Imperfect Data : From Mathematical Foundations to Applications in Life Sciences – IMAL

ARC 20/25-107 (2020-2025)

Joint UCLouvain-UNamur ARC project / Sponsored by the Wallonia-Brussels Federation

Promotors:
•   Catherine Legrand (UCLouvain, spoke-person)
•   Anouar El Ghouch (UCLouvain)
•   Philippe Lambert (ULiege / UCLouvain)
•   Eugen Pircalabelu (UCLouvain)
•   Germain Van Bever (UNamur)
•   Ingrid Van Keilegom (KULeuven / UCLouvain).

Summary:
We are witnessing a period of time where the data collection potential has increased exponentially. The cautionary tale of this big data era is that large amounts of data do not necessarily contribute to an increment in our knowledge about the underlying phenomenon. One of the principal reasons for this is that even though one would desire to measure a characteristic for a subject, in many instances one can only get an approximate measurement due to difficulty in obtaining the direct measurement of the desired phenomenon (e.g. tumor size), non-replicability across instances (e.g. blood pressure), necessity to obtain numerous measurements rapidly, sometimes at the cost of accuracy. As a result, many modern observed markers are proxies for the real data because invasive, costly or too complex methods would be required to obtain accurate measurements. In this project we study how one can correct for different types of imperfect data when building statistical models with a focus on applications coming from life sciences. Imperfect data appear in different contexts, structures and models, and this project focuses on two common settings which regularly suffer from imperfect data: data in a regression context with imperfectly measured explanatory variables (Theme 1) and high-dimensional or functional data with measurement error (Theme 2).

Promotors and research team

Anouar El Ghouch

Anouar El Ghouch (anouar.elghouch@uclouvain.be) is Associate Professor of Statistics at UCLouvain and has been working at the Institute of Statistics, Biostatistics and Actuarial Sciences within LIDAM in the Faculty of Science since 2009. Before joining UC Louvain, he held a Postdoc position at the University of Geneva in the Centre for Research in Statistics. He obtained his PhD in Statistics in 2007 from the Institute of Statistics at UCLouvain. His research interests include robust statistics, survival analysis, nonparametric methods and regression analysis. He was associate editor of Computational Statistics & Data Analysis (2018-2021).
Google Scholar: https://scholar.google.dk/citations?user=qhVF9FMAAAAJ&hl=en

Philippe Lambert

Philippe Lambert is full professor of quantitative methods at ULiege and part-time professor of Bayesian biostatistics at UCLouvain (Belgium). He obtained a bachelor's degree in mathematics (1992) from the University of Liège, a master's degree (1994) and a PhD (1995) in biostatistics from the University of Hasselt. He publishes methodological work in various areas of statistical modelling, including Bayesian smoothing methods, survival analysis, interval censoring, Bayesian inference in dynamic models, copula dependence modelling and longitudinal data analysis, with applications in medicine, epidemiology, demography, sociology and actuarial science, resulting in more than 70 publications in international peer reviewed journals. He is Associate Editor of Biostatistics (Oxford U.P.) and Statistical Modelling (Sage).
Google scholar: https://scholar.google.dk/citations?hl=en&user=K5SGdRUAAAAJ
ORCID: https://orcid.org/0000-0002-3670-3328
Personal website: http://www.statsoc.ulg.ac.be

Catherine Legrand

Catherine Legrand (Catherine.legrand@uclouvain.be) is Full Professor of statistics at the UCLouvain (Belgium). After having obtained a Master Degree in Mathematics from the Université Libre de Bruxelles (ULB), she worked for 7 years at the European Organization for Research and Treatment of Cancer (EORTC) and became the primary statistician of the EORTC Lung Cancer Group. She was also a member of the Treatment Outcome Research Group, the Elderly Task Force, and coordinator of the EORTC Independent Data Monitoring Committee. In parallel, she completed a PhD in 2005 at the Center for Statistics, Hasselt University, in the field of survival analysis (frailty models). Early 2006, she started working as biometrician at Merck Sharp & Dohme (MSD) where she was involved in the design and analysis of clinical trials in respiratory diseases. In 2007, she joined the Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA-LIDAM) of the Université catholique de Louvain (UCLouvain). Her area of research includes survival data analysis and design of clinical trials. Along with these professional experiences, she co-authored more than 80 papers in peer-reviewed clinical and statistical journals and published in 2021 a book entitled “Advanced Survival Model” (Chapman and Hall/CRC). She is member of the Scientific Committee of the Belgian Fondation Contre le Cancer and Associate Editor for Biometrics.
Google Scholar: https://scholar.google.dk/citations?hl=en&user=xyQwjrUAAAAJ
Personal website: (under construction)

