References

Alvares, D., Van Niekerk, J., Krainski, E. T., Rue, H., & Rustand, D. (2024). Bayesian survival analysis with INLA. Statistics in Medicine, 43(20), 3975–4010. https://doi.org/10.1002/sim.10160

Alvarez, I., Niemi, J., & Simpson, M. (2014). Bayesian inference for a covariance matrix. Conference on Applied Statistics in Agriculture. https://doi.org/10.4148/2475-7772.1004

Anderson-Bergman, C. (2017). icenReg: Regression models for interval censored data in R. Journal of Statistical Software, 81(12), 1–23. https://doi.org/10.18637/jss.v081.i12

Andrinopoulou, E.-R., Rizopoulos, D., Takkenberg, J. J. M., & Lesaffre, E. (2014). Joint modeling of two longitudinal outcomes and competing risk data. Statistics in Medicine, 33(18), 3167–3178. https://doi.org/10.1002/sim.6158

Arbeeva, L., Nelson, A. E., Alvarez, C., Cleveland, R. J., Allen, K. D., Golightly, Y. M., Jordan, J. M., Callahan, L. F., & Schwartz, T. A. (2020). Application of traditional and emerging methods for the joint analysis of repeated measurements with time-to-event outcomes in rheumatology. Arthritis Care & Research, 72(5), 615–621. https://doi.org/10.1002/acr.23881

Baart, S. J., Palen, R. L. van der, Putter, H., Tsonaka, R., Blom, N. A., Rizopoulos, D., & Geloven, N. van. (2021). Joint modeling of longitudinal markers and time-to-event outcomes: An application and tutorial in patients after surgical repair of transposition of the great arteries. Circulation: Cardiovascular Quality and Outcomes, 14(11), e007593. https://doi.org/10.1161/CIRCOUTCOMES.120.007593

Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01

Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 192–225. https://doi.org/10.1111/j.2517-6161.1974.tb00999.x

Besag, J., York, J., & Mollié, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43, 1–20. https://doi.org/10.1007/BF00116466

Bivand, R. S., Pebesma, E., & Gómez-Rubio, V. (2013). Applied spatial data analysis with r, second edition. Springer. https://doi.org/10.1007/978-1-4614-7618-4

Blanche, P., Proust-Lima, C., Loubere, 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

Box, G. E., & Draper, N. R. (2007). Response surfaces, mixtures, and ridge analyses. John Wiley & Sons. https://doi.org/10.1002/0470072768

Broström, G. (2022). Event history analysis with r, second edition. Chapman; Hall/CRC. https://doi.org/10.1201/9780429503764

Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M. A., Guo, J., Li, P., & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76. https://doi.org/10.18637/jss.v076.i01

Chai, H., Jiang, H., Lin, L., & Liu, L. (2018). A marginalized two-part beta regression model for microbiome compositional data. PLoS Computational Biology, 14(7), e1006329. https://doi.org/10.1371/journal.pcbi.1006329

Chen, E. Z., & Li, H. (2016). A two-part mixed-effects model for analyzing longitudinal microbiome compositional data. Bioinformatics, 32(17), 2611–2617. https://doi.org/10.1093/bioinformatics/btw308

Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x

Cox, D. R. (1975). Partial likelihood. Biometrika, 62(2), 269–276. https://doi.org/10.1093/biomet/62.2.269

Crowther, M. J. (2020). Merlin—a unified modeling framework for data analysis and methods development in stata. The Stata Journal, 20(4), 763–784. https://doi.org/10.1177/1536867X20976311

Dagum, L., & Menon, R. (1998). OpenMP: An industry standard API for shared-memory programming. Computational Science & Engineering, IEEE, 5(1), 46–55. https://doi.org/10.1109/99.660313

Dantan, E., Joly, P., Dartigues, J.-F., & Jacqmin-Gadda, H. (2011). Joint model with latent state for longitudinal and multistate data. Biostatistics, 12(4), 723–736. https://doi.org/10.1093/biostatistics/kxr003

De Gruttola, V., & Tu, X. M. (1994). Modelling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics, 1003–1014. https://doi.org/10.2307/2533439

