1. Meta-Analysis of PSA Growth Lurdes Y.T. Inoue, Ph.D. Ruth Etzioni, Ph.D. Elizabeth Slate, Ph.D. Christopher Morrel, Ph.D.

2. OUTLINE Background Description of Studies Change-Point Models Some Results Future Plans

3. BACKGROUND

4. Prostate Cancer Prostate Cancer: most commonly diagnosed cancer in men Risk factors: Race, family history Black men: higher incidence and poorer survival Treatment options: Surgery or radiation for localized disease Hormone ablation for advanced or recurrent disease Survival: Excellent for localized disease Poor if metastases are present (approx. 30% at 5 years)

6. PSA Screening Most significant and controversial development in prostate cancer control over the last 20 years Initial studies: PSA markedly elevated in men with prostate cancer PSA screening resulted in dramatic shift in stage of disease PSA screening in US exploded in late 1980s and early 1990s Dissemination was not tracked Heterogeneity in how PSA is used Conclusive evidence of efficacy is still lacking! In absence of clinical trial results, controversy about role of PSA in PC mortality declines

7. SCIENCE TIMES, April 9 2002

8. Prostate Cancer Trends Incidence per 100,000

9. Cancer Intervention and Surveillance Network (CISNET) Consortium of NCI-sponsored investigators Main Goal: understand the impact of cancer control interventions (e.g. screening, treatment and prevention) on population trends in incidence and mortality Approach: Simulation-based models Requirement Estimates of PSA growth in cases and non-cases

10. Prior Studies of PSA Growth Five prior studies CARET, BLSA, NPCT, VA, KAISER Variability in the results e.g. 17% increase in Alice Whittemore’s study versus 33% in the Baltimore study). Small samples

11. Summarize important growth rate parameters in a large dataset Understand the impact of advanced cancers (stage/grade) on growth rates Bayesian approach provide new ways of looking at these data… Goals

12. Description of Studies Baltimore Longitudinal Study of Aging (BLSA) Continual longitudinal and multi-disciplinary study of normal human aging Beta-Carotene and Retinol Efficacy Trial (CARET) Chemo-preventive efficacy and safety of beta- carotene and retinol in a population at risk for lung cancer Nutritional Prevention of Cancer Trial (NPCT) Determine whether a supplement of selenium decreases the incidence of cancer

13. Data: Summary Statistics

15. Change-point Models Broad literature on change-point models, some applied to PSA data: Pearson, et. al. (1994) Morrell et. al. (1995), Slate and Cronin (1997), Slate and Clark (1999) ALL BASED ON SINGLE STUDIES.

16. Age Log(PSA+1) Local Metastasis

17. Features: Estimate a change-point in clinically diagnosed cases Combining data from different studies HIERARCHICAL MODELS (RANDOM-EFFECTS)

18. Meta-Analysis using One- Change Point Models Restricted to prostate cancer patients Is there a growth rate change? Stratified by Grade/Gleason’s Score Higher growth rate under poor prognosis?

19. One-Change Point Model Priors:

20. Meta-Analysis using Two- Change Point Models Restricted to prostate cancer patients Use stage information: worse prognosis inducing a second change point in patient’s FU window.

21. Two-Change Point Model + Interval censored observation for second change-point using stage of disease

22. Slopes after the change point

29. Other results: Stratified Analysis: Slope parameter after change-point: Grade 0: CI(95%)= [-0.06,0.23] Grade 1: CI(95%)= [-0.11,0.47] Indication of faster PSA growth for grade 1 patients (more variability too). Two-Change Point Analysis: No evidence for a two-change point: unlikely to occur during subject’s lifetime. Maybe just different post-change point slopes depending on tumor stage…

30. Using Stage Information

31. Post-Change Point Slope – baseline (LOCAL) Effect of Advanced Stage on Post-Change Point Slope (METASTASIS)

35. Conclusions

36. Change point below threshold in many cases No evidence for second change point in patients with metastasis Patients with metastasis have higher post-change point slopes Post-change point slope effects for high and low grade tumors (greater variability for high grade tumors) Misclassification of metastasis as localized Many prostate cancers are not pathologically staged Many clinical cases are upstaged at pathological staging RESULTS ARE CONSISTENT WITH SCIENTIFIC LITERATURE.

37. Future Plans: Obtain a fourth longitudinal data set Validation of the simulation model (CISNET) Focus on natural history models Effects of intervention (prostate cancer prevention) Including controls Dealing with misclassification

38. Challenge: Extremely high latent prevalence relative to clinical incidence Majority of men over 70 harbor a prostate cancer! Lifetime probability of PC onset: 36% Lifetime probability of a PC diagnosis pre-PSA: 9% 9 27 36