Eugen Pircalabelu

Eugen Pircalabelu (eugen.pircalabelu@uclouvain.be) is a Lecturer (Chargé de cours) at UCLouvain working at the Institute of Statistics, Biostatistics and Actuarial Sciences within LIDAM at the Faculty of Science since 2018. Prior to moving at UC Louvain, he held a Visiting professor position at Ghent University affiliated with the Department of Applied Mathematics, Computer Science and Statistics within the Faculty of Sciences and a Postdoctoral position at KU Leuven affiliated with the ORSTAT department within the Faculty of Economics and Business. His research focuses on: Models for high-dimensional data, Distributed estimation and inference, Probabilistic graphical models, Social network models, Copula and dependence modelling and Information criteria.
Google scholar: https://scholar.google.dk/citations?hl=en&user=6GUExvgAAAAJ
Personal website: https://perso.uclouvain.be/eugen.pircalabelu/

Germain Van Bever

Germain Van Bever (germain.vanbever@unamur.be) is Associate Professor of Statistics at the Université de Namur. He holds a master (2008) degree in Mathematics and a PhD (2012) in Statistics from the Université libre de Bruxelles. His research focuses on theoretical innovations in the context of nonparametric statistics (including depth-based methods), functional data analysis and high dimensional statistics. He is currently the author of 14 published papers (including contributions in the Annals of Statistics, Bernoulli and the Journal of the American Statistical Association), 5 peer-reviewed chapter of books and two submitted papers. He is currently an Associate Editor for Econometrics and Statistics (Part B: Statistics), the Bulletin of the Belgian Mathematical Society – Simon Stevin and Mathematical Reviews. He is also the President of the FNRS doctoral school in Statistics and Actuarial Sciences, as well as member of the board of the Royal Statistical Society of Belgium and the Belgium Mathematical Society. He obtained several awards, among which the annual prize from the royal Academy of Belgium. He is also an Elected member of the International Statistical Institute (2018).
Google scholar: https://scholar.google.dk/citations?hl=en&user=hnz7L-gAAAAJ
Personal website: https://sites.google.com/site/germainvanbever/home-1

Ingrid Van Keilgom

Ingrid Van Keilegom (Ingrid.vankeilegom@uclouvain.be) is Full Professor of Statistics at the UCLouvain and the KU Leuven. She received a B.S. degree in mathematics (1993) from the Universiteit Antwerpen, and a master in biostatistics and a PhD in statistics (both in 1998) from the Universiteit Hasselt. Her research focuses on the development of new methodology and theory in areas like survival analysis, causal inference, quantile regression, measurement errors, and non- and semiparametric regression, and this resulted in more than 160 publications in international peer-reviewed journals. Ingrid Van Keilegom is currently holder of an ERC Advanced Grant (2016-2022), and has held in the past an ERC Starting Grant (2008-2014). She has been joint editor of the Journal of the Royal Statistical Society–Series B (2012-2015). Currently she is Associate Editor of Annals of Statistics (2018-), Biometrika (2017-), Annual Review of Statistics and Its Application (2016-), Electronic Journal of Statistics (2018-), and has been Associate Editor of several other journals in the past. She is fellow of the American Statistical Association (2013) and of the Institute of Mathematical Statistics (2008).
Google Scholar: https://scholar.google.dk/citations?hl=en&user=6Sb63foAAAAJ
ORCID: http://orcid.org/0000-0001-8827-7642
Personal website: https://www.kuleuven.be/wieiswie/en/person/00062045

