
Education
2014 HDR (Habilitation to conduct research), Univ. Bordeaux, France.
2006 PhD in Biostatistics, Univ. Bordeaux Segalen, France.
2003 Master degree in Epidemiology and Public Health, ISPED, Univ. Bordeaux Segalen, France.
2002 Master degree in Biostatistics, National School for Statistics and Information Analysis, ENSAI, Rennes, France.
Positions
2019-… Director of Research (DR2), Inserm, Bordeaux, France.
2008-2019 Researcher (CR1), Inserm, Bordeaux, France.
2008 Research Assistant, Biostatistics and Nutrition departments, Inserm, Bordeaux, France.
2008 Assistant Professor, ISPED, Université Bordeaux Segalen, Bordeaux, France.
2007 Postdoctoral fellow, Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, USA.
2003-2006 Assistant professor, ISPED, Université Bordeaux Segalen, Bordeaux, France.
Teaching
Joint models, Latent class mixed models, Latent variable models, (Generalized) linear mixed models, Longitudinal analysis of psychometric scales in Masters, summer schools, short courses
Research interests
Development of dynamic statistical models to describe, explain and predict chronic disease progressions. I specialized in the joint analysis of correlated longitudinal markers and event time history using the so called joint models with applications mainly in neurodegenerative diseases (e.g., Alzheimer’s disease) and cancers. My works often involve latent processes to translate dynamic health phenomena measured by repeated markers, and latent classes to translate the heterogeneity of disease progression. I have also worked on the development of individual dynamic predictions that quantify the risk of experiencing a clinical event based on the dynamic history of an individual. All my works are motivated by epidemiological and clinical questions thanks to strong collaborations with epidemiologists and clinicians, and access to large cohort studies. My developments are made available in R packages with constant maintainance and upgrades.
I serve as Associate Editor for Biometrics (since 2014) and Biostatistics (since 2016).
I belong to the international initiative in Methods in Longitudinal Research in Dementia (MELODEM).
Software
See my github repository and
marqLevAlg: A Parallelized Algorithm for Least-Squares Curve Fitting. R package version 2.0.1.
CInNPL: Causal Inference in a Network of Latent Processes
multLPM: Joint model for multivariate latent processes and competing events
lcmm: Extended Mixed Models Using Latent Classes and Latent Processes. R package version 1.7.8.
NormPsy: Normalisation of Psychometric Tests. R package version 1.0.3.
HETMIXSURV, Fortran 90 program for multivariate heterogeneous longitudinal data analysis.
Publications
2019
2018
2016
Bellera, C., Proust-Lima, C., Joseph, L., Richaud, P., Taylor, J., Sandler, H., … Mathoulin-Pélissier, S. (2016). A two-stage model in a Bayesian framework to estimate a survival endpoint in the presence of confounding by indication. Statistical Methods in Medical Research. https://doi.org/10.1177/0962280216660127
Carrière, I., Farré, A., Proust-Lima, C., Ryan, J., Ancelin, M. L., & Ritchie, K. (2016). Chronic and remitting trajectories of depressive symptoms in the elderly. Characterisation and risk factors. Epidemiology and Psychiatric Sciences, 1–11. https://doi.org/10.1017/S2045796015001122
Edjolo, A., Proust-Lima, C., Delva, F., Dartigues, J.-F., & Pérès, K. (2016). Natural History of Dependency in the Elderly: A 24-Year Population-Based Study Using a Longitudinal Item Response Theory Model. American Journal of Epidemiology, 183(4), 277–285. https://doi.org/10.1093/aje/kwv223
Ferrer, L., Rondeau, V., Dignam, J., Pickles, T., Jacqmin-Gadda, H., & Proust-Lima, C. (2016). Joint modelling of longitudinal and multi-state processes: application to clinical progressions in prostate cancer. Statistics in Medicine, 35(22), 3933–3948. https://doi.org/10.1002/sim.6972
Król, , A., Ferrer, L., Pignon, J.-P., Proust-Lima, C., Ducreux, M., Bouché, O., … Rondeau, V. (2016). Joint model for left-censored longitudinal data, recurrent events and terminal event: Predictive abilities of tumor burden for cancer evolution with application to the FFCD 2000-05 trial. Biometrics, 72(3), 907–916. https://doi.org/10.1111/biom.12490
Rouanet, A., Joly, P., Dartigues, J.-F., Proust-Lima, C., & Jacqmin-Gadda, H. (2016). Joint latent class model for longitudinal data and interval-censored semi-competing events: Application to dementia. Biometrics, 72(4), 1123–1135. https://doi.org/10.1111/biom.12530
Sène, M., Taylor, J. M., Dignam, J. J., Jacqmin-Gadda, H., & Proust-Lima, C. (2016). Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: Development and validation. Statistical Methods in Medical Research, 25(6), 2972–2991. https://doi.org/10.1177/0962280214535763
Vivot, A., Power, M. C., Glymour, M. M., Mayeda, E. R., Benitez, A., Spiro, A., … Gross, A. L. (2016). Jump, Hop, or Skip: Modeling Practice Effects in Studies of Determinants of Cognitive Change in Older Adults. American Journal of Epidemiology, 183(4), 302–314. https://doi.org/10.1093/aje/kwv212
Weuve*, J., Proust-Lima*, C., Power*, M. C., Gross, A. L., Hofer, S. M., Thiébaut, R., … MELODEM Initiative. (2015). Guidelines for reporting methodological challenges and evaluating potential bias in dementia research. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 11(9), 1098–1109. https://doi.org/10.1016/j.jalz.2015.06.1885
2015
Blanche, P., Proust-Lima, C., Loubère, L., Berr, C., Dartigues, J.-F., & Jacqmin-Gadda, H. (2015). Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks. Biometrics, 71(1), 102–113. https://doi.org/10.1111/biom.12232
Commenges, D., Jacqmin-Gadda, H., Alioum, A., Joly, P., Liquet, B., Proust-Lima, C., … Thiébaut, R. (2015a). Dynamical Biostatistical Models. CRC Press.
Commenges, D., Jacqmin-Gadda, H., Alioum, A., Joly, P., Liquet, B., Proust-Lima, C., … Thiébaut, R. (2015b). Modèles biostatistiques pour l’épidémiologie. Louvain-la-Neuve: DE BOECK UNIVERSITE.
Commenges, D., Proust-Lima, C., Samieri, C., & Liquet, B. (2015). A universal approximate cross-validation criterion for regular risk functions. The International Journal of Biostatistics, 11(1), 51–67. https://doi.org/10.1515/ijb-2015-0004
Marioni, R. E., Proust-Lima, C., Amieva, H., Brayne, C., Matthews, F. E., Dartigues, J.-F., & Jacqmin-Gadda, H. (2015). Social activity, cognitive decline and dementia risk: a 20-year prospective cohort study. BMC Public Health, 15, 1089. https://doi.org/10.1186/s12889-015-2426-6
Proust-Lima, C., & Blanche, P. (2015). Dynamic Predictions. In Wiley StatsRef: Statistics Reference Online. John Wiley & Sons, Ltd.