Hierarchical Changepoint Models for Biochemical Markers Illustrated by Tracking Postradiotherapy Prostate-Specific Antigen Series in Men With Prostate Cancer

Annals of Epidemiology

Volumn 18, Issue 4, 2008, Pages 270-282

  Bellera, Carine A ^a,b   Hanley, James A ^b   Joseph, Lawrence ^b   Albertsen, Peter C ^c  

^a INSTITUT BERGONIÉ   (France)

^b MCGILL UNIVERSITY   (Canada)

^c UNIVERSITY OF CONNECTICUT HEALTH CENTER   (United States)

Author keywords

Bayesian Models; Biomarkers; Hierarchical Changepoint Models; Longitudinal Data; Prostate Cancer; PSA; Radiotherapy

Indexed keywords

BIOLOGICAL MARKER; PROSTATE SPECIFIC ANTIGEN;

AGED; ARTICLE; BAYES THEOREM; CANCER RADIOTHERAPY; GLEASON SCORE; HUMAN; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MALE; PRIORITY JOURNAL; PROSTATE CANCER; STATISTICAL ANALYSIS;

AGED; BAYES THEOREM; BIOLOGICAL MARKERS; COHORT STUDIES; CONNECTICUT; DATA INTERPRETATION, STATISTICAL; DISEASE PROGRESSION; HUMANS; MALE; MODELS, STATISTICAL; PROSTATE-SPECIFIC ANTIGEN; PROSTATIC NEOPLASMS;

EID: 40949106465     PISSN: 10472797     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.annepidem.2007.10.006     Document Type: Article

References (35)