Duan, N., Manning, W. G., Morris, C. N., & Newhouse, J. P. (1983). A comparison of alternative models for the demand for medical care. Journal of Business & Economic Statistics, 1(2), 115–126. https://doi.org/10.2307/1391852

Fattah, E. A., Ltaief, H., Rue, H., & Keyes, D. (2025a). GPU-accelerated parallel selected inversion for structured matrices using sTiles. arXiv Preprint arXiv:2504.19171. https://doi.org/10.48550/arXiv.2504.19171

Fattah, E. A., Ltaief, H., Rue, H., & Keyes, D. (2025b). sTiles: An accelerated computational framework for sparse factorizations of structured matrices. ISC High Performance 2025 Research Paper Proceedings (40th International Conference). https://doi.org/10.48550/arXiv.2501.02483

Fattah, E. A., Van Niekerk, J., & Rue, H. (2022). Smart gradient-an adaptive technique for improving gradient estimation. Foundations of Data Science, 4(1), 123–136. https://doi.org/10.3934/fods.2021037

Faucett, C. L., & Thomas, D. C. (1996). Simultaneously modelling censored survival data and repeatedly measured covariates: A gibbs sampling approach. Statistics in Medicine, 15(15), 1663–1685. https://doi.org/10.1002/(SICI)1097-0258(19960815)15:15<1663::AID-SIM294>3.0.CO;2-1

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

Field, R. J., Adamson, C., Jhund, P., & Lewsey, J. (2023). Joint modelling of longitudinal processes and time-to-event outcomes in heart failure: Systematic review and exemplar examining the relationship between serum digoxin levels and mortality. BMC Medical Research Methodology, 23(1), 94. https://doi.org/10.1186/s12874-023-01918-4

Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015). MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biology, 16(1), 1–13. https://doi.org/10.1186/s13059-015-0844-5

Fisher, L. D., & Lin, D. Y. (1999). Time-dependent covariates in the cox proportional-hazards regression model. Annual Review of Public Health, 20(1), 145–157. https://doi.org/10.1146/annurev.publhealth.20.1.145

Fitzmaurice, G. M., Molenberghs, G., & Lipsitz, S. R. (1995). Regression models for longitudinal binary responses with informative drop-outs. Journal of the Royal Statistical Society: Series B (Methodological), 57(4), 691–704. https://doi.org/10.1111/j.2517-6161.1995.tb02056.x

Garcı́a-Hernandez, A., Pérez, T., Pardo, M. del C., & Rizopoulos, D. (2020). MMRM vs joint modeling of longitudinal responses and time to study drug discontinuation in clinical trials using a “de jure” estimand. Pharmaceutical Statistics. https://doi.org/10.1002/pst.2045

Gentleman, R., Whalen, E., Huber, W., Falcon, S., Gentry, J., & Shannon, P. (2024). Graph: A package to handle graph data structures. https://doi.org/10.18129/B9.bioc.graph

Genz, A., & Bretz, F. (2009). Computation of multivariate normal and t probabilities. Springer-Verlag. https://doi.org/10.1007/978-3-642-01689-9

Gilbert, P., & Varadhan, R. (2019). numDeriv: Accurate numerical derivatives. https://doi.org/10.32614/CRAN.package.numDeriv

Gómez-Rubio, V. (2020). Bayesian inference with INLA. Chapman; Hall/CRC. https://doi.org/10.1201/9781315175584

Goodrich, B., Gabry, J., Ali, I., & Brilleman, S. (2020). Rstanarm: Bayesian applied regression modeling via Stan. https://doi.org/10.32614/CRAN.package.rstanarm

Gressani, O., Faes, C., & Hens, N. (2022). Laplacian-p-splines for bayesian inference in the mixture cure model. Statistics in Medicine, 41(14), 2602–2626. https://doi.org/10.1002/sim.9373

Guo, X., & Carlin, B. P. (2004). Separate and joint modeling of longitudinal and event time data using standard computer packages. The American Statistician, 58(1), 16–24. https://doi.org/10.1198/0003130042854

Han, D., Liu, L., Su, X., Johnson, B., & Sun, L. (2019). Variable selection for random effects two-part models. Statistical Methods in Medical Research, 28(9), 2697–2709. https://doi.org/10.1177/0962280218784712

Harville, D. A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association, 72(358), 320–338. https://doi.org/10.1080/01621459.1977.10480998

