applied to each facility, only slight increases in misclassification (mean=1.2%, 95% CI: 1.0%-1.5%) were observed. Using this definition, the proportion of patients classified as LTFU by facility ranged from 3.1% to 45.1% (mean=19.9%, 95% CI: 19.1%-21.7%).ConclusionsBased on this evaluation, we recommend the adoption of ≥180 d since the last clinic visit as a standard LTFU definition. Such standardization is an important step to understanding the reasons that underlie patient attrition and establishing more reliable and comparable program evaluation worldwide. Please see later in the article for the Editors' Summary.

Published
2011

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