The online version of this article (doi:10.1186/1471-2288-13-146) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
All authors were involved in conception and design of the project. MJC conducted the simulation study, analysed the clinical dataset and wrote the first draft of the manscript. PCL and KRA both revised the manuscript. All authors read and approved the final manuscript.
Methodological development of joint models of longitudinal and survival data has been rapid in recent years; however, their full potential in applied settings are yet to be fully explored. We describe a novel use of a specific association structure, linking the two component models through the subject specific intercept, and thus extend joint models to account for measurement error in a biomarker, even when only the baseline value of the biomarker is of interest. This is a common occurrence in registry data sources, where often repeated measurements exist but are simply ignored.
The proposed specification is evaluated through simulation and applied to data from the General Practice Research Database, investigating the association between baseline Systolic Blood Pressure (SBP) and the time-to-stroke in a cohort of obese patients with type 2 diabetes mellitus.
By directly modelling the longitudinal component we reduce bias in the hazard ratio for the effect of baseline SBP on the time-to-stroke, showing the large potential to improve on previous prognostic models which use only observed baseline biomarker values.
The joint modelling of longitudinal and survival data is a valid approach to account for measurement error in the analysis of a repeatedly measured biomarker and a time-to-event. User friendly Stata software is provided.
Conroy RM, Pyorala K, Fitzgerald AP, Sans S, Menotti A, De Backer G, De Bacquer D, Ducimetiere P, Jousilahti P, Keil U, Njolstad I, Oganov RG, Thomsen T, Tunstall-Pedoe H, Tverdal A, Wedel H, Whincup P, Wilhelmsen L, Graham IM, SCOREpg: Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003, 24 (11): 987-1003. 10.1016/S0195-668X(03)00114-3. CrossRefPubMed
Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P: Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ. 2007, 335 (7611): 136-10.1136/bmj.39261.471806.55. [ http://dx.doi.org/10.1136/bmj.39261.471806.55], CrossRefPubMedPubMedCentral
Ara R, Blake L, Gray L, Hernandez M, Crowther M, Dunkley A, Warren F, Jackson R, Rees A, Stevenson M, Abrams K, Cooper N, Davies M, Khunti K, Sutton A: What is the clinical effectiveness and cost-effectiveness of using drugs in treating obese patients in primary care? A systematic review. Health Technol Assess. 2012, 16 (5): 1-202. [ http://dx.doi.org/10.3310/hta16050], CrossRef
Philipson P, Sousa I, Diggle P, Williamson P, Kolamunnage-Dona R, Henderson R: joineR - Joint modelling of repeated measurements and time-to-event data. 2012, [ http://cran.r-project.org/web/packages/joineR/index.html],
Crowther MJ, Abrams KR, Lambert PC: Joint modeling of longitudinal and survival data. Stata J. 2013, 13: 165-184.
Wolbers M, Babiker A, Sabin C, Young J, Dorrucci M, Chêne G, Mussini C, Porter K, Bucher HC, CASCADE: Pretreatment CD4 cell slope and progression to AIDS or death in HIV-infected patients initiating antiretroviral therapy–the CASCADE collaboration: a collaboration of 23 cohort studies. PLoS Med. 2010, 7 (2): e1000239-10.1371/journal.pmed.1000239. [ http://dx.doi.org/10.1371/journal.pmed.1000239], CrossRefPubMedPubMedCentral
Royston P, Lambert PC: Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model. 2011, College Station: Stata Press
Cox DR: Regression models and life-tables. J R Stat Soc Ser B Methodological. 1972, 34 (2): 187-220.
Hsieh F, Tseng YK, Wang JL: Joint modeling of survival and longitudinal data: likelihood approach revisited. Biometrics. 2006, 62 (4): 1037-1043. 10.1111/j.1541-0420.2006.00570.x. [ http://dx.doi.org/10.1111/j.1541-0420.2006.00570.x], CrossRefPubMed
Rutherford MJ, Crowther MJ, Lambert PC: The use of restricted cubic splines to approximate complex hazard functions in the analysis of time-to-event data: a simulation study. J Stat Comput Simul. 2013, [ http://www.tandfonline.com/doi/abs/10.1080/00949655.2013.845890],
Rizopoulos D: Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics. 2011, 67 (3): 819-829. 10.1111/j.1541-0420.2010.01546.x. [ http://dx.doi.org/10.1111/j.1541-0420.2010.01546.x], CrossRefPubMed
Rothwell PM, Howard SC, Dolan E, O’Brien E, Dobson JE, Dahlöf B, Sever PS, Poulter NR: Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet. 2010, 375 (9718): 895-905. 10.1016/S0140-6736(10)60308-X. [ http://dx.doi.org/10.1016/S0140-6736(10)60308-X], CrossRefPubMed
Zucker DM: A pseudo partial likelihood method for semiparametric survival regression with covariate errors. J Am Stat Assoc. 2005, 100 (472): 1264-1277. 10.1198/016214505000000538. [ http://www.tandfonline.com/doi/abs/10.1198/016214505000000538], CrossRef
Liao X, Zucker DM, Li Y, Spiegelman D: Survival analysis with error-prone time-varying covariates: a risk set calibration approach. Biometrics. 2011, 67: 50-58. 10.1111/j.1541-0420.2010.01423.x. [ http://dx.doi.org/10.1111/j.1541-0420.2010.01423.x], CrossRefPubMedPubMedCentral
Crowther MJ: STJM: Stata module to fit shared parameter joint models of longitudinal and survival data. Stat Softw Components Boston Coll Dep Econ. 2012, [ http://ideas.repec.org/c/boc/bocode/s457339.html],
- Adjusting for measurement error in baseline prognostic biomarkers included in a time-to-event analysis: a joint modelling approach
Michael J Crowther
Paul C Lambert
Keith R Abrams
- BioMed Central
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