Detecting trends in noisy data series: Application to biomarker series

American Journal of Epidemiology

Volumn 167, Issue 9, 2008, Pages 1130-1139

  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 hierarchical model; Changepoint; Markov chain Monte Carlo; Noise; Prostate specific antigen; Sensitivity and specificity; Simulation; Trend analysis

Indexed keywords

BIOLOGICAL MARKER; PROSTATE SPECIFIC ANTIGEN;

ANTIGEN; BAYESIAN ANALYSIS; BIOMARKER; DATA SET; EMPIRICAL ANALYSIS; HEALTH STATUS; HIERARCHICAL SYSTEM; MARKOV CHAIN; MONTE CARLO ANALYSIS; SENSITIVITY ANALYSIS; TREND ANALYSIS;

ACCURACY; ARTICLE; BAYES THEOREM; CANCER RADIOTHERAPY; DATA ANALYSIS; DISEASE MARKER; EMPIRICISM; FOLLOW UP; METHODOLOGY; MONTE CARLO METHOD; NOISE; OUTCOME ASSESSMENT; PROBABILITY; PROSTATE CANCER; RANDOMIZATION; SENSITIVITY AND SPECIFICITY; SIMULATION; TREATMENT FAILURE; TREND STUDY; VARIANCE;

AGED; BAYES THEOREM; CONNECTICUT; DECISION SUPPORT TECHNIQUES; HUMANS; LINEAR MODELS; MALE; MARKOV CHAINS; PROSTATE-SPECIFIC ANTIGEN; PROSTATIC NEOPLASMS; REGISTRIES; RETROSPECTIVE STUDIES; SENSITIVITY AND SPECIFICITY; TUMOR MARKERS, BIOLOGICAL;

EID: 42649139570     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwn003     Document Type: Article

References (38)

