Personalized screening intervals for biomarkers using joint models for longitudinal and survival data

Biostatistics

Volumn 17, Issue 1, 2016, Pages 149-164

  Rizopoulos, Dimitris ^a   Taylor, Jeremy M G ^b   Van Rosmalen, Joost ^a   Steyerberg, Ewout W ^a   Takkenberg, Johanna J M ^a  

^a ERASMUS MEDICAL CENTER   (Netherlands)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Decision making; Information theory; Personalized medicine; Random effects.

Indexed keywords

BIOLOGICAL MARKER;

CLINICAL DECISION MAKING; HUMAN; PERSONALIZED MEDICINE; PROCEDURES; STATISTICAL MODEL;

BIOMARKERS; CLINICAL DECISION-MAKING; HUMANS; MODELS, STATISTICAL; PRECISION MEDICINE;

EID: 84959881079     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxv031     Document Type: Article

References (22)

1. ANDRINOPOULOU, E. R., RIZOPOULOS, D., TAKKENBERG, J. AND LESAFFRE, E. (2014). Joint modeling of two longitudinal outcomes and competing risk data. Statistics in Medicine 33, 3167-3178.
2. BEKKERS, J., KLIEVERIK, L., RAAP, G., TAKKENBERG, J. AND BOGERS, A. (2011). Re-operations for aortic allograft root failure: experience from a 21-year single-center prospective follow-up study. European Journal of Cardio-Thoracic Surgery 40, 35-42.
3. CHEN, M.-H., HUANG, L., IBRAHIM, J. AND KIM, S. (2008). Bayesian variable selection and computation for generalized linear models with conjugate priors. Bayesian Analysis 3, 585-614.
4. CLYDE, M. AND CHALONER, K. (1996). The equivalence of constrained and weighted designs in multiple objective design problems. Journal of the American Statistical Association 91, 1236-1244.
5. COMMENGES, D., LIQUET, B. AND PROUST-LIMA, C. (2012). Choice of prognostic estimators in joint models by estimating differences of expected conditional Kullback-Leibler risks. Biometrics 68, 380-387.
6. COOK, D. AND WONG, W. K. (1994). On the equivalence of constrained and compound optimal designs. Journal of the American Statistical Association 89, 687-692.
7. COVER, T. AND THOMAS, J. (1991) Elements of Information Theory. New York: Wiley.
8. EILERS, P. AND MARX, B. (1996). Flexible smoothing with b-splines and penalties. Statistical Science 11, 89-121.
9. LEE, S. AND ZELEN, M. (1998). Scheduling periodic examinations for the early detection of disease: Applications to breast cancer. Journal of the American Statistical Association 93, 1271-1281.
10. PARMIGIANI, G. (1993). On optimal screening ages. Journal of the American Statistical Association 88, 622-628.
11. PARMIGIANI, G. (1998). Designing observation times for interval censored data. Sankhya: The Indian Journal of Statistics 60, 446-458.
12. PARMIGIANI, G. (1999). Decision model in screening for breast cancer. In: Bernardo, J. M., Berger, J. O., Dawid, A. P. and Smith, A. F. M. (editors), Bayesian Statistics 6. Oxford: Oxford University Press, pp. 525-546.
13. PARMIGIANI, G. (2002) Modeling in Medical Decision Making: A Bayesian Approach. New York: Wiley.
14. RIZOPOULOS, D. (2011). Dynamic predictions and prospective accuracy in joint models for longitudinal and time-Toevent data. Biometrics 67, 819-829.
15. RIZOPOULOS, D. (2012) Joint Models for Longitudinal and Time-To-Event Data, with Applications in R. Boca Raton: Chapman & Hall/CRC.
16. RIZOPOULOS, D. AND GHOSH, P. (2011). A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-To-event. Statistics in Medicine 30, 1366-1380.
17. RIZOPOULOS, D., HATFIELD, L., CARLIN, B. AND TAKKENBERG, J. (2014). Combining dynamic predictions from joint models for longitudinal and time-To-event data using Bayesian model averaging. Journal of the American Statistical Association 109, 1385-1397.
18. SCHAEFER, A., BAILEY, M., SHECHTER, S. AND ROBERTS, M. (2004). Modeling medical treatment using Markov decision processes. In: Brandeau, M., Sainfort, F. and Pierskalla, W. (editors), Operations Research and Health Care. Berlin: Springer, pp. 593-612.
19. TAYLOR, J. M. G., PARK, Y., ANKERST, D., PROUST-LIMA, C., WILLIAMS, S., KESTIN, L., BAE, K., PICKLES, T. AND SANDLER, H. (2013). Real-Time individual predictions of prostate cancer recurrence using joint models. Biometrics 69, 206-213.
20. TSIATIS, A. A. AND DAVIDIAN, M. (2004). Joint modeling of longitudinal and time-To-event data: An overview. Statistica Sinica 14, 809-834.
21. VERDINELLI, I. AND KADANE, J. B. (1992). Bayesian designs for maximizing information and outcome. Journal of the American Statistical Association 87, 510-515.
22. YU, M., TAYLOR, J. M. G. AND SANDLER, H. (2008). Individualized prediction in prostate cancer studies using a joint longitudinal-survival-cure model. Journal of the American Statistical Association 103, 178-187.