Henderson, R., Diggle, P., & Dobson, A. (2000). Joint modelling of longitudinal measurements and event time data. Biostatistics, 1(4), 465–480. https://doi.org/10.1093/biostatistics/1.4.465

Hernangómez, D. (2023). Using the tidyverse with terra objects: The tidyterra package. Journal of Open Source Software, 8(91), 5751. https://doi.org/10.21105/joss.05751

Hickey, G. L., Philipson, P., Jorgensen, A., & Kolamunnage-Dona, R. (2016). Joint modelling of time-to-event and multivariate longitudinal outcomes: Recent developments and issues. BMC Medical Research Methodology, 16(1), 1–15. https://doi.org/10.1186/s12874-016-0212-5

Hickey, G. L., Philipson, P., Jorgensen, A., & Kolamunnage-Dona, R. (2018). joineRML: A joint model and software package for time-to-event and multivariate longitudinal outcomes. BMC Medical Research Methodology, 18, 1–14. https://doi.org/10.1186/s12874-018-0502-1

Hijmans, R. J. (2025). Terra: Spatial data analysis. https://doi.org/10.32614/CRAN.package.terra

Hogan, J. W., & Laird, N. M. (1997). Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine, 16(3), 239–257. https://doi.org/10.1002/(sici)1097-0258(19970215)16:3<239::aid-sim483>3.0.co;2-x

Holford, T. R. (1976). Life tables with concomitant information. Biometrics, 32(3), 587–597. https://doi.org/10.2307/2529747

Huang, Y., Wu, L., Yi, G. Y., & Lu, W. (2012). Joint models and their applications. Journal of Probability and Statistics, 2012(1), 463506. https://doi.org/10.1155/2012/463506

Ibrahim, J. G., Chu, H., & Chen, L. M. (2010). Basic concepts and methods for joint models of longitudinal and survival data. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 28(16), 2796–2801. https://doi.org/10.1200/JCO.2009.25.0654

Jacqmin-Gadda, H., Thiébaut, R., Chêne, G., & Commenges, D. (2000). Analysis of left-censored longitudinal data with application to viral load in HIV infection . Biostatistics, 1(4), 355–368. https://doi.org/10.1093/biostatistics/1.4.355

Johansen, S. (1983). An extension of cox’s regression model. International Statistical Review / Revue Internationale de Statistique, 51(2), 165–174. https://doi.org/10.2307/1402746

Kassahun-Yimer, W., Valle, K. A., Oshunbade, A. A., Hall, M. E., Min, Y.-I., Cain-Shields, L., Anugu, P., & Correa, A. (2020). Joint modelling of longitudinal lipids and time to coronary heart disease in the jackson heart study. BMC Medical Research Methodology, 20(1), 294. https://doi.org/10.1186/s12874-020-01177-7

Kerioui, M., Beaulieu, M., Desmée, S., Bertrand, J., Mercier, F., Jin, J. Y., Bruno, R., & Guedj, J. (2023). Nonlinear multilevel joint model for individual lesion kinetics and survival to characterize intra-individual heterogeneity in patients with advanced cancer. Biometrics, 79(4), 3752–3763. https://doi.org/10.1111/biom.13912

Krainski, E., Gómez-Rubio, V., Bakka, H., Lenzi, A., Castro-Camilo, D., Simpson, D., Lindgren, F., & Rue, H. (2018). Advanced spatial modeling with stochastic partial differential equations using r and INLA. Chapman; Hall/CRC. https://doi.org/10.1201/9780429031892

Król, A., Ferrer, L., Pignon, J.-P., Proust-Lima, C., Ducreux, M., Bouché, O., Michiels, S., & 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

Kurland, B. F., & Heagerty, P. J. (2005). Directly parameterized regression conditioning on being alive: Analysis of longitudinal data truncated by deaths. Biostatistics, 6(2), 241–258. https://doi.org/10.1093/biostatistics/kxi006

Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. https://doi.org/10.2307/2529876

Lawrence Gould, A., Boye, M. E., Crowther, M. J., Ibrahim, J. G., Quartey, G., Micallef, S., & Bois, F. Y. (2015). Joint modeling of survival and longitudinal non-survival data: Current methods and issues. Report of the DIA bayesian joint modeling working group. Statistics in Medicine, 34(14), 2181–2195. https://doi.org/10.1002/sim.6141

