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Original Research

Longitudinal FEV1 and Exacerbation Risk in COPD: Quantifying the Association Using Joint Modelling

, , , ORCID Icon, , , & ORCID Icon show all
Pages 101-111 | Published online: 15 Jan 2021

References

  • Global Initiative for Chronic Obstructive Lung Disease (GOLD). Global strategy for the diagnosis, management and prevention of COPD 2016. Available from: http://goldcopd.org/global-strategy-diagnosis-management-prevention-copd-2016/. Accessed December 31, 2020.
  • Donaldson GC, Seemungal TA, Bhowmik A, Wedzicha JA. Relationship between exacerbation frequency and lung function decline in chronic obstructive pulmonary disease. Thorax. 2002;57(10):847–852. doi:10.1136/thorax.57.10.847
  • Hoogendoorn M, Feenstra TL, Hoogenveen RT, Al M, Molken MR. Association between lung function and exacerbation frequency in patients with COPD. Int J Chron Obstruct Pulmon Dis. 2010;5:435–444. doi:10.2147/COPD.S13826
  • Zider AD, Wang X, Buhr RG, Sirichana W, Barjaktarevic IZ, Cooper CB. Reduced COPD Exacerbation Risk Correlates With Improved FEV1: A Meta-Regression Analysis. Chest. 2017;152(3):494–501. doi:10.1016/j.chest.2017.04.174
  • Ribbing J, Korell J, Cerasoli F, Milligan P, Martin S, O. Karlsson M. Predicting Reductions in Chronic Obstructive Pulmonary Disease (COPD) Exacerbations from FEV1-A Model-Based Meta-Analysis of Literature Data from Controlled Randomized Clinical Trials. J Pharmacokinet Pharmacodyn. 2015;42:S63S63.
  • Donohue JF, Jones PW, Bartels C, et al. Correlations between FEV1 and patient-reported outcomes: A pooled analysis of 23 clinical trials in patients with chronic obstructive pulmonary disease. Pulm Pharmacol Ther. 2018;49:11–19. doi:10.1016/j.pupt.2017.12.005
  • Jones PW, Donohue JF, Nedelman J, Pascoe S, Pinault G, Lassen C. Correlating changes in lung function with patient outcomes in chronic obstructive pulmonary disease: a pooled analysis. Respir Res. 2011;12:161. doi:10.1186/1465-9921-12-161
  • Facius A, Krause A, Claret L, Bruno R, Lahu G. Modeling and Simulation of Pivotal Clinical Trials Using Linked Models for Multiple Endpoints in Chronic Obstructive Pulmonary Disease With Roflumilast. J Clin Pharmacol. 2017;57(8):1042–1052. doi:10.1002/jcph.885
  • Calverley PM, Eriksson G, Jenkins CR, et al. Early efficacy of budesonide/formoterol in patients with moderate-to-very-severe COPD. Int J Chron Obstruct Pulmon Dis. 2016;12:13–25. doi:10.2147/COPD.S114209
  • Ibrahim JG, Chu H, Chen LM. Basic concepts and methods for joint models of longitudinal and survival data. J Clin Oncol. 2010;28(16):2796–2801. doi:10.1200/JCO.2009.25.0654
  • Tardivon C, Desmée S, Kerioui M, et al. Association between tumor size kinetics and survival in advanced urothelial carcinoma patients treated with atezolizumab: implication for patient’s follow-up. Population Analysis Group Europe (PAGE) 28; 2019. Available from: www.page-meeting.org/?abstract=8824. Accessed December 31, 2020.
  • Lim HJ, Mondal P, Skinner S. Joint modeling of longitudinal and event time data: application to HIV study. J Med Stat Inf. 2013;1(1). doi:10.7243/2053-7662-1-1
  • Rennard SI, Tashkin DP, McElhattan J, et al. Efficacy and tolerability of budesonide/formoterol in one hydrofluoroalkane pressurized metered-dose inhaler in patients with chronic obstructive pulmonary disease: results from a 1-year randomized controlled clinical trial. Drugs. 2009;69(5):549–565. doi:10.2165/00003495-200969050-00004
  • Tashkin DP, Rennard SI, Martin P, et al. Efficacy and safety of budesonide and formoterol in one pressurized metered-dose inhaler in patients with moderate to very severe chronic obstructive pulmonary disease: results of a 6-month randomized clinical trial. Drugs. 2008;68(14):1975–2000. doi:10.2165/00003495-200868140-00004
  • Sharafkhaneh A, Southard JG, Goldman M, Uryniak T, Martin UJ. Effect of budesonide/formoterol pMDI on COPD exacerbations: a double-blind, randomized study. Respir Med. 2012;106(2):257–268. doi:10.1016/j.rmed.2011.07.020
  • Rizopoulos D. JM: an R Package for the Joint Modelling of Longitudinal and Time-to-Event Data. J Stat Softw. 2010;35(9):1–33. doi:10.18637/jss.v035.i09
  • R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, Vienna, Austria; 2016.
  • Gavrilov S, Zhudenkov K, Peskov K, Helmlinger G, Aksenov S Longitudinal assessment of tumor size and neutrophil count in multivariate joint models are more predictive of survival than their baseline values in patients with non-small cell lung cancer. Population Analysis Group Europe (PAGE) 28; 2019. Available from: www.page-meeting.org/?abstract=9172. Accessed December 31, 2020.
  • Alsefri M, Sudell M, García-Fiñana M, Kolamunnage-Dona R. Bayesian joint modelling of longitudinal and time to event data: a methodological review. BMC Med Res Methodol. 2020;20(1):94. doi:10.1186/s12874-020-00976-2
  • Musuamba FT, Teutonico D, Maas HJ, et al. Prediction of disease progression, treatment response and dropout in chronic obstructive pulmonary disease (COPD). Pharm Res. 2015;32(2):617–627. doi:10.1007/s11095-014-1490-4
  • Bell ML, Kenward MG, Fairclough DL, Horton NJ. Differential dropout and bias in randomised controlled trials: when it matters and when it may not. BMJ. 2013;346:e8668. doi:10.1136/bmj.e8668
  • Vestbo J, Anderson JA, Calverley PM, et al. Bias due to withdrawal in long-term randomised trials in COPD: evidence from the TORCH study. Clin Respir J. 2011;5(1):44–49. doi:10.1111/j.1752-699X.2010.00198.x
  • Król A, Palmér R, Rondeau V, Rennard S, Eriksson UG, Jauhiainen A. Improving the evaluation of COPD exacerbation treatment effects by accounting for early treatment discontinuations: a post-hoc analysis of randomized clinical trials. Respir Res. 2020;21(1):158. doi:10.1186/s12931-020-01419-8