The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study

Statistics in Medicine

Volumn 36, Issue 28, 2017, Pages 4514-4528

  Sweeting, Michael J ^a   Barrett, Jessica K ^a   Thompson, Simon G ^a   Wood, Angela M ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

C index; cardiovascular risk prediction; joint models; regression calibration; repeat measures

Indexed keywords

BIOLOGICAL MARKER;

ARTICLE; ATHEROSCLEROSIS; BLOOD PRESSURE MEASUREMENT; CALIBRATION; CARDIOVASCULAR DISEASE; CARDIOVASCULAR RISK; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DISEASE MARKER; INTERMETHOD COMPARISON; PREDICTION; PRIMARY MEDICAL CARE; REGRESSION ANALYSIS; REPEAT PROCEDURE; RISK ASSESSMENT; SIMULATION; STATISTICAL MODEL; SURVIVAL; SYSTOLIC BLOOD PRESSURE; BLOOD PRESSURE; COMPUTER SIMULATION; FEMALE; HUMAN; LONGITUDINAL STUDY; MALE; MIDDLE AGED; PROCEDURES; RISK FACTOR; STATISTICAL BIAS; STATISTICS AND NUMERICAL DATA; SURVIVAL ANALYSIS;

BIAS; BIOMARKERS; BLOOD PRESSURE; BLOOD PRESSURE DETERMINATION; CARDIOVASCULAR DISEASES; COMPUTER SIMULATION; FEMALE; HUMANS; LONGITUDINAL STUDIES; MALE; MIDDLE AGED; MODELS, STATISTICAL; REGRESSION ANALYSIS; RISK ASSESSMENT; RISK FACTORS; SURVIVAL ANALYSIS;

EID: 85034844728     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.7144     Document Type: Article

References (33)

1. The Emerging Risk Factors Collaboration. C-reactive protein, fibrinogen, and cardiovascular disease prediction. The New England Journal of Medicine 2012; 367(14):1310–1320.
2. Hippisley-Cox J, Coupland, Robson J, Brindle P. Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ 2010; 341:c6624.
3. Paynter NP, Crainiceanu CM, Sharrett AR, Chambless LE, Coresh J. Effect of correcting for long-term variation in major coronary heart disease risk factors: relative hazard estimation and risk prediction in the atherosclerosis risk in communities study. Annals of Epidemiology 2012; 22(3):191–197.
4. Chamnan P, Simmons RK, Sharp SJ, Khaw K-T, Wareham NJ, Griffin SJ. Repeat cardiovascular risk assessment after four years: is there improvement in risk prediction?PLoS ONE 2013; 11(2):e0147417.
5. Henderson R, Diggle P, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics 2000; 1(4):465–480.
6. Tsiatis AA, Davidian M. Joint modeling of longitudinal and time-to-event data: an overview. Statistica Sinica 2004; 14(3):809–834.
7. Rizopoulos D. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. CRC Press: Boca Raton, 2012.
8. Yang L, Yu M, Gao S. Prediction of coronary artery disease risk based on multiple longitudinal biomarkers. Statistics in Medicine 2016; 35(8):1299–1314.
9. van Houwelingen HC, Putter H. Dynamic Prediction in Clinical Survival Analysis. CRC Press: Boca Raton, 2011.
10. D'Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation 2008; 117(6):743–753.
11. Sweeting MJ, Thompson SG. Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture. Biometrical Journal 2011; 53(5):750–763.
12. Ye W, Lin X, Taylor JMG. Semiparametric modeling of longitudinal measurements and time-to-event dataa two-stage regression calibration approach. Biometrics 2008; 64(4):1238–1246.
13. Tsiatis AA, Degruttola V, Wulfsohn MS. Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association 1995; 90(429):27–37.
14. Bycott P, Taylor J. A comparison of smoothing techniques for CD4 data measured with error in a time-dependent Cox proportional hazards model. Statistics in Medicine 1998; 17(18):2061–2077.
15. Dafni UG, Tsiatis AA. Evaluating surrogate markers of clinical outcome when measured with error. Biometrics 1998; 54(4):1445–1462.
16. Wu MC, Carroll RJ. Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics 1988; 44(1):175–188.
17. Hogan JW, Laird NM. Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine 1997; 16(3):239–257.
18. Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 1996; 15(4):361–387.
19. Zheng Y, Heagerty PJ. Prospective accuracy for longitudinal markers. Biometrics 2007; 63(2):332–341.
20. Schoop R, Schumacher M, Graf E. Measures of prediction error for survival data with longitudinal covariates. Biometrical Journal 2011; 53(2):275–293.
21. Gerds TA, Schumacher M. Consistent estimation of the expected brier score in general survival models with right-censored event times. Biometrical Journal 2006; 48(6):1029–1040.
22. Royston P, Parmar MKB. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine 2002; 21(15):2175–2197.
23. Lambert PC, Royston P. Further development of flexible parametric models for survival analysis. Stata Journal 2009; 9(2):265–290.
24. Crowther MJ, Abrams KR, Lambert PC. Joint modeling of longitudinal and survival data. Stata Journal 2013; 13(1):165–184.
25. Newson RB. Comparing the predictive powers of survival models using Harrell's C or Somers' D. Stata Journal 2010; 10(3):339–358.
26. Gönen M, Heller G. Concordance probability and discriminatory power in proportional hazards regression. Biometrika 2005; 92(4):965–970.
27. White IR, Rapsomaniki E. Covariate-adjusted measures of discrimination for survival data. Biometrical Journal 2015; 57(4):592–613.
28. Allen NB, Siddique J, Wilkins JT, Shay C, Lewis CE, Goff DavidC, Jacobs DR, Liu K, Lloyd-Jones D. Blood pressure trajectories in early adulthood and subclinical atherosclerosis in middle age. Journal of the American Medical Association 2014; 311(5):490–497.
29. Allen N, Berry JD, Ning H, Van Horn L, Dyer A, Lloyd-Jones DM. Impact of blood pressure and blood pressure change during middle age on the remaining lifetime risk for cardiovascular disease: the cardiovascular lifetime risk pooling project. Circulation 2012; 125(1):37–44.
30. Taylor JM, Park Y, Ankerst DP, Proust-Lima C, Williams S, Kestin L, Bae K, Pickles T, Sandler H. Real-time individual predictions of prostate cancer recurrence using joint models. Biometrics 2013; 69(1):206–213.
31. Wolbers M, Koller MT, Witteman JCM, Steyerberg EW. Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology 2009; 20(4):555–561.
32. Williamson PR, Kolamunnage-Dona R, Philipson P, Marson AG. Joint modelling of longitudinal and competing risks data. Statistics in Medicine 2008; 27(30):6426–6438.
33. Sène M, Bellera CA, Proust-Lima C. Shared random-effect models for the joint analysis of longitudinal and time-to-event data: application to the prediction of prostate cancer recurrence. Journal de la Sociètè Française de Statistique 2014; 155(1):134–155.