Lewandowski, D., Kurowicka, D., & Joe, H. (2009). Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis, 100(9), 1989–2001. https://doi.org/10.1016/j.jmva.2009.04.008

Li, N., Liu, Y., Li, S., Elashoff, R. M., & Li, G. (2021). A flexible joint model for multiple longitudinal biomarkers and a time-to-event outcome: With applications to dynamic prediction using highly correlated biomarkers. Biometrical Journal. https://doi.org/10.1002/bimj.202000085

Li, S., Ouyang, E., Zhou, J., Cui, X., & Li, G. (2025). Efficient implementation of a semiparametric joint model for multivariate longitudinal biomarkers and competing risks time-to-event data. arXiv Preprint arXiv:2506.12741. https://doi.org/10.48550/arXiv.2506.12741

Li, Z., Tosteson, T. D., & Bakitas, M. A. (2013). Joint modeling quality of life and survival using a terminal decline model in palliative care studies. Statistics in Medicine, 32(8), 1394–1406. https://doi.org/10.1002/sim.5635

Lindgren, F. (2025). Fmesher: Triangle meshes and related geometry tools. https://doi.org/10.32614/CRAN.package.fmesher

Lindgren, F., & Rue, H. (2015). Bayesian spatial modelling with R-INLA. Journal of Statistical Software, 63(19), 1–25. https://doi.org/10.18637/jss.v063.i19

Lindgren, F., Rue, H., & Lindström, J. (2011). An explicit link between gaussian fields and gaussian markov random fields: The stochastic partial differential equation approach. Journal of the Royal Statistical Society Series B: Statistical Methodology, 73(4), 423–498. https://doi.org/10.1111/j.1467-9868.2011.00777.x

Liu, L. (2009). Joint modeling longitudinal semi-continuous data and survival, with application to longitudinal medical cost data. Statistics in Medicine, 28(6), 972–986. https://doi.org/10.1002/sim.3497

Liu, L., Ma, J. Z., & Johnson, B. A. (2008). A multi-level two-part random effects model, with application to an alcohol-dependence study. Statistics in Medicine, 27(18), 3528–3539. https://doi.org/10.1002/sim.3205

Liu, L., Strawderman, R. L., Cowen, M. E., & Shih, Y.-C. T. (2010). A flexible two-part random effects model for correlated medical costs. Journal of Health Economics, 29(1), 110–123. https://doi.org/10.1016/j.jhealeco.2009.11.010

Liu, L., Strawderman, R. L., Johnson, B. A., & O’Quigley, J. M. (2016). Analyzing repeated measures semi-continuous data, with application to an alcohol dependence study. Statistical Methods in Medical Research, 25(1), 133–152. https://doi.org/10.1177/0962280212443324

Martino, S., Akerkar, R., & Rue, H. (2011). Approximate bayesian inference for survival models. Scandinavian Journal of Statistics, 38(3), 514–528. https://doi.org/10.1111/j.1467-9469.2010.00715.x

Massicotte, P., & South, A. (2023). Rnaturalearth: World map data from natural earth. https://doi.org/10.32614/CRAN.package.rnaturalearth

Matérn, B. (1960). Spatial variation: Stochastic models and their application to some problems in forest surveys and other sampling investigations [PhD thesis]. https://pub.epsilon.slu.se/10033/1/medd_statens_skogsforskningsinst_049_05.pdf

Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. https://doi.org/10.2113/gsecongeo.58.8.1246

McDavid, A., Finak, G., Chattopadyay, P. K., Dominguez, M., Lamoreaux, L., Ma, S. S., Roederer, M., & Gottardo, R. (2013). Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics, 29(4), 461–467. https://doi.org/10.1093/bioinformatics/bts714

Medina-Olivares, V., Lindgren, F., Calabrese, R., & Crook, J. (2023). Joint models of multivariate longitudinal outcomes and discrete survival data with INLA: An application to credit repayment behaviour. European Journal of Operational Research, 310(2), 860–873. https://doi.org/10.1016/j.ejor.2023.03.012