Researchers involved in the project

Ensiyeh Nezakati, PhD Student, 2019-2023
Hortense Doms, PhD Student, 2020-present
Morine Delhelle, PhD Student, 2020-present
Benjamin Deketelaere, PhD student, 2020-present
Chikeola Ladepko, PhD student, 2020-2023
Quentin Le Coënt, PhD student 2020-2023 and Post-doc researcher 2023-present
Jeon Jeong Min, Post-doc researcher, 50%, 2021-2022
Oskar Laverny, Post-doc researcher, 2022-present
Lise Leonard, PhD Student, 2022-current
Hugo Brunet, PhD Student, 2023-present

Research objectives and summary of progress

Still to be done

Research activities

Publications related to this ARC:

Books, as author, co-author or editor

Cure Rate Models. De Backer M, Legrand C. Invited book chapter in: Handbook of Generalized Pairwise Comparisons: Methods for Patient-Centric Analysis (1st ed.). Buyse, M., Verbeeck, J., Saad, E.D., Backer, M.D., Deltuvaite-Thomas, V., & Molenberghs, G. (Eds.). Chapman and Hall/CRC Press, 2025.
Van Keilegom, I. (2024). Dependent censoring based on copulas. In J. Ansari, S. Fuchs, W. Trutschnig, M. A. Lubiano, M.A. Gil, P. Grzegorzewski, & O. Hryniewicz (Eds.), Combining, modelling and analyzing imprecision, randomness and dependence (pp. 526-531, Vol. 1458). Springer Nature Switzerland.
Legrand, C. and Bertrand, A. Cure Models in Cancer Clinical trials. In: Halabi, S. and Michiels, S. (eds), Textbook of Clinical Trials in Oncology. A Statistical Perspective. First Edition. Chapman and Hall/CRC, Boca Raton. 2020.
Legrand, C. Advanced Survival Models. First Edition. Chapman and Hall/CRC, Boca Raton. 2021.
Bucher, A., El Ghouch, A. and Van Keilegom, I. Single-index quantile regression models for censored data. In: Daouia, A. and Ruiz-Gazen, A. (eds.), Advances in Contemporary Statistics and Econometrics, Springer, p. 177-196.
Delaigle, A. and Van Keilegom, I. Deconvolution with unknown error distri- bution. In: Yi, G., Delaigle, A. and Gustafson, P. (eds.), Handbook on Measurement Error Models, Chapman and Hall/CRC, 2021, Chapter 12, p. 245-270.
Conde-Amboage, M., Van Keilegom, I. and Gonzalez-Manteiga, W. Application of quantile regression models for biomedical data. In: Larriba, Y. (eds.), Statistical Methods at the Forefront of Biomedical Advances, Springer, 83-113.

