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Meta-Analysis of PSA Growth

Meta-Analysis of PSA Growth. Lurdes Y.T. Inoue, Ph.D. Ruth Etzioni, Ph.D. Elizabeth Slate, Ph.D. Christopher Morrel, Ph.D. OUTLINE. Background Description of Studies Change-Point Models Some Results Future Plans. BACKGROUND. Prostate Cancer. Prostate Cancer:

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Meta-Analysis of PSA Growth

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  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)

  5. 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

  6. SCIENCE TIMES, April 9 2002

  7. Incidence per 100,000 Prostate Cancer Trends

  8. 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

  9. 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

  10. Goals • 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…

  11. 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

  12. Data: Summary Statistics

  13. 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.

  14. Log(PSA+1) Local Metastasis Age

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

  16. 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?

  17. One-Change Point Model Priors:

  18. 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.

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

  20. Slopes after the change point

  21. 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…

  22. Using Stage Information

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

  24. Conclusions

  25. 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.

  26. 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

  27. 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 36 27

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