Molenberghs, G., & Verbeke, G. (2001). A review on linear mixed models for longitudinal data, possibly subject to dropout. Statistical Modelling, 1(4), 235–269. https://doi.org/10.1177/1471082X0100100402

Murray, J., & Philipson, P. (2022). A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data. Computational Statistics & Data Analysis, 170, 107438. https://doi.org/10.1016/j.csda.2022.107438

Murtaugh, P. A., Dickson, E. R., Van Dam, G. M., Malinchoc, M., Grambsch, P. M., Langworthy, A. L., & Gips, C. H. (1994). Primary biliary cirrhosis: Prediction of short-term survival based on repeated patient visits. Hepatology, 20(1), 126–134. https://doi.org/10.1016/0270-9139(94)90144-9

Neto, D. S., Rustand, D., Rue, H., Alvares, D., & Tomazella, V. L. (2025). A new approach for bayesian joint modeling of longitudinal and cure-survival outcomes using the defective gompertz distribution. arXiv Preprint arXiv:2507.23196. https://doi.org/10.48550/arXiv.2507.23196

Pan, W. (2000). A multiple imputation approach to cox regression with interval-censored data. Biometrics, 56(1), 199–203. https://doi.org/ 10.1111/j.0006-341x.2000.00199.x

Pettit, L. (1990). The conditional predictive ordinate for the normal distribution. Journal of the Royal Statistical Society: Series B (Methodological), 52(1), 175–184. https://doi.org/10.1111/j.2517-6161.1990.tb01780.x

Proust-Lima, C., Séne, M., Taylor, J. M., & Jacqmin-Gadda, H. (2014). Joint latent class models for longitudinal and time-to-event data: A review. Statistical Methods in Medical Research, 23(1), 74–90. https://doi.org/10.1177/0962280212445839

Proust-Lima, C., & Taylor, J. M. (2009). Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: A joint modeling approach. Biostatistics, 10(3), 535–549. https://doi.org/10.1093/biostatistics/kxp009

Putter, H., Fiocco, M., & Geskus, R. B. (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine, 26(11), 2389–2430. https://doi.org/10.1002/sim.2712

R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Riebler, A., Sørbye, S. H., Simpson, D., & Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research, 25(4), 1145–1165. https://doi.org/10.1177/0962280216660421

Rizopoulos, D. (2010). JM: An r package for the joint modelling of longitudinal and time-to-event data. Journal of Statistical Software, 35, 1–33. https://doi.org/10.18637/jss.v035.i09

Rizopoulos, D. (2016). The r package JMbayes for fitting joint models for longitudinal and time-to-event data using MCMC. Journal of Statistical Software, 72(i07). https://doi.org/10.18637/jss.v072.i07

Rizopoulos, D., Miranda Afonso, P., & Papageorgiou, G. (2025). JMbayes2: Extended joint models for longitudinal and time-to-event data. https://doi.org/10.32614/CRAN.package.JMbayes2

Rondeau, V., Marzroui, Y., & Gonzalez, J. R. (2012). Frailtypack: An r package for the analysis of correlated survival data with frailty models using penalized likelihood estimation or parametrical estimation. Journal of Statistical Software, 47, 1–28. https://doi.org/10.18637/jss.v047.i04

Rondeau, V., Mathoulin-Pelissier, S., Jacqmin-Gadda, H., Brouste, V., & Soubeyran, P. (2007). Joint frailty models for recurring events and death using maximum penalized likelihood estimation: Application on cancer events. Biostatistics, 8(4), 708–721. https://doi.org/10.1093/biostatistics/kxl043

Rouanet, A., Helmer, C., Dartigues, J.-F., & Jacqmin-Gadda, H. (2019). Interpretation of mixed models and marginal models with cohort attrition due to death and drop-out. Statistical Methods in Medical Research, 28(2), 343–356. https://doi.org/10.1177/0962280217723675

Royston, P., & Parmar, M. K. (2002). Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine, 21(15), 2175–2197. https://doi.org/10.1002/sim.1203

Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592. https://doi.org/10.2307/2335739

Rue, H., & Held, L. (2005). Gaussian markov random fields: Theory and applications. CRC Press. https://doi.org/10.1201/9780203492024

