BAYESIAN JOINT LONGITUDINAL-DISCRETE TIME SURVIVAL MODELS: EVALUATING BIOPSY PROTOCOLS IN ACTIVE-SURVEILLANCE STUDIES

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1 BAYESIAN JOINT LONGITUDINAL-DISCRETE TIME SURVIVAL MODELS: EVALUATING BIOPSY PROTOCOLS IN ACTIVE-SURVEILLANCE STUDIES Lurdes Y. T. Inoue, PhD Professor Department of Biostatistics University of Washington

2 OUTLINE Background Statistical Model Case Study Discussion

3 BACKGROUND

4 BACKGROUND: PROSTATE CANCER Prostate: Gland in the male reproductive system found below the bladder and in front of the rectum. Prostate Cancer: Disease in which malignant cancer cells form in the tissues of the prostate Second most frequently diagnosed cancer in men. Second leading cause of cancer death in men. (American Cancer Society) 2014 Estimates: New cases: 233,000; Deaths: 29,480 Symptoms include a weak flow of urine or frequent urination

5 BACKGROUND: PROSTATE-SPECIFIC ANTIGEN (PSA) Measures PSA level in the blood. PSA is elevated in men who have prostate cancer; PSA is also elevated in men who have an infection or inflammation of the prostate or BPH PSA cut-off 4.0 ng/ml: Sensitivity of 86%, Specificity of 33%

6 BACKGROUND: PROSTATE CANCER GRADE Gleason score is based on the loss of normal glandular tissue architecture (i.e. shape, size and differentiation of the glands) The higher the Gleason score, the more aggressive the tumor - worse patient s prognosis.

7 Why is this a decision problem? Some men benefit from treatment but Some men will not die of prostate cancer There could be non-trivial (harmful) consequences of treatment 7

8 BACKGROUND: ACTIVE SURVEILLANCE What is it? Treatment delayed until test results show that your prostate cancer is growing or changing Who is candidate?: Early-stage prostate cancer Low grade Slow growth tumor Older men Protocol? Cons: Pros: Regular exams: PSA tests & Digital rectal exams - every three to six months; biopsy - annual Treatment Initiation: Rise of Gleason score, PSA level, symptoms. Options: surgery, radiation therapy, or other. Anxiety with living with an untreated prostate cancer Waiting to start treatment may reduce the chance to control cancer before it spreads [Having regular checkups reduces this risk] Avoid side effects of surgery, radiation therapy, or other treatments.

9 BACKGROUND: ACTIVE SURVEILLANCE STUDIES Several ongoing prospective cohort studies of active surveillance (Warlick et al., 2006; Dall Era et al., 2008; Newcomb et al., 2010; Tosoian et al., 2011) Disease grade upgrading used as one of the criteria for initiating curative treatment. New insights about Risk of disease progression in the absence of immediate treatment Time until initiation of treatment

10 BACKGROUND: ACTIVE SURVEILLANCE STUDIES Data: JHU PASS TORONTO (UCSF not yet but soon!) Goals of Analysis: Learning about the natural history of PC progression while accounting for differences in study protocols Investigate impact of different monitoring policies for treatment initiation

11 STATISTICAL MODEL

12 MOTIVATION PSA Correlation between processes Serial correlation of longitudinal outcomes Grade Time since Diagnosis Treatment

13 Years Since Diagnosis Years Since Diagnosis Years Since Diagnosis PSA JHU PSA JHU PSA JHU Years Since Diagnosis Years Since Diagnosis Years Since Diagnosis PSA PASS PSA PASS PSA PASS Years Since Diagnosis Years Since Diagnosis Years Since Diagnosis PSA TORONTO PSA TORONTO PSA TORONTO

14 MODEL FORMULATION PSA grade (binary indicator of high grade) Bayesian estimation Priors (relatively non-informative) normal for regression coefficients inverse-gamma for variance components

15 MODEL FORMULATION: JOINT MODEL Mixed-effects model Multivariate Discrete-time survival model (primary event: upgrade, secondary event: treatment) PSA grade (binary indicator of high grade) Informed by PSA growth parameters (shared random effects formulation)

16 POSTERIOR PREDICTIVE MARGINAL SURVIVAL

17 CASE-STUDY

18 Model Cohort JHU PASS TORONTO Component Endpoint Coefficient Md L U Md L U Md L U PSA PSA Intercept Survival Slope Biopsy Upgrade Intercept Slope Age Survival Treatment Intercept Slope Age

19 COMBINED ANALYSIS Biopsy Upgrade Treatment Cumulative Probability JHU PASS TORONTO Cumulative Probability JHU PASS TORONTO Years from Diagnosis Years from Diagnosis

20 COMBINED ANALYSIS: AGE-ADJUSTED Biopsy Upgrade Treatment Cumulative Probability JHU PASS TORONTO Cumulative Probability JHU PASS TORONTO Years from Diagnosis Years from Diagnosis

21 Biopsy Upgrade Years from Diagnosis Biopsy Upgrade Years from Diagnosis Varying PSA growth parameters JHU PASS TORONTO JHU PASS TORONTO Years from Diagnosis Years from Diagnosis Biopsy Upgrade Biopsy Upgrade JHU PASS TORONTO JHU PASS TORONTO

22 Time of Upgrade on Observed Biopsy Number of Biopsies Delay of Upgrade Detection COHORT Rule Me SD Md Q1 Q3 Me SD Md Q1 Q3 Me SD Md Q1 Q3 JHU PASS TORONTO

23 Doubling PSA growth: Time of Upgrade on Observed Biopsy Number of Biopsies Delay of Upgrade Detection COHORT Rule Me SD Md Q1 Q3 Me SD Md Q1 Q3 Me SD Md Q1 Q3 JHU PASS TORONTO

24 DISCUSSION

25 DISCUSSION Use of joint models to capture informative dropouts (off-treatment protocol) Differences in active surveillance studies yet Surveillance policies do not need to be differentiated Potential surveillance policy: biopsy every other year reduces the number of biopsy by halve and yet only delaying upgrade detection by half year (on average)