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Joint Modeling of Longitudinal Measurements and Time-to-event Outcomes Using BUGS (2403.07778v2)

Published 12 Mar 2024 in stat.ME and stat.CO

Abstract: The objective of this paper is to provide an introduction to the principles of Bayesian joint modeling of longitudinal measurements and time-to-event outcomes, as well as model implementation using the BUGS language syntax. This syntax can be executed directly using OpenBUGS or by utilizing convenient functions to invoke OpenBUGS and JAGS from R software. In this paper, all details of joint models are provided, ranging from simple to more advanced models. The presentation started with the joint modeling of a Gaussian longitudinal marker and time-to-event outcome. The implementation of the Bayesian paradigm of the model is reviewed. The strategies for simulating data from the JM are also discussed. A proportional hazard model with various forms of baseline hazards, along with the discussion of all possible association structures between the two sub-models are taken into consideration. The paper covers joint models with multivariate longitudinal measurements, zero-inflated longitudinal measurements, competing risks, and time-to-event with cure fraction. The models are illustrated by the analyses of several real data sets. All simulated and real data and code are available at \url{https://github.com/tbaghfalaki/JM-with-BUGS-and-JAGS}.

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References (81)
  1. Time-dependent hazard ratio: modeling and hypothesis testing with application in lupus nephritis. Journal of the American Statistical Association, 91(436):1432–1439, 1996.
  2. A comparative trial of didanosine or zalcitabine after treatment with zidovudine in patients with human immunodeficiency virus infection. New England Journal of Medicine, 330(10):657—662, 1994.
  3. Bayesian survival analysis with bugs. Statistics in Medicine, 40(12):2975–3020, 2021.
  4. Cure models in survival analysis. Annual Review of Statistics and Its Application, 5(1):null, 2018.
  5. Peter C Austin. Generating survival times to simulate cox proportional hazards models with time-varying covariates. Statistics in medicine, 31(29):3946–3958, 2012.
  6. T Baghfalaki and M Ganjali. Approximate bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data. Statistical Methods in Medical Research, 30(6):1484–1501, 2021.
  7. Spatial modeling with r-inla: A review. Wiley Interdisciplinary Reviews: Computational Statistics, 10(6):e1443, 2018.
  8. Package ‘superdiag’. R CRAN, 2022.
  9. Joint longitudinal and time-to-event cure models for the assessment of being cured. Statistical Methods in Medical Research, 29(4):1256—1270, 2020.
  10. A fast em algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits. Computational statistics & data analysis, 85:37–53, 2015.
  11. Spatial data analysis with r-inla with some extensions. American Statistical Association, 2015.
  12. Ignace Bogaert. Iteration-free computation of gauss-legendre quadrature nodes and weights. SIAM Journal on Scientific Computing, 36(3):A1008–A1026, 2014.
  13. William M Bolstad. Understanding computational Bayesian statistics, volume 644. John Wiley & Sons, 2009.
  14. Richard P Brent. Algorithms for minimization without derivatives. Courier Corporation, 2013.
  15. Bayesx: analyzing bayesian structural additive regression models. Journal of statistical software, 14:1–22, 2005.
  16. Samuel L Brilleman. simjm: Simulate joint longitudinal and survival data. R package version 0.0.1, URL https://github.com/sambrilleman/simjm., 2018.
  17. Simulating survival data using the simsurv r package. Journal of Statistical Software, 97:1–27, 2021.
  18. Bayesian Approaches to Joint Cure-Rate and Longitudinal Models with Applications to Cancer Vaccine Trials. Biometrics, 59(3):686–693, September 2003.
  19. Bayesian ideas and data analysis: an introduction for scientists and statisticians. CRC press, 2010.
  20. Simulating complex survival data. The Stata Journal, 12(4):674–687, 2012.
  21. Package ‘mcmcplots’. R CRAN, 2018.
  22. Peter Diggle. Analysis of longitudinal data. Oxford university press, 2002.
  23. Temporal and spatial variation of the human microbiota during pregnancy. Proceedings of the National Academy of Sciences, 112(35):11060–11065, 2015.
  24. Modeling longitudinal data with nonparametric multiplicative random effects jointly with survival data. Biometrics, 64(2):546–556, 2008.
  25. Joint modeling of longitudinal and time-to-event data. CRC press, 2016.
  26. V. T. Farewell. The Use of Mixture Models for the Analysis of Survival Data with Long-Term Survivors. Biometrics, 38(4):1041–1046, 1982.
  27. Longitudinal data analysis. CRC press, 2008.
  28. Inference from iterative simulation using multiple sequences. Statistical science, 7(4):457–472, 1992.
  29. John Geweke. Evaluating the accuracy of sampling-based approaches to the calculations of posterior moments. Bayesian statistics, 4:641–649, 1992.
  30. Package ‘rstan’. URL https://cran. r―project. org/web/packages/rstan, 2020.
  31. Xu Guo and Bradley P Carlin. Separate and joint modeling of longitudinal and event time data using standard computer packages. The american statistician, 58(1):16–24, 2004.
  32. Simulation run length control in the presence of an initial transient. Operations Research, 31(6):1109–1144, 1983.
  33. Joint modelling of longitudinal measurements and event time data. Biostatistics, 1(4):465–480, 2000.
  34. The effect of improper priors on gibbs sampling in hierarchical linear mixed models. Journal of the American Statistical Association, 91(436):1461–1473, 1996.
  35. Bayesian survival analysis, volume 2. Springer, 2001.
  36. Handbook of survival analysis. CRC Press, 2016.
  37. Survival analysis a self-learning text. Springer, 1996.
  38. Bayesian p-splines. Journal of computational and graphical statistics, 13(1):183–212, 2004.
  39. The joint modeling of a longitudinal disease progression marker and the failure time process in the presence of cure. Biostatistics (Oxford, England), 3(4):547–563, December 2002.
  40. Tom Leonard. Density estimation, stochastic processes and prior information. Journal of the Royal Statistical Society: Series B (Methodological), 40(2):113–132, 1978.
  41. Generating random correlation matrices based on vines and extended onion method. Journal of multivariate analysis, 100(9):1989—2001, 2009.
  42. Modeling survival data: extending the cox model, 2002.
  43. A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease. PLoS computational biology, 16(12):e1008473, 2020.
  44. Joint analysis of longitudinal and survival aids data with a spatial fraction of long-term survivors: A bayesian approach. Biometrical journal, 59(6):1166–1183, 2017.
  45. Longitudinal data analysis. Wiley StatsRef: Statistics Reference Online, pages 1–28, 2006.
  46. Package ‘gmvjoint’. R CRAN, 2023.
  47. Ioannis Ntzoufras. Bayesian modeling using WinBUGS. John Wiley & Sons, 2011.
  48. Joint longitudinal and survival-cure models in tumour xenograft experiments. Statistics in Medicine, 33(18):3229–3240, August 2014.
  49. joiner: Joint modelling of repeated measurements and time-to-event data. Comprehensive R Archive Network, 2012.
  50. Martyn Plummer. Jags version 3.3. 0 user manual, 2012.
  51. Coda: convergence diagnosis and output analysis for mcmc. R news, 6(1):7–11, 2006.
  52. How many iterations in the gibbs sampler. Bayesian statistics, 4(2):763–773, 1992.
  53. Dimitris Rizopoulos. JM: An R package for the joint modelling of longitudinal and time-to-event data. Journal of Statistical Software, 35(9):1–33, 2010.
  54. Dimitris Rizopoulos. Joint models for longitudinal and time-to-event data: With applications in R. CRC press, 2012.
  55. Dimitris Rizopoulos. The r package jmbayes for fitting joint models for longitudinal and time-to-event data using mcmc. Journal of Statistical Software, 72(7):1–46, 2016.
  56. A bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event. Statistics in medicine, 30(12):1366–1380, 2011.
  57. Combining dynamic predictions from joint models for longitudinal and time-to-event data using bayesian model averaging. Journal of the American Statistical Association, 109(508):1385–1397, 2014.
  58. Jmbayes2: extended joint models for longitudinal and time-to-event data. R package version 0.3–0, ed, 2022.
  59. Package ‘jmbayes’. Journal of Statistical Software CRAN, 2020.
  60. Personalized screening intervals for biomarkers using joint models for longitudinal and survival data. Biostatistics, 17(1):149–164, 2016.
  61. Bayesian thinking in biostatistics. CRC Press, 2021.
  62. Sheldon M Ross. Simulation, 5th edision, 2012.
  63. Denisrustand/inlajoint: Joint modeling multivariate longitudinal and time-to-event outcomes with inla. Github, 2022.
  64. Debajyoti Sinha. Semiparametric bayesian analysis of multiple event time data. Journal of the American Statistical Association, 88(423):979–983, 1993.
  65. Brian J Smith. boa: an r package for mcmc output convergence assessment and posterior inference. Journal of statistical software, 21:1–37, 2007.
  66. Fully bayesian spline smoothing and intrinsic autoregressive priors. Biometrika, 90(2):289–302, 2003.
  67. Openbugs user manual. Version, 3(2):2007, 2007.
  68. R2openbugs: a package for running openbugs from r. R Package Version, pages 3–2, 2019.
  69. Package ‘r2jags’. R package version 0.03–08, URL http://CRAN. R-project. org/package= R2jags, 2015.
  70. Comparison of algorithms to generate event times conditional on time-dependent covariates. Statistics in Medicine, 27(14):2618–2634, 2008.
  71. Permalgo: Permutational algorithm to generate event times conditional on a covariate matrix including time-dependent covariates. R package version 1.1, URL https://CRAN.R-project.org/ package=PermAlgo., 2017.
  72. James Tanton. Encyclopedia of mathematics. Facts On File, Inc, 2005.
  73. Real-time individual predictions of prostate cancer recurrence using joint models. Biometrics, 69(1):206–213, 2013.
  74. Alexander Tsodikov. A Proportional Hazards Model Taking Account of Long-Term Survivors. Biometrics, 54(4):1508–1516, 1998.
  75. Bayesian regression modeling with INLA. CRC Press, 2018.
  76. Joint modelling of longitudinal and competing risks data. Statistics in medicine, 27(30):6426–6438, 2008.
  77. Semi-parametric joint modeling of survival and longitudinal data: the r package jsm. Journal of Statistical Software, 93:1–29, 2020.
  78. Joint longitudinal-survival-cure models and their application to prostate cancer. Statistica Sinica, 14(3):835–862, 2004.
  79. Individual Prediction in Prostate Cancer Studies Using a Joint Longitudinal Survival-Cure Model. Journal of the American Statistical Association, 103(481):178–187, 2008.
  80. Joint latent class trees: A tree-based approach to modeling time-to-event and longitudinal data. Statistical Methods in Medical Research, 31(4):719–752, 2022.
  81. Longitudinal analysis of the premature infant intestinal microbiome prior to necrotizing enterocolitis: a case-control study. PloS one, 10(3):e0118632, 2015.

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