Rue, H., Martino, S., & Chopin, N. (2009). Approximate bayesian inference for latent gaussian models by using integrated nested laplace approximations. Journal of the Royal Statistical Society B: Statistical Methodology, 71(2), 319–392. https://doi.org/10.1111/j.1467-9868.2008.00700.x

Rustand, D., Briollais, L., & Rondeau, V. (2024). A marginalized two-part joint model for a longitudinal biomarker and a terminal event with application to advanced head and neck cancers. Pharmaceutical Statistics, 23(1), 60–80. https://doi.org/10.1002/pst.2338

Rustand, D., Briollais, L., Tournigand, C., & Rondeau, V. (2020). Two-part joint model for a longitudinal semicontinuous marker and a terminal event with application to metastatic colorectal cancer data. Biostatistics, 23(1), 50–68. https://doi.org/10.1093/biostatistics/kxaa012

Rustand, D., Niekerk, J. V., Krainski, E. T., & Rue, H. (2024). Joint modeling of multivariate longitudinal and survival outcomes with the r package INLAjoint. arXiv Preprint arXiv:2402.08335. https://doi.org/10.48550/arXiv.2402.08335

Rustand, D., Van Niekerk, J., Krainski, E. T., Rue, H., & Proust-Lima, C. (2024). Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested laplace approximations. Biostatistics, 25(2), 429–448. https://doi.org/10.1093/biostatistics/kxad019

Rustand, D., Van Niekerk, J., Rue, H., Tournigand, C., Rondeau, V., & Briollais, L. (2023). Bayesian estimation of two-part joint models for a longitudinal semicontinuous biomarker and a terminal event with INLA: Interests for cancer clinical trial evaluation. Biometrical Journal, 65(4), 2100322. https://doi.org/10.1002/bimj.202100322

Sakia, R. M. (1992). The box-cox transformation technique: A review. Journal of the Royal Statistical Society Series D: The Statistician, 41(2), 169–178. https://doi.org/10.2307/2348250

SAS Institute Inc. (2003). SAS/STAT software, version 9.1. https://www.sas.com/

Simpson, D., Rue, H., Riebler, A., Martins, T. G., & Sørbye, S. H. (2017). Penalising model component complexity: A principled, practical approach to constructing priors. Statistical Science, 32(1), 1–28. https://doi.org/10.1214/16-STS576

Smith, V. A., Maciejewski, M. L., & Olsen, M. K. (2018). Modeling semicontinuous longitudinal expenditures: A practical guide. Health Services Research, 53, 3125–3147. https://doi.org/10.1111/1475-6773.12815

Smith, V. A., Preisser, J. S., Neelon, B., & Maciejewski, M. L. (2014). A marginalized two-part model for semicontinuous data. Statistics in Medicine, 33(28), 4891–4903. https://doi.org/10.1002/sim.6263

Song, H., Peng, Y., & Tu, D. (2016). Recent development in the joint modeling of longitudinal quality of life measurements and survival data from cancer clinical trials. In Advanced statistical methods in data science (pp. 153–168). Springer. https://doi.org/10.1007/978-981-10-2594-5_8

Sørbye, S. H., & Rue, H. (2014). Scaling intrinsic Gaussian Markov random field priors in spatial modelling. Spatial Statistics, 8(3), 39–51. https://doi.org/10.1016/j.spasta.2013.06.004

Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society B: Statistical Methodology, 64(4), 583–639. https://doi.org/10.1111/1467-9868.00353

Sterrantino, A. F., Krainski, E. T., Rustand, D., Niekerk, J. V., & Rue, H. (2025). Informative penalized complexity prior for correlation matrix for multivariate modelling.

Sterrantino, A. F., Rustand, D., Niekerk, J. V., Krainski, E. T., & Rue, H. (2025). A graphical framework for interpretable correlation matrix models. arXiv Preprint arXiv:2312.06289. https://doi.org/10.48550/arXiv.2312.06289

Terry M. Therneau, & Patricia M. Grambsch. (2000). Modeling survival data: Extending the Cox model. Springer. https://doi.org/10.1007/978-1-4757-3294-8

Therneau, T. M. (2023). Survival: A package for survival analysis in r. https://doi.org/10.32614/CRAN.package.survival

Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica: Journal of the Econometric Society, 24–36. https://doi.org/10.2307/1907382

