The joint modeling approach with a simulation study for evaluating the association between the trajectory of serum albumin levels and mortality in peritoneal dialysis patients

Year 2022, Volume: 51 Issue: 3, 900 - 913, 01.06.2022

Merve Başol Göksülük Dinçer Göksülük Murat Sipahioğlu A. Ergun Karaağaoğlu

https://doi.org/10.15672/hujms.884326

Abstract

We aimed to study the association between mortality and trajectory of serum albumin levels (g/dL) in peritoneal dialysis patients via a joint modeling approach. Joint modeling is a statistical method used to evaluate the relationship between longitudinal and time-to-event processes by fitting both sub-models simultaneously. A comprehensive simulation study was conducted to evaluate model performances and generalize the findings to more general scenarios. Model performances and prediction accuracies were evaluated using the time-dependent ROC area under the curve (AUC) and Brier score (BS). According to the real-life dataset results, the trajectory of serum albumin levels was inversely associated with mortality increasing the risk of death 2.21 times (p=0.003). The simulation results showed that the model performances increased with sample size. However, the model complexity had increased as more repeated measurements were taken from patients and resulted in lower prediction accuracy unless the sample size was increased. In conclusion, using the trajectory of risk predictors rather than baseline (or averaged) values provided better predictive accuracy and prevented biased results. Finally, the study design (e.g., number of samples and repeated measurements) should be carefully defined since it played an important role in model performances.

Keywords

joint modeling, longitudinal model, survival model, personalized medicine, simulation

References

• [1] E.R. Andrinopoulou and D. Rizopoulos, Bayesian shrinkage approach for a joint model of longitudinal and survival outcomes assuming different association structures, Stat. Med. 35 (26), 4813-4823, 2016.
• [2] M. Basol, D. Goksuluk, M.H. Sipahioglu and E. Karaagaoglu, Effect of serum albumin changes on mortality in patients with peritoneal dialysis: a joint modeling approach and personalized dynamic risk predictions, Biomed Res. Int. 2021, 6612464, 2021.
• [3] D.R. Cox, Regression models and life tables (with discussion), J. R. Stat. Soc. Ser. B. Stat. Methodol. 34, 187-200, 1972.
• [4] S. Desmee, F. Mentre, C. Veyrat-Follet, B. Sebastien and J. Guedj, Nonlinear joint models for individual dynamic prediction of risk of death using Hamiltonian Monte Carlo: application to metastatic prostate cancer, BBMC Med. Res. Methodol. 17 (105), 1-12, 2017.
• [5] R.M. Elashoff, G. Li and N. Li, Joint Modelling of Longitudinal and Time-to-Event Data, CRC Press. Taylor & Francis Group, 2017.
• [6] C. Faucett and D. Thomas, Simultaneously modelling censored survival data and repeatedly measured covariates: A Gibbs sampling approach, Stat. Med. 15 (15), 1663- 1685, 1996.
• [7] T. Gerds and M. Schumacher, Consistent estimation of the expected Brier score in general survival models with right-censored event times, Biom. J. 48 (6), 1029-1040, 2006.
• [8] A.L. Gould, M.E. Boye, M.J. Crowther, J.G. Ibrahim, G. Quartey, S. Micallef and F.Y. Bois, Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group, Stat. Med. 34 (14), 2181-2195, 2015.
• [9] I. Guler, C. Faes, C. Cadarso-Suarez, L. Teixeira, A. Rodrigues and D. Mendonca, Two-stage model for multivariate longitudinal and survival data with application to nephrology research, Biom. J. 59 (6), 1204-1220, 2017.
• [10] D. Hedeker and R.D. Gibbons, Longitudinal Data Analysis, John Wiley & Sons Inc, 2006.
• [11] R. Henderson, P. Diggle and A. Dobson, Joint modelling of longitudinal measurements and event time data, Biostatistics 1 (4), 465-448, 2000.
• [12] J.G. Ibrahim, H. Chu and L.M. Chen, Basic concepts and methods for joint models of longitudinal and survival data, Kidney Res Clin Pract. 28 (16), 2796-2801, 2010.
• [13] M. Khoshhali, I. Kazemi, S.M. Hoseeini and S. Sierafian, Predicting tree-year clinical outcomes using the baseline and trajectories of serum albumin in patients on peritoneal dialysis, Iran Red Crescent Med J 19 (10), 2017.
• [14] K. Lange, Optimization, Springer-Verlag, 2004.
• [15] P.K.T. Li, K.M. Chow, M.W.M. Van de Luijtgaarden, D.W. Johnson, K.J. Jager, R. Mehrotra, S. Naicker, R. Pecoits-Filho, X.Q. Yu and N. Lameire, Changes in the worldwide epidemiology of peritoneal dialysis, Nat. Rev. Nephrol. 13 (2), 90-103, 2016.
• [16] L.M. McCrink, A.H. Marshall and K.J. Cairns, Advances in joint modelling: a review of recent developments with application to the survival of end stage renal disease patients, Int. Stat. Rev. 81 (2), 249-269, 2013.
• [17] L.C. Proust and P. Blanche, Dynamic Predictions, Wiley StatsRef-Statistics Reference Online, 2016.
• [18] L.C. Proust and J. Taylor, 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, 2009.
• [19] R Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2021. URL https://www.R-project.org/.
• [20] D. Rizopoulos, JM: an R package for the joint modelling of longitudinal and time-to- event data, J. Stat. Softw. 35 (9), 1-33, 2010.
• [21] D. Rizopoulos, Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data, Biometrics 67 (3), 819 - 829,2011.
• [22] D. Rizopoulos, Joint Models for Longitudinal and Time-to-Event Data with Applications in R., Chapman and Hall/CRC Biostatistics Series: Boca Raton, 2012.
• [23] D. Rizopoulos, J.M. Taylor, J.V. Rosmalen, E.W. Steyerberg and J.J.M. Takkenberg, Personalized screening intervals for biomarkers using joint models for longitudinal and survival data, Biostatistics 17 (1), 149-164, 2016.
• [24] A. Sayers, J. Heron, A.D.A.C. Smith, C. Macdonald-Wallis, M.S. Gilthorpe, F. Steele and K. Tilling, Joint modelling compared with two stage methods for analysing longitudinal data and prospective outcomes: A simulation study of childhood growth and BP, Stat. Methods Med. Res. 26 (1), 437-452, 2017.
• [25] M.D. Schluchter, Methods for the analysis of informatively censored longitudinal data, Stat. Med. 11 (14-15), 1861-1870, 1992.
• [26] R. Schoop, E. Graf and M. Schumacher, Quantifying the predictive performance of prognostic models for censored survival data with time-dependent covariates, Biometrics 64 (2), 603-610, 2008.
• [27] M.H. Sipahioglu, A. Aybal, A. Unal, B. Tokgoz, O. Oymak and C. Utas, Patient and technique survival and factors affecting mortality on peritoneal dialysis in Turkey: 12 years experience in a single center, Perit Dial Int. 28 (3), 238-245, 2008.
• [28] I. Sousa, A review on joint modelling of longitudinal measurements and time-to-event, Revstat Stat. J. 9 (1), 57-81, 2011.
• [29] N. Tekkarismaz and D. Torun, Long-term clinical outcomes of peritoneal dialysis patients: 9-year experience of a single centre in Turkey, Turk. J. Med. Sci. 50 (2), 386-397, 2020.
• [30] A.A. Tsiatis, Joint modeling of longitudinal and time-to-event data: an overview, SStatist. Sinica 14 (3), 809-834, 2004.
• [31] X. Wang, Q. Han, T. Wang and W. Tang, Serum albumin changes and mortality risk of peritoneal dialysis patients, Int Urol Nephrol 52 (3), 565-571, 2020.
• [32] H. Wickham, ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag, New York, 2016.
• [33] L. Wu, W. Liu, G.Y. Yi and Y. Huang, Analysis of longitudinal and survival data: Joint modeling, inference methods, and issues, J. Probab. Stat. 2012, 640153, 2012.
• [34] M. Wulfsohn and A.A. Tsiatis, A joint model for survival and longitudinal data measured with error, Biometrics 53 (1), 330-339, 1997.