Articles published in peer-reviewed journals

2025

Le Coent Q, , Legrand C, Dignam JJ, Rondeau V. (2025). Validation of a longitudinal marker as a surrogate using mediation analysis and joint modeling: evolution of the PSA as a surrogate of the Disease-Free Survival. Biometrical journal. 2025; 67(4):e70064.
Soetewey A, Legrand C, Denuit M, Silversmit G. (2025). Semi-markov modeling for disease incidence risk and duration. Biostatistics & Epidemiology. 2025; 9(1).
Soetewey A, Legrand C, Denuit M, Silversmit G. (2025). Right to be forgotten for mortgage insurance issued to cancer survivors: critical assessment and new proposal. European Actuarial Journal. 2025; 15; 15-43
Noh, H. and Van Keilegom, I. (2025). Estimation of bivariate measurement error models with application to the corrupted Pearson correlation. J. Nonpar. Statist. (to appear).
Zong, Y., Liu, Y., Ma, Y. and Van Keilegom, I. (2025). Inference on data with both multiplicative and additive measurement error. Scand. J. Statist. (to appear).
Kreiss, A. and Van Keilegom, I. (2025). Eﬃcient quantile regression under censoring using Laguerre polynomials. Bernoulli (to appear).
Van der Elst, W., Ong, F., Stijven, F., Alonso Abad, A., Van Keilegom, I. Geys, H., Eisele, L. and Molenberghs, G. (2025). Multiple surrogates in the meta-analytic setting for normally distributed endpoints. Statist. Biopharm. Res. (to appear).
Mastromarco, C., Simar, L. and Van Keilegom, I. (2025). Estimating nonparametric conditional frontiers and eﬃciencies: a new approach. Econometrics J. (to appear).
D’Haen, M., Van Keilegom, I. and Verhasselt, A. (2025). Quantile regression under dependent censoring with unknown association. Lifetime Data Anal., 31, 253-299.
Deresa, N.W. and Van Keilegom, I. (2025). Semiparametric transformation models for survival data with dependent censoring. Ann. Instit. Statist. Math., 77, 425-457.
Verhasselt, A., Fl´orez, A.J., Molenberghs, G. and Van Keilegom, I. (2025). Copula-based pairwise estimator for quantile regression with hierarchical missing data. Statist. Modelling, 25, 129-149.
Ong, F., Molenberghs, G., Callegaro, A., Van der Elst, W., Verbeke, G., Stijven, F., Van Keilegom, I. and Alonso Abad, A. (2025). Evaluating hemagglutination inhibition antibody titers as correlate of protection for inﬂuenza: A sensitivity analysis based on information theory and causal inference. J. Global Infectious Diseases, 17, 17-23.
Stijven, F., Molenberghs, G., Van Keilegom, I., Van der Elst, W. and Alonso, A. (2025). Evaluating time-to-event surrogates for time-to-event true endpoints: an information-theoretic approach based on causal inference. Lifetime Data Anal., 31, 1-23.
Delhelle, M. and Van Keilegom, I. (2025). Copula based dependent censoring in cure models. TEST, 34, 361-382.
Crommen, G., Deresa, N.W., D’Haen, M., Ding, J., Willems, I. and Van Keilegom, I.(2025). Recent advances in copula-based methods for dependent censoring. SORT, 49, 3-42.
Piulachs, X., El Ghouch, A. and Van Keilegom, I. (2025). Testing for the functional form of a continuous covariate in the shared-parameter joint model. Statist. Med., 44, e10340.
Tedesco, L., Beyhum, J. and Van Keilegom, I. (2025). Instrumental variable estimation of the proportional hazards model by presmoothing. Electr. J. Statist., 19, 656-717.
Ong, F., Molenberghs, G., Callegaro, A., Van der Elst, W., Stijven, F., Verbeke, G., Van Keilegom, I. and Alonso, A. (2025). Assessing the operational characteristics of the individual causal association as a metric of surrogacy in the binary continuous setting. Pharm. Stat., 24, e2437.
Musta, E., Patilea, V. and Van Keilegom, I. (2025). Regression estimation using surrogate responses obtained by presmoothing. Statist. Neerl., 79, e12351.
Van Keilegom, I. and Deketelaere, B. (2025). Quantile regression for interval censored data using an Enriched Laplace distribution. Electr. J. Statist., 19, 54-86.
Lambert, P. and Kreyenfeld, M. (2025). Time-varying exogenous covariates with frequently changing values in double additive cure survival model: an application to fertility. Journal of the Royal Statistical Society: Series A - doi:10.1093/jrsssa/qnaf035 - R-package tvcure

