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Angelina D. Tan, Paul J. Novotny, Judith S. Kaur, Jan C. Buckner,

A patient-level meta-analysis of the prognostic significance of baseline quality of life (QOL) for overall survival (OS) among 3704 patients in 24 oncology clinical trials. Angelina D. Tan, Paul J. Novotny, Judith S. Kaur, Jan C. Buckner,

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Angelina D. Tan, Paul J. Novotny, Judith S. Kaur, Jan C. Buckner,

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  1. A patient-level meta-analysis of the prognostic significance of baseline quality of life (QOL) for overall survival (OS) among 3704 patients in 24 oncology clinical trials Angelina D. Tan, Paul J. Novotny, Judith S. Kaur, Jan C. Buckner, Paul L. Schaefer, Philip J. Stella, Phil Kuebler, Jeff A. Sloan Division of Biostