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Inference and Validation of Prognostic Marker for Correlated Survival Data with Application to Cancer Alessandra Meddis

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Inference and Validation of Prognostic Marker for Correlated Survival Data with Application to Cancer Alessandra Meddis Inference and validation of prognostic marker for correlated survival data with application to cancer Alessandra Meddis To cite this version: Alessandra Meddis. Inference and validation of prognostic marker for correlated survival data with application to cancer. Cancer. Université Paris-Saclay, 2020. English. NNT : 2020UPASR005. tel-03106118 HAL Id: tel-03106118 https://tel.archives-ouvertes.fr/tel-03106118 Submitted on 11 Jan 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Inference and validation of prognostic marker for correlated survival data with application to cancer Thèse de doctorat de l’université Paris-Saclay École doctorale n◦ 570, École doctorale de Santé Publique (EDSP) Spécialité de doctorat: Santé publique - Biostatistiques Unité de recherche: U900, INSERM, PSL Research University Institut Curie Référent: université de Versailles -Saint-Quentin-en-Yvelines Thèse présentée et soutenue à Paris, le 23/10/2020, par Alessandra MEDDIS Composition du jury: Estelle Kuhn Présidente Directrice de Recherche, INRAE (UNITÉ Ma- IAGE) Helene Jacqmin-Gadda Rapportrice & Examinatrice Directrice de Recherche, University of Bor- deaux, ISPED, Bordeaux Population Health Research Center, UMR1219 (INSERM) Hein Putter Rapporteur & Examinateur Professor, Leiden University Medical Cen- ter (Department of Biometrics) Yohann Foucher Examinateur Docteur, Institut de Recherche en Santé 2 (Inserm UMR 1246 - SPHERE) Aurélien Latouche Directeur de thése Professeur, Institut Curie (PSL Research University, INSERM, U900) NNT : 2020UPASR005 Thèse de doctorat de Thèse To my Family, who has given to me a lifetime of support and unconditional love. Acknowledgments At the end of this "trip" I have to thank all the people who have supported me along the way. I would like to thank my supervisor, Aur´elienLatouche who has the merit of having introduced me the world of research. I am very grateful for his guidance and encouragement in these years together. It was a pleasure being supervised by someone with a strongly positive attitude always ready with the right advice. Thanks to all the members of the StaMPM team, in particular to Xavier Paoletti for his invaluable help and all the interesting discussions; thanks to my colleagues Bassirou and Jonas, we supported each others during these years, sharing bad and good news. Thank you to the HR department and Caroline for their essential help with the administrative part. My gratitude are also due to the members of the jury for having accepted to be part of this last step before becoming a Doctor. Thank you for the interest showed on my work and for the inestimable feedback. I had the pleasure to collaborate with great researchers during this three years. I would like to thank Paul Blanche for his ideas and his encouragement, his love for research inspired me; thanks to Stephen Cole for the opportunity to work together, for his precious time and his constant enthusiasm. I had also the opportunity to work with the biomarker team in Servier. Thanks for their worm welcome during my visits and, in particular, to Julia Geronimi, her pragmatism has taught me a lot. I would like to thank my friends, the ones that I met along the way in Paris and those that even from a distance there have been. Thanks to Cucchi for his advises, he has always been there during my moments of uncertainty. Thanks to Valentina and Pasquale for their friendship, they have the ability to understand me in every situation. Thanks to Alida, Rosamaria, Giusy and Chiara for all the support and positivity. My deepest thank goes to my family, my parents for their unconditional support in all my choices. They taught me the value of working with ambition and perseverance. Thanks to my first supporter, my sister Mariagrazia, for her love, she is my greatest example of determination. I would also like to thank my "new family": Serge, Isabelle, Victor and Justine. The time spent together made me feel home. Finally, I thank Alex for all the emotional support, for sharing with me ups and downs of these years and thank you for all the happiness you give me everyday. Scientific production Published articles I A. Meddis, A.Latouche, B. Zhou, S. Michiel J. Fine (2020). Meta- analysis of clinical trials with competing time-to-event endpoints. Bio- metrical Journal, 62(3), 712-723. I A. Meddis, P. Blanche, F.C. Bidard, A. Latouche (2020). A covariate- specific time-dependent receiver operating characteristic curve for cor- related survival data. Statistics in Medicine. 2020 Aug 30;39(19):2477- 2489. Articles in preparation I A. Meddis, A.Latouche (2020). Test for informative cluster size with survival data. Related articles I F.C. Bidard, S.Michiels, ..., A. Meddis, P. Blanche, K. D'Hollander, P. Cottu, J. W. Park, S. Loibl, A. Latouche, J. Pierga, K. Pantel (2018). Circulating tumor cells in breast cancer patients treated by neoadjuvant chemotherapy: a meta-analysis. JNCI: Journal of the National Cancer Institute, 110(6), 560-567. I L. Thery , A. Meddis, L. Cabel, C. Proudhon, A. Latouche, J.Y. Pierga, F. C. Bidard (2019). Circulating tumor cells in early breast cancer.JNCI cancer spectrum, 3(2). ii Oral communications and conferences I A. Meddis, P. Blanche, A. Latouche. Semiparametric approach for covariate-specific time dependent ROC curve for correlated survival data Journ´eedes Jeunes chercheurs de la SFB. Paris, France - May 2018 I A. Meddis, P. Blanche, A. Latouche. Semiparametric approach for covariate-specific time dependent ROC curve for correlated survival data. XXIXTH International Biometric Conference. Barcelona, Spain - July 2018. I A. Meddis, A.Latouche, B. Zhou, S. Michiel J. Fine. Meta-analysis of clinical trials with competing time-to-event endpoints. Survival analysis for young researchers. Copenhagen, Denmark- April 2019 I A. Meddis, A. Latouche. Un test pour la taille informative de grappes en presence des donn´eescensur´ees. GDR Statistiques et Sant´e.Paris, France - October 2019 I A. Meddis, A. Latouche. On interpretation of biomarker performance with clustered survival data. Journ´eedes statisticiens. Paris, France - January 2020 I A. Meddis, A. Latouche. On interpretation of biomarker performance with clustered survival data. Seminaire SPHERE. Tours, France - February 2020. I A. Meddis, A. Latouche. Test of informative cluster size with sur- vival data. 41st International Society for Clinical Biostatistics (ISCB). Krakov, Poland - August 2020 iii Resum´een Fran¸cais Les donn´eesen grappes apparaissent lorsque les observations sont collect´ees dans plusieurs groupes diff´erents (grappes) et que les observations de la m^emegrappe sont plus sem- blables que les observations d'une autre grappe en raison de plusieurs facteurs. Ce type de donn´eessont souvent recueillies dans la recherche biom´edicale. La m´eta-analyseest un exemple populaire qui combine observations provenant de plusieurs essais cliniques randomis´esportant sur la m^emequestion m´edicale. Il s'agit d'une proc´eduretypique qui permet une analyse plus pr´ecisedes donn´eesmais soul`eve de nouveaux probl`emes statistique. D'autres exemples sont les donn´eeslongitudinales o`ude multiples mesures sont prises au fil du temps sur chaque individu; ou les donn´eesfamiliales o`ules facteurs g´en´etiquessont partag´espar les membres d'une m^emefamille. L'´etudeIMENEO est une m´eta-analysesur donn´eesindividuelles (IPD) sur des pa- tientes avec un cancer du sein non m´etastatiqueet trait´eespar chimioth´erapien´eoadjuvante. L'objectif principal ´etaitd'´etudier la capacit´epronostique des cellules tumorales cir- culantes (CTC). Ce biomarqueur s'est av´er´e^etrepronostique dans les cas de cancer m´etastatique,et cette m´eta-analysea voulu l'´etudier ´egalement dans le cadre non m´etastatique. Les donn´eesont ´et´erecueillies dans plusieurs centres et il est l´egitime de suspecter diverses sources d'h´et´erog´en´eit´e. Cela pourrait conduire `aune corr´elationentre les observations appartenant `aun m^emegroupe. Il n'´etaitpas clair comment aborder le probl`emede la corr´elationdans l'analyse discriminatoire d'un biomarqueur candidat. Nous avons pro- pos´eune m´ethode pour estimer la courbe ROC d´ependante du temps en traitant les temps d'´ev´enement corr´el´escensur´es`adroite. De plus, un grand nombre de CTC sont d´etect´esdans les cancers m´etastatiqueset, dans un contexte non m´etastatique,ils sont plus fr´equents dans les tumeurs inflammatoires. Les cliniciens ´etaient donc interess´es d'´evaluer la performance des CTC en discriminant les patients qui ont un profil de risque similaire, `asavoir un stade de tumeur similaire. La courbe ROC cumulative/dynamique sp´ecifiqueaux covariables et sa AUC ont ´et´eestim´eesen mod´elisant l’effet des covariables et des biomarqueurs sur le r´esultatpar un mod`elede fragilit´epartag´eeet un mod`elede r´egressionparam´etriquepour la distribution des biomarqueurs conditionn´eepar les co- iv variables. L'introduction de la fragilit´epermet de saisir la corr´elationdes observations `al'int´erieurdes groupes. Dans l'exemple motivant, un mod`elede r´egressionbinomiale n´egative pour les CTC s'est montr´eappropri´e. Une ´etudede simulation a ´et´er´ealis´ee et a montr´eun biais n´egligeablepour l'estimateur propos´eet pour un estimateur non param´etriquefond´esur la pond´eration par la proba- bilit´einverse d'^etrecensur´e(IPCW), tandis qu'un estimateur semi-param´etrique, ignorant la structure en grappe est nettement biais´e. En outre, nous avons illustr´ela robustesse de la m´ethode en cas de mauvaise sp´ecificationde la distribution de la fragilit´e.
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