2024

Beyhum, J., Tedesco, L. and Van Keilegom, I. (2024). Instrumental variable quantile regression under random right censoring. Econometrics Journal, 27, 21-36.
Lambert AS, Legrand C, Scholtes B, Samadoulougou S, Deconinck H, et al. Population stratification based on healthcare trajectories: A method for encouraging adaptive learning at meso level. Health Policy. 2024; 148, 105137
Jacquemain, A., Heuchenne, C., & Pircalabelu, E. (2024). A penalised bootstrap estimation procedure for the explained Gini coefficient. Electronic Journal of Statistics. 18(1): 247-300.
Nezakati, E. & Pircalabelu, E. (2024). Estimation and inference in sparse multivariate regression and conditional Gaussian graphical models under an unbalanced distributed setting. Electronic Journal of Statistics. 18(1): 599-652.
Doms, H., Lambert, P. and Legrand, C. (2024). Flexible joint model for time-to-event and non-Gaussian longitudinal outcomes. Statistical Methods in Medical Research, 33(10): 1783-1799. doi:10.1177/096228022412690
Van Keilegom, I. and Parsa, M. (2024). On a semiparametric estimation method for AFT mixture cure models. Electr. J. Statist., 18, 4882-4915.
Xie, P., Escobar-Bach, M. and Van Keilegom, I. (2024). Testing for suﬃcient follow-up in censored survival data by using extremes. Biometr. J., 66, e202400033. AFT mixture cure models. Electr. J. Statist., 18, 4882-4915.
Musta, E., Patilea, V. and Van Keilegom, I. (2024). A two-step estimation procedure for semiparametric mixture cure models. Scand. J. Statist., 51, 987-1011.
Crommen, G., Beyhum, J. and Van Keilegom, I. (2024). An instrumental variable approach under dependent censoring. TEST, 33, 473-495.
Deresa, N.W. and Van Keilegom, I. (2024). Copula based Cox proportional hazards models for dependent censoring. J. Amer. Statist. Assoc., 119, 1044-1054.
Beyhum, J., Florens, J.-P., Lapenta, E. and Van Keilegom, I. (2024). Testing for homogeneous treatment eﬀects in linear and nonparametric instrumental variable models. Economic Reviews, 43, 540-557.
Parmeter, C., Simar, L., Van Keilegom, I. and Zelenyuk, V. (2024). Inference in the nonparametric stochastic frontier model. Economic Reviews, 43, 518-539.
Parsa, M., Taghavi-Shahri, S.M. and Van Keilegom, I. (2024). On variable selection in a semiparametric AFT mixture cure model. Lifetime Data Anal., 30, 472-500.
Jeon, J.M. and Van Keilegom, I. (2024). Density estimation and regression analysis on hyperspheres in the presence of measurement error. Scand. J. Statist., 51, 513-556.
Beyhum, J., Centorrino, S., Florens, J.-P. and Van Keilegom, I. (2024). Instrumental variable estimation of dynamic treatment eﬀects on a duration outcome. J. Bus. Econ. Statist., 42, 732-742.
Venturini, M., Van Keilegom, I., De Corte, W. and Vens, C. (2024). Predicting time-to-intubation after critical care admission using machine learning and cured fraction information. Artif. Intell. Medic., 150, 102817.
Alonso, A., Ong, F., Stijven, F., Van der Elst, W., Molenberghs, G., Van Keilegom, I., Verbeke, G. and Callegaro, A. (2024). An information-theoretic approach for the assessment of a continuous outcome as a surrogate for a binary true endpoint based on causal inference: Application to vaccine evaluation. Statist. Medic., 43, 1083-1102.
Paparoditis, S. and Van Keilegom, I. (2024). Editorial - Special Issue ISNPS Conference Paphos . J. Nonpar. Statist., 36, 1-3.
Van Keilegom, I. and Keke¸c, E. (2024). Estimation of the density for censored and contaminated data. STAT, 13 (1), e651.
Pel´aez, R., Van Keilegom, I., Cao, R. and Vilar, J. (2024). Probability of default estimation in credit risk using mixture cure models. Comp. Statist. Data Anal., 189, 107853.

2023

Lambert, P. and Gressani, O. (2023). Penalty parameter selection and asymmetry corrections to Laplace approximations in Bayesian P-splines models. Statistical Modelling, 23(5-6): 409–
423. doi:10.1177/1471082X231181173 - R-package ordgam
De Backer, M., Legrand, C., Péron, J., Lambert, A., Buyse M. On the use of Extreme Value Tail Modeling for Generalized Pairwise Comparisons with Censored Outcomes. Pharm Stat. 2023; 22: 284-299. doi: 10.1002/pst.2271.
Lambert, P. (2023). Comments on: Nonparametric estimation in mixture cure models with covariates. Test, 32: 506-509. doi:10.1007/s11749-023-00860-3
Kreyenfeld, M., Konietzka, D., Lambert, P. and Ramos, .V. Second birth fertility in Germany: social class, gender, and the role of economic uncertainty. European Journal of Population, 2023, 39:5.
Lambert, P. Nonparametric density estimation and risk quantification from tabulated sample moments. Insurance: Mathematics and Economics, 2023, 108: 177-189.
Pircalabelu, E. & Claeskens, G. Linear manifold modeling and graph estimation based on multivariate functional data. Journal of Computational and Graphical Statistics. 2023, 32(2), 378-387.
Nezakati, E. & Pircalabelu, E. Unbalanced distributed estimation and inference for precision matrices. Statistics and Computing, 2023, 33, 47.
Pircalabelu, E. A spline-based time-varying reproduction number for modelling epidemiological outbreaks. Journal of the Royal Statistical Society (C), 2023, 72(3), 688–702.
Beyhum, J., Florens, J.-P. and Van Keilegom, I. (2023). A nonparametric instrumental approach to confounding in competing risks models. Lifetime Data Anal., 29, 709-734.
Kekec, E. and Van Keilegom, I. Variance matrix estimation in multivariate classical measurement error models. Statist. Papers, 2023 (to appear).
Tedesco, L. and Van Keilegom, I. (2023). Comparison of quantile regression curves with censored data. TEST, 32, 829-864.
Escobar-Bach, M. and Van Keilegom, I. (2023). Nonparametric estimation of conditional cure cure models for heavy-tailed distributions and under insuﬃcient follow-up. Comput. Statist. Data Anal., 183, 107728.
Czado, C. and Van Keilegom, I. (2023). Dependent censoring based on parametric copulas. Biometrika, 110, 721-738.
Fanjul-Hevia, A., Pardo-Fern´andez, J.C., Van Keilegom, I. and Gonz´alez-Manteiga, W.(2024). A test for comparing conditional ROC curves with multidimensional covariates. J. Appl. Statist., 51, 87-113.
Parsa, M. and Van Keilegom, I. (2023). Accelerated failure time vs Cox proportional hazards mixture cure models: David vs Goliath? Statist. Papers, 64, 835-855.
Beyhum, J., Florens, J.-P. and Van Keilegom, I. (2023). Discussion on “Instrumented difference-in-differences” by T. Ye, A. Ertefaie, J. Flory, S. Hennessy, and D.S. Small. Biometrics, 79, 582-586.
Gonz´alez Manteiga, W., Mart´ınez Miranda, M.D. and Van Keilegom, I. (2023). Goodness- of-ﬁt tests in proportional hazards models with random eﬀects. Biometr. J., 65, 2000353.
Beyhum, J. and Van Keilegom, I. Robust censored regression with l1-norm regularization. TEST, 2023, 32, 146-162.
Jeon, J.M. and Van Keilegom, I. Density estimation for mixed Euclidean and non-Euclidean data in the presence of measurement error. J. Multiv. Anal., 2023, 193, 105125.

2022

Garcia-Barrado, L., Burzykowski, T., Legrand, C., and Buyse, M. Using an interim analysis based exclusively on an early outcome in a randomized clinical trial with a long-term clinical endpoint. Pharmaceutical Statistics, 2022, 21(1): 209-2019.
Soetewey, A., Legrand, C, Denuit, M., & Silversmit, G. Semi-Markov modeling for cancer insurance. European Actuarial Journal 2022, 12, 813-837.
Pircalabelu, E. & Artemiou, A. "High-dimensional Sufficient Dimension Reduction through principal projections", Electronic Journal of Statistics, 2022, 16(1), 1804-1830.
Zhao, Y., Van Keilegom, I. and Ding, S. Envelopes for censored quantile re- gression. Scand. J. Statist., 2022, 49, 1562-1585.
Jeon, J.M., Park, B. and Van Keilegom, I. Nonparametric regression on Lie groups with measurement errors. Ann. Statist., 2022, 50, 2973-3008.
Deresa, N.W., Van Keilegom, I. and Antonio, K. Copula-based inference for bivariate survival data with left truncation and dependent censoring. Insur.: Math. Econ., 2022, 107, 1-21.
Kreiss, A. and Van Keilegom, I. Semi-parametric estimation of incubation and generation times by means of Laguerre polynomials. J. Nonpar. Statist., 2022, 34, 570-606.
Musta, E., Patilea, V. and Van Keilegom, I. A presmoothing approach for estimation in the semiparametric Cox mixture cure model. Bernoulli, 2022, 28, 2689-2715.
Venturini, M., Van Keilegom, I., De Corte, W. and Vens, C. A novel survival analysis approach to predict the need for intubation in intensive care units. Artif. Intell. Medic., 2022, 13263.
Han, B., Van Keilegom, I. and Wang, X. Semiparametric estimation of the non-mixture cure model with auxiliary survival information. Biometrics, 2022, 78, 448-459.
Zhao, Y., Gijbels, I. and Van Keilegom, I. Parametric copula adjusted for non- and semi-parametric regression. Ann. Statist., 2022, 50, 754-780.
Kekec, E. and Van Keilegom, I. (2022). Estimation of the variance matrix in bivariate classical measurement error models. Electr. J. Statist, 16, 1831-1854.
Beyhum, J., El Ghouch, A., Portier, F. and Van Keilegom, I. On an extension of the promotion time cure model. Ann. Statist., 2022, 50, 537-559.

