The aim of this application is to develop a tool for feasible evidence-based clinical monitoring of prostate cancer patients treated by external beam radiation. Methods require the monitoring of serum prostate- specific antigen (PSA). Joint statistical models are fit to retrospective PSA and clinical recurrence data from 1945 patients treated at Massachusetts General Hospital and the University of Michigan Cancer Center (UMCC). Predictions of time to recurrence for new patients based on their current serial PSA history are obtained by integrating over fits from the joint model. The methods are validated using additional follow-up data from UMCC patients and from additional RTOG patients. Alternative prediction models are developed for patients treated with concurrent hormonal therapy using data from 541 UMCC patients and validated on data from RTOG. A web-based interface is constructed for access by clinicians and patients. The user enters characteristics of the patient along with all available dates and PSA values from start of treatment. The program returns an estimated probability of recurrence along with a measure of uncertainty. In addition to the early detection of prostate cancer recurrence, the long term objective of this work is to facilitate evidence-based monitoring of cancer patients through training of clinicians to interact with optimized programs on the world wide web.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants Phase II (R33)
Project #
4R33CA110518-02
Application #
7303694
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Kagan, Jacob
Project Start
2005-09-13
Project End
2008-12-31
Budget Start
2007-01-01
Budget End
2007-12-31
Support Year
2
Fiscal Year
2007
Total Cost
$329,702
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Sène, Mbéry; Taylor, Jeremy Mg; Dignam, James J et al. (2016) Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: Development and validation. Stat Methods Med Res 25:2972-2991
Proust-Lima, Cécile; Séne, Mbéry; Taylor, Jeremy M G et al. (2014) Joint latent class models for longitudinal and time-to-event data: a review. Stat Methods Med Res 23:74-90
Taylor, Jeremy M G; Shen, Jincheng; Kennedy, Edward H et al. (2014) Comparison of methods for estimating the effect of salvage therapy in prostate cancer when treatment is given by indication. Stat Med 33:257-74
Taylor, Jeremy M G; Park, Yongseok; Ankerst, Donna P et al. (2013) Real-time individual predictions of prostate cancer recurrence using joint models. Biometrics 69:206-13
Proust-Lima, Cécile; Taylor, Jeremy M G; Sécher, Solène et al. (2011) Confirmation of a low ?/? ratio for prostate cancer treated by external beam radiation therapy alone using a post-treatment repeated-measures model for PSA dynamics. Int J Radiat Oncol Biol Phys 79:195-201
Zhu, Bin; Taylor, Jeremy M G; Song, Peter X-K (2011) Semiparametric Stochastic Modeling of the Rate Function in Longitudinal Studies. J Am Stat Assoc 106:1485-1495
Zhu, Bin; Song, Peter X-K; Taylor, Jeremy M G (2011) Stochastic functional data analysis: a diffusion model-based approach. Biometrics 67:1295-304
Kennedy, Edward H; Taylor, Jeremy M G; Schaubel, Douglas E et al. (2010) The effect of salvage therapy on survival in a longitudinal study with treatment by indication. Stat Med 29:2569-80
Albert, Paul S; Shih, Joanna H (2010) On estimating the relationship between longitudinal measurements and time-to-event data using a simple two-stage procedure. Biometrics 66:983-7; discussion 987-91
Proust-Lima, Cécile; Taylor, Jeremy M G (2009) Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach. Biostatistics 10:535-49