1. Catalona W, Smith D, Ratliff T, et al. Measurement of prostate-specific antigen in serum as a screening test for prostate cancer. N Engl J Med 1991;324:1156-61.
2. D'Amico A, Cote K, Loffredo M, et al. Determinants of prostate cancer specific survival following therapy during the prostate specific antigen era. J Urol 2003;170(pt 2):S42-7.
3. Sartor C, Strawderman M, Lin X, et al. Rate of PSA rise predicts metastatic versus local recurrence after definitive radiotherapy. Int J Radiat Oncol Biol Phys 1997;38:941-7.
4. Rustin GJ, Nelstrop AE, Tuxen MK, et al. Defining progression of ovarian carcinoma during follow-up according to CA 125: a North Thames Ovary Group Study. Ann Oncol 1996;7:361-4.
5. Bast RC Jr, Ravdin P, Hayes DF, et al. 2000 update of recommendations for the use of tumor markers in breast and colorectal cancer: clinical practice guidelines of the American Society of Clinical Oncology. J Clin Oncol 2001;19:1865-78.
6. American Society for Therapeutic Radiology and Oncology Consensus Panel. Consensus statement: guidelines for PSA following radiation therapy. Int J Radiat Oncol Biol Phys 1997;37:1035-41.
7. Thames H, Kuban D, Levy L, et al. Comparison of alternative biochemical failure definitions based on clinical outcome in 4839 prostate cancer patients treated by external beam radiotherapy between 1986 and 1995. Int J Radiat Oncol Biol Phys 2003;57:929-43.
8. Buyyounouski M, Hanlon A, Eisenberg D, et al. Defining biochemical failure after radiotherapy with and without androgen deprivation for prostate cancer. Int J Radiat Oncol Biol Phys 2005;63:1455-62.
9. Taylor J, Griffith K, Sandler H. Definitions of biochemical failure in prostate cancer following radiation therapy. Int J Radiat Oncol Biol Phys 2001;50:1212-19.
10. Eastham J, Riedel E, Scardino P, et al. Variation of serum prostate-specific antigen levels. JAMA 2003;289:2695-700.
11. Ballentine Carter H, Pearson J, Metter J, et al. Longitudinal evaluation of prostate-specific antigen levels in men with and without prostate disease. JAMA 1992;267:2215-20.
12. Shipley W, Thames H, Sandler H, et al. Radiation therapy for clinically localized prostate cancer: a multi-institutional pooled analysis. JAMA 1999;281:1598-604.
13. Slate E, Clark L. Using PSA to detect prostate cancer onset: an application of Bayesian retrospective and prospective changepoint identification. In: Gatsonis C, Kass RE, Carlin B, et al, eds. Case studies in Bayesian statistics. Vol 4. New York, NY: Springer-Verlag, 1999:395-412.
14. Pearson J, Morrell C, Landis P, et al. Mixed-effects regression models for studying the natural history of prostate disease. J Am Stat Assoc 1994;13:587-601.
15. 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 1995;90:45-53.
16. Pauler D, Finkelstein D. Predicting time to prostate cancer recurrence based on joint models for non-linear longitudinal biomarkers and event time outcomes. Stat Med 2002;21:3897-911.
17. Slate E, Cronin K. Changepoint modeling of longitudinal PSA as biomarker for prostate cancer. In: Gatsonis C, Hodges J, Kass RE, et al, eds. Case studies in Bayesian statistics. Vol 3. New York, NY: Springer-Verlag, 1999:435-56.
18. McMullen KR, Lee WR. A structured literature review to determine the use of the American Society for Therapeutic Radiology and Oncology consensus definition of biochemical failure. Urology 2003;61:391-6.
19. Vicini F, Kestin L, Martinez A. The correlation of serial prostate specific antigen measurements with clinical outcome after external beam radiation therapy of patients for prostate carcinoma. Cancer 2000;88:2305-18.
20. Horwitz E, Thames H, Kuban D, et al. Definitions of biochemical failure that best predict clinical failure in prostate cancer patients treated with external beam radiation alone: a multi-institutional pooled analysis. J Urol 2005;173:797-802.
21. Kuban D, Thames H, Shipley W. Defining recurrence after radiation for prostate cancer. J Urol 2005;173:1871-8.
22. 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 2004;171:2221-5.
23. Schmid H, McNeal J, 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 1993;71:2031-40.
24. Meek A, Park T, Oberman E, et al. A prospective study of prostate specific antigen levels in patients receiving radiotherapy for localized carcinoma of the prostate. Int J Radiat Oncol Biol Phys 1990;19:733-41.
25. Gelman A, Carlin J, Stern H, et al. Bayesian data analysis. 2nd ed. (Texts in statistical science series). London, United Kingdom: Chapman & Hall/CRC, 2004.
26. Bellera C, Hanley J, Joseph L, et al. Hierarchical change point models for biochemical markers illustrated by tracking postradiotherapy PSA series in men with prostate cancer. Ann Epidemiol (in press).
27. Spiegelhalter D, Thomas A, Best N, et al. WinBUGS: Bayesian inference using Gibbs sampling, version 1.3. Cambridge, MA: MRC Biostatistics Unit, 2000.
28. Raftery A, Lewis S. How many iterations in the Gibbs sampler? In: Bernardo JM, Berger JO, Dawid AP, et al, eds. Bayesian statistics. Vol 4. Oxford, United Kingdom: Oxford University Press, 1992:763-73.
29. Urban N, Drescher C, Etzioni R, et al. Use of a stochastic simulation model to identify an efficient protocol for ovarian cancer screening. Control Clin Trials 1997;18:251-70.
30. Hopkins M, Coyle D, Le T, et al. Cancer antigen 125 in ovarian cancer surveillance: a decision analysis model. Curr Oncol 2007;14:167-72.
31. Ankerst DP, Finkelstein D. Clinical monitoring based on joint models for longitudinal biomarkers and event times. In: Crowley J, Ankerst DP, eds. Handbook of statistics in clinical oncology. 2nd ed. Boca Raton, FL: Chapman & Hall/CRC Taylor & Francis Group, 2006.
32. Slate E, Turnbull B. Statistical models for longitudinal biomarkers of disease onset. Stat Med 2000;19:617-37.
33. Law N, Taylor J, Sandler H. The joint modeling of longitudinal disease progression marker and the failure time process in the presence of cure. Biostatistics 2002;3:547-63.
34. Taylor J, Yu M, Sandler H. Individualized predictions of disease progression following radiotherapy for prostate cancer. J Clin Oncol 2005;23:816-25.
35. Joseph L, Wolfson DB, Belisle P, et al. Taking account of between-patient variability when modeling decline in Alzheimer's disease. Am J Epidemiol 1999;149:963-73.
36. Olmstead P. Distribution of sample arrangements for runs up and down. Ann Math Stat 1946;17:24-33.
37. Levene H, Wolfowitz J. The covariance matrix of runs up and down. Ann Math Stat 1944;15:58-69.
38. Chauvet M, Piger J. Identifying business cycle turning points in real time. The Federal Reserve Bank of St Louis Review 2003;85:47-62.