2021

Cantagallo, E., De Backer, M., Kicinski, M., Ozenne, B., Collette, L., Legrand, C., Buyse, M., Péron ,J. A new measure of treatment effect in clinical trials involving competing risks based on generalized pairwise comparisons. Biometrical Journal, 2021 , 63, 272-288.
Soetewey, A., Legrand, C., Denuit, M., Silversmit G. (2021) Waiting period from diagnosis for mortgage insurance issued to cancer survivors. European Actuarial Journal. 2021; 11, 135–160.
Helander, S., Laketa, P., Ilmonen, P., Nagy, S., Van Bever, G., and Viitasaari, L. Integrated shape- sensitive functional metrics. Journal of Multivariate Analysis, 2021, 189, 104880.
Lambert, P. Fast Bayesian inference using Laplace approximations in nonparametric double additive location-scale models with right- and interval-censored data. Computational Statistics and Data Analysis, 2021, 161, 107250.
Le Coënt, Q., Legrand, C., Rondeau, V. Time-to-event surrogate endpoint validation using mediation analysis and meta-analytic data. Biostatistics, 2022; kxac044, https://doi.org/10.1093/biostatistics/kxac044
Nagy, S., Helander, S., Viitasaari, L., Van Bever, G. and Ilmonen, P. Flexible integrated functional depths. Bernoulli, 2021, 27, 1.
Pircalabelu, E. and Artemiou, A (2021). Graph informed sufficient dimension reduction. Computational Statistics and Data Analysis, 2021, 164, 107302.
Jacquemain, A., Heuchenne, C., & Pircalabelu, E. A lasso-type estimation for the Lorenz regression. Proceedings of the 22nd European Young Statistician Meeting, September 6-10th 2021, Athens. Panteion University. Pages 41-45.
Beyhum, J., Florens, J.-P. and Van Keilegom, I. Nonparametric instrumental regression with right censored duration outcomes. J. Bus. Econ. Statist., 2021, 40, 1034- 1045.
Florez, A.J., Van Keilegom, I., Molenberghs, G. and Verhasselt, A. Quantile regression for longitudinal data via the multivariate generalized hyperbolic distribution. Statist. Modelling, 2021, 22, 566-584.
Jeon, J.M., Park, B.U. and Van Keilegom, I. Additive regression for non- Euclidean responses and predictors. Ann. Statist., 2021, 49, 2611-2641.
Escobar-Bach, M., Maller, R., Van Keilegom, I. and Zhao, M. Estimation of the cure rate for distributions in the Gumbel maximum domain of attraction under insufficient follow-up. Biometrika, 2021, 109, 243-256.
Verhasselt, A., Flo ́rez, A.J., Van Keilegom, I. and Molenberghs, G. (2021). The impact of incomplete data on quantile regression for longitudinal data. J. Statist. Research, 2021, 55, 43-58.
Kloodt, N., Neumeyer, N. and Van Keilegom, I. Specification testing in semi- parametric transformation models. TEST, 2021, 30, 980-1003.
Conde-Amboage, M., Van Keilegom, I. and Gonzalez-Manteiga, W. A new lack- of-fit test for quantile regression with censored data. Scand. J. Statist., 2021, 48, 655-688.
Deresa, N. and Van Keilegom, I. On semiparametric modelling, estimation and inference for survival data subject to dependent censoring. Biometrika, 2021, 108, 965-979.
Amico, M., Van Keilegom, I. and Han, B. Assessing cure status prediction from survival data using receiver operating characteristic curves. Biometrika, 2021, 108, 727-740.
Musta, E. and Van Keilegom, I. A simulation-extrapolation approach for the mixture cure model with mismeasured covariates. Electr. J. Statist., 2021, 15, 3708-3742.
Jeon, J.M., Park, B. and Van Keilegom, I. Additive regression for predictors of various natures and possibly incomplete Hilbertian responses. Electr. J. Statist, 2021, 15, 1473-1548.
de Una Alvarez, J. and Van Keilegom, I. Efron-Petrosian integrals for doubly truncated data with covariates: an asymptotic analysis. Bernoulli, 2021, 27, 249-273.

2020

Lambert, P. and Bremhorst, V. (2020). Inclusion of time-varying covariates in cure survival models with an application in fertility studies. Journal of the Royal Statistical Society: Series A, 183(1): 333-354. doi:10.1111/rssa.12501 - R-code
De Backer, M., El Ghouch, A. and Van Keilegom, I. Linear censored quantile regression: a novel minimum-distance approach. Scand. J. Statist., 2020, 47, 1275-1306.
Barbieri A, Legrand C. Joint longitudinal and time-to-event cure models for the assessment of being cured. Stat Methods Med Res. 2020 Apr;29(4):1256-1270. doi: 10.1177/0962280219853599.
Florens, J.-P., Simar, L. and Van Keilegom, I. Estimation of the boundary of a variable observed with symmetric error. J. Amer. Statist. Assoc., 2020, 115, 425-441.
Gressani, O. and Lambert, P. Laplace approximation for fast Bayesian inference in generalized additive models based on P-splines. Computational Statistics and Data Analysis, 2021, 154, 107088.
Molenberghs, G., Buyse, M., Hens, N., Beutels, P., Faes, C., Verbeke, G., Van Damme, P., Goossens, H., Neyens, T., Abrams, S., Theeten, H., Pepermans, K., Alonso Abad, A., Van Keilegom, I., Speybroeck, N., Legrand, C., De Buyser, S. and Hulstaert, F. Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country. Controlled Clinical Trials, 2020, 99, Art. Nr. 106189.
Noh, H. and Van Keilegom, I. On relaxing the distributional assumption of stochastic frontier models. J. Korean Statist. Soc., 2020, 49, 1-14.
Chown, J., Heuchenne, C. and Van Keilegom, I. The nonparametric location- scale mixture cure model. TEST, 2020, 29, 1008-1028.
Deresa, N.W. and Van Keilegom, I. A multivariate normal regression model for survival data subject to different types of dependent censoring. Comp. Statist. Data Anal., 2020, 106879.
Racine, J. and Van Keilegom, I. A smooth nonparametric, multivariate, mixed- data location-scale test. J. Bus. Econ. Statist., 2020, 38, 784-795.
Geerdens, C., Janssen, P. and Van Keilegom, I. Goodness-of-fit test for a parametric survival function with cure fraction. TEST, 2020, 29, 768-792.
Zhao, Y., Gijbels, I. and Van Keilegom, I. Inference for semiparametric Gaussian copula model adjusted for linear regression using residual ranks. Bernoulli, 2020, 26, 2815- 2846.
Patilea, V. and Van Keilegom, I. A general approach for cure models in survival analysis. Ann. Statist., 2020, 48, 2323-2346.
Lopez-Cheda, A., Jacome-Pumar, A., Van Keilegom, I. and Cao, R. Nonparametric covariate hypothesis tests for the cure rate in mixture cure models. Statist. Med., 2020, 39, 2291-2307.
Florens, J.-P., Simar, L. and Van Keilegom, I. Estimation of the boundary of a variable observed with symmetric error. J. Amer. Statist. Assoc., 2020, 115, 425-441.
Deresa, N.W. and Van Keilegom, I. Flexible parametric model for survival data subject to dependent censoring. Biometr. J., 2020, 62, 136-156.
Delsol, L. and Van Keilegom, I. Semiparametric M-estimation with non-smooth criterion functions. Ann. Inst. Statist. Math., 2020, 72, 577-605.
Bravo, F., Escanciano, J.C. and Van Keilegom, I. Two-step semiparametric empirical likelihood inference. Ann. Statist., 2020, 48, 1-26.
Colling, B. and Van Keilegom, I. Estimation of a semiparametric transformation model : a novel approach based on least squares minimization. Electr. J. Statist., 2020, 14, 769-800.

Seminars/oral presentations by members of the ARC project: