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JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data

Citations

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Cited by:

  1. Hongyuan Cao & Mathew M. Churpek & Donglin Zeng & Jason P. Fine, 2015. "Analysis of the Proportional Hazards Model With Sparse Longitudinal Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1187-1196, September.
  2. Francesco Bartolucci & Alessio Farcomeni, 2015. "A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates," Biometrics, The International Biometric Society, vol. 71(1), pages 80-89, March.
  3. Solène Desmée & France Mentré & Christine Veyrat-Follet & Bernard Sébastien & Jérémie Guedj, 2017. "Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients," Biometrics, The International Biometric Society, vol. 73(1), pages 305-312, March.
  4. Graeme L. Hickey & Pete Philipson & Andrea Jorgensen & Ruwanthi Kolamunnage‐Dona, 2018. "A comparison of joint models for longitudinal and competing risks data, with application to an epilepsy drug randomized controlled trial," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1105-1123, October.
  5. Rizopoulos, Dimitris, 2012. "Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 491-501.
  6. Shahedul A. Khan & Saima K. Khosa, 2016. "Generalized log-logistic proportional hazard model with applications in survival analysis," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-18, December.
  7. Medina-Olivares, Victor & Calabrese, Raffaella & Crook, Jonathan & Lindgren, Finn, 2023. "Joint models for longitudinal and discrete survival data in credit scoring," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1457-1473.
  8. Philipson, Pete & Hickey, Graeme L. & Crowther, Michael J. & Kolamunnage-Dona, Ruwanthi, 2020. "Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
  9. Wang, Songfeng & Zhang, Jiajia & Lu, Wenbin, 2014. "Sample size calculation for the proportional hazards model with a time-dependent covariate," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 217-227.
  10. Oi, Katsuya, 2020. "Disuse as time away from a cognitively demanding job; how does it temporally or developmentally impact late-life cognition?," Intelligence, Elsevier, vol. 82(C).
  11. Marlena Maziarz & Patrick Heagerty & Tianxi Cai & Yingye Zheng, 2017. "On longitudinal prediction with time-to-event outcome: Comparison of modeling options," Biometrics, The International Biometric Society, vol. 73(1), pages 83-93, March.
  12. Jani Raitanen & Sari Stenholm & Kristina Tiainen & Marja Jylhä & Jaakko Nevalainen, 2020. "Longitudinal change in physical functioning and dropout due to death among the oldest old: a comparison of three methods of analysis," European Journal of Ageing, Springer, vol. 17(2), pages 207-216, June.
  13. Daniel Commenges, 2019. "Dealing with death when studying disease or physiological marker: the stochastic system approach to causality," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 381-405, July.
  14. Shakhawat Hossain & Shahedul A. Khan, 2020. "Shrinkage estimation of the exponentiated Weibull regression model for time‐to‐event data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(4), pages 592-610, November.
  15. Kamaryn T. Tanner & Linda D. Sharples & Rhian M. Daniel & Ruth H. Keogh, 2021. "Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 3-30, January.
  16. Wang, Shikun & Li, Zhao & Lan, Lan & Zhao, Jieyi & Zheng, W. Jim & Li, Liang, 2022. "GPU accelerated estimation of a shared random effect joint model for dynamic prediction," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
  17. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
  18. Zhuowei Sun & Hongyuan Cao & Li Chen, 2022. "Regression analysis of additive hazards model with sparse longitudinal covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 263-281, April.
  19. Colin Griesbach & Andreas Groll & Elisabeth Bergherr, 2021. "Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
  20. Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2014. "Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 146-162.
  21. Cuihong Zhang & Jing Ning & Steven H. Belle & Robert H. Squires & Jianwen Cai & Ruosha Li, 2022. "Assessing predictive discrimination performance of biomarkers in the presence of treatment‐induced dependent censoring," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1137-1157, November.
  22. Daniel Commenges & Benoit Liquet & Cécile Proust-Lima, 2012. "Choice of Prognostic Estimators in Joint Models by Estimating Differences of Expected Conditional Kullback–Leibler Risks," Biometrics, The International Biometric Society, vol. 68(2), pages 380-387, June.
  23. Colin Griesbach & Andreas Mayr & Elisabeth Bergherr, 2023. "Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques," Mathematics, MDPI, vol. 11(2), pages 1-16, January.
  24. de Godoy, Isabelle Bueno Silva & McGrane-Corrigan, Blake & Mason, Oliver & Moral, Rafael de Andrade & Godoy, Wesley Augusto Conde, 2023. "Plant-host shift, spatial persistence, and the viability of an invasive insect population," Ecological Modelling, Elsevier, vol. 475(C).
  25. Chen, Chyong-Mei & Shen, Pao-sheng & Tseng, Yi-Kuan, 2018. "Semiparametric transformation joint models for longitudinal covariates and interval-censored failure time," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 116-127.
  26. Shahedul A. Khan & Nyla Basharat, 2022. "Accelerated failure time models for recurrent event data analysis and joint modeling," Computational Statistics, Springer, vol. 37(4), pages 1569-1597, September.
  27. Hanze Zhang & Yangxin Huang, 2020. "Quantile regression-based Bayesian joint modeling analysis of longitudinal–survival data, with application to an AIDS cohort study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(2), pages 339-368, April.
  28. Taban Baghfalaki & Mojtaba Ganjali & Geert Verbeke, 2017. "A shared parameter model of longitudinal measurements and survival time with heterogeneous random-effects distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(15), pages 2813-2836, November.
  29. Lisa M. McCrink & Adele H. Marshall & Karen J. Cairns, 2013. "Advances in Joint Modelling: A Review of Recent Developments with Application to the Survival of End Stage Renal Disease Patients," International Statistical Review, International Statistical Institute, vol. 81(2), pages 249-269, August.
  30. Walter Dempsey & Peter McCullagh, 2018. "Survival models and health sequences," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 550-584, October.
  31. Xavier Piulachs & Ramon Alemany & Montserrat Guillen, 2014. "A joint longitudinal and survival model with health care usage for insured elderly," Working Papers 2014-07, Universitat de Barcelona, UB Riskcenter.
  32. Yih‐Huei Huang & Wen‐Han Hwang & Fei‐Yin Chen, 2016. "Improving efficiency using the Rao–Blackwell theorem in corrected and conditional score estimation methods for joint models," Biometrics, The International Biometric Society, vol. 72(4), pages 1136-1144, December.
  33. Zhang, Zili & Charalambous, Christiana & Foster, Peter, 2023. "A Gaussian copula joint model for longitudinal and time-to-event data with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
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