Tsiatis, A. A., Degruttola, V., & Wulfsohn, M. S. (1995). Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association, 90(429), 27–37. https://doi.org/10.2307/2291126

Van Eijk, R. P., Roes, K. C., Berg, L. H. van den, & Lu, Y. (2022). Joint modeling of endpoints can be used to answer various research questions in randomized clinical trials. Journal of Clinical Epidemiology, 147, 32–39. https://doi.org/10.1016/j.jclinepi.2022.03.009

Van Niekerk, J., Bakka, H., & Rue, H. (2021). A principled distance-based prior for the shape of the weibull model. Statistics & Probability Letters, 174, 109098. https://doi.org/10.1016/j.spl.2021.109098

Van Niekerk, J., Bakka, H., & Rue, H. (2023). Stable non-linear generalized bayesian joint models for survival-longitudinal data. Sankhya A.

Van Niekerk, J., Bakka, H., Rue, H., & Schenk, O. (2021). New frontiers in bayesian modeling using the INLA package in R. Journal of Statistical Software, 100(2), 1–28. https://doi.org/10.18637/jss.v100.i02

Van Niekerk, J., Krainski, E., Rustand, D., & Rue, H. (2023). A new avenue for bayesian inference with INLA. Computational Statistics & Data Analysis, 181, 107692. https://doi.org/10.1016/j.csda.2023.107692

Van Niekerk, J., & Rue, H. (2022). Use of the INLA approach for the analysis of interval-censored data. In Emerging topics in modeling interval-censored survival data (pp. 123–140). Springer.

Van Niekerk, J., & Rue, H. (2024). Low-rank variational bayes correction to the laplace method. Journal of Machine Learning Research, 25(62), 1–25. https://jmlr.org/papers/v25/21-1405.html

Verbeke, G., & Molenberghs, G. (2000). How ignorable is missing at random? In Linear mixed models for longitudinal data (pp. 375–386). Springer New York. https://doi.org/10.1007/978-1-4419-0300-6_21

Wang, H., Li, N., Li, S., & Li, G. (2021). JMcmprsk: An r package for joint modelling of longitudinal and survival data with competing risks. The R Journal, 13(1), 53–67. https://doi.org/10.32614/RJ-2021-028

Wang, J.-L., & Zhong, Q. (2024). Joint modeling of longitudinal and survival data. Annual Review of Statistics and Its Application, 12. https://doi.org/10.1146/annurev-statistics-112723-034334

Wang, Y., & Taylor, J. M. G. (2001). Jointly modeling longitudinal and event time data with application to acquired immunodeficiency syndrome. Journal of the American Statistical Association, 96(455), 895–905. https://doi.org/10.1198/016214501753208591

Watanabe, S. (2013). A widely applicable bayesian information criterion. The Journal of Machine Learning Research, 14(1), 867–897. https://www.jmlr.org/papers/volume14/watanabe13a/watanabe13a.pdf

Whitehead, J. (1980). Fitting cox’s regression model to survival data using GLIM. Journal of the Royal Statistical Society C (Applied Statistics), 29(3), 268–275. https://doi.org/10.2307/2346901

Whittle, P. (1954). On stationary processes in the plane. Biometrika, 41(3/4), 434–449. https://doi.org/10.2307/2332724

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. https://doi.org/10.1007/978-0-387-98141-3

Wickham, H., Hester, J., Chang, W., & Bryan, J. (2022). Devtools: Tools to make developing r packages easier. https://doi.org/10.32614/CRAN.package.devtools

Wulfsohn, M. S., & Tsiatis, A. A. (1997). A joint model for survival and longitudinal data measured with error. Biometrics, 330–339. https://doi.org/10.2307/2533118

Xu, C., Hadjipantelis, P. Z., & Wang, J.-L. (2020). Semi-parametric joint modeling of survival and longitudinal data: The r package JSM. Journal of Statistical Software, 93(2). https://doi.org/10.18637/jss.v093.i02

Zhang, D., Chen, M.-H., Ibrahim, J. G., Boye, M. E., Wang, P., & Shen, W. (2014). Assessing model fit in joint models of longitudinal and survival data with applications to cancer clinical trials. Statistics in Medicine, 33(27), 4715–4733. https://doi.org/10.1002/sim.6269