1. Carlin B., Gelfand A., and Smith A. Hierarchical Bayesian analysis of changepoint problems. Appl Stat 41 (1992) 389-405
2. Stephens D. Bayesian retrospective multiple-changepoint identification. Appl Stat 43 (1994) 159-178
3. Lange N., Carlin B., and Gelfand A. Hierarchical Bayes models for the progression of HIV infection using longitudinal CD4 T-cell numbers. J Am Stat Assoc 87 (1992) 615-632
4. Ghosh P., and Vaida F. Random changepoint modelling of HIV immunologic responses. Stat Med 26 (2007) 2074-2087
5. Slate E., and Lark L. Using PSA to detect prostate cancer onset: an application of Bayesian retrospective and prospective changepoint identification. In: Gatsonis C., Kass R., Carlin B., et al. (Eds). Case Studies in Bayesian Statistics volume 4 (1999), Springer, New York 395-412
6. Slate E., and Turnbull B. Statistical models for longitudinal biomarkers of disease onset. Stat Med 19 (2000) 617-637
7. Inoue L., Etzioni R., Slate E., Morrell C., and Penson D. Combining longitudinal studies of PSA. Biostatistics 5 (2004) 483-500
8. Slate E., and Cronin K. Changepoint modeling of longitudinal PSA as biomarker for prostate cancer. In: Gatsonis C., Hodges J., Kass R., et al. (Eds). Case Studies in Bayesian Statistics volume 3 (1999), Springer, New York 435-456
9. Pauler D., and Finkelstein D. Predicting time to prostate cancer recurrence based on joint models for non-linear longitudinal biomarkers and event time outcome. Stat Med 21 (2002) 3897-3911
10. Joseph L., Wolfson D., Belisle P., et al. Taking account of between-patient variability when modeling decline in Alzheimer's disease. Am J Epidemiol 149 (1999) 10963-10973
11. Eastham J., Riedel E., Scardino P., et al. Variation of serum prostate-specific antigen levels. JAMA 289 (2003) 2695-2700
12. Ballentine Carter H., Pearson J., Metter J., et al. Longitudinal evaluation of prostate-specific antigen levels in men with and without prostate disease. JAMA 267 (1992) 2215-2220
13. Albertsen P., Hanley J., Penson D., et al. Validation of increasing prostate specific antigen as a predictor of prostate cancer death after treatment of localized cancer with surgery or radiation. J. Urol 171 (2004) 2221-2225
14. Schmid H., McNeal J., and Stamey T. Observations on the doubling time of prostate cancer. The use of serial prostate-specific antigen in patients with untreated disease as a measure of increasing volume. Cancer 71 (1993) 2031-2040
15. Meek A., Park T., Oberman E., and Wielopolski L. A prospective study of prostate specific antigen levels in patients receiving radiotherapy for localized carcinoma of the prostate. Int J Radiat Oncol Biol Phys 19 (1990) 733-741
16. nd ed. (2004), Chapman and Hall, New York
17. Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Anal 1 (2006) 515-533
18. Spiegelhalter D., Thomas A., Best N., and Gilks W. WinBUGS: Bayesian inference using Gibbs sampling, version 1.3 (2000), MRC Biostatistics Unit, Cambridge, MA
19. D'Amico A., Cote K., Loffredo, et al. Determinants of prostate cancer specific survival following therapy during the prostate specific antigen era. J. Urol 170 (2003) S42-S47
20. Zagars G., and Pollack A. The fall and rise of prostate-specific antigen: kinetics of serum prostate specific antigen levels after radiation therapy for prostate cancer. Cancer 72 (1993) 832-842
21. Pollack A., Zagars G., and Kavadi V. Prostate specific antigen doubling time and disease relapse after radiotherapy for prostate cancer. Cancer 74 (1994) 670-678
22. Ulm K. A statistical method for assessing a threshold in epidemiological studies. Stat Med 10 (1991) 341-349
23. American Society for Therapeutic Radiology and Oncology Consensus Panel. Consensus statement: Guidelines for PSA following radiation therapy. Int J Radiat Oncol Biol Phys 37 (1997) 1035-1041
24. Hanlon A., Diratzouian H., and Hanks G. Posttreatment prostate-specific antigen nadir highly predictive of distant failure and death from prostate cancer. Int J Radiat Oncol Biol Phys 53 (2002) 297-303
25. DeWitt K., Sandler H., Weinberg V., McLaughlin P., and Roach M. What does post-radiotherapy PSA nadir tell us about freedom from PSA failure and progression-free survival in patients with low and intermediate-risk localized prostate cancer?. Urology 62 (2003) 492-496
26. Kaminski J., Hanlon A., Horwitz E., Pinover W., Mitra R., and Hanks G. Relationship between prostate volume, prostate-specific antigen nadir, and biochemical control. Int J Radiat Oncol Biol Phys 52 (2002) 888-892
27. Critz F., Levinson K., Williams W., Holladay D., Griffin V., and Holladay C. Prostate specific antigen nadir achieved by men apparently cured of prostate cancer by radiotherapy. J Urol 161 (1999) 1199-1205
28. Hanlon A., Moore D., and Hanks G. Modeling postradiation prostate specific antigen level kinetics. Cancer 83 (1998) 130-134
29. Guess B., Jennrich R., Johnson H., Refheffer R., and Scholz M. Using splines to detect changes in PSA doubling times. Prostate 54 (2003) 88-94
30. Law N., Taylor J., and Sandler H. The joint modeling of longitudinal disease progression marker and the failure time process in the presence of cure. Biostatistics 3 (2002) 547-563
31. Taylor J., Yu M., and Sandler H. Individualized predictions of disease progression following radiotherapy for prostate cancer. J Clin Oncol 23 (2005) 816-825
32. Hinkley D. Inference about the change-point in a sequence of random variables. Biometrika 57 (1970) 1-17
33. Hinkley D. Inference about the intersection in two-phase regression. Biometrika 56 (1969) 495-504
34. Morrell C., Pearson J., Ballentine Carter H., et al. Estimating unknown transition times using a piecewise nonlinear mixed-effects model in men with prostate cancer. J Am Stat Assoc 90 (1995) 45-53
35. Pearson J., Morrell C., Landis P., Ballentine Carter H., and Brant L. Mixed-effects regression models for studying the natural history of prostate disease. J Am Stat Assoc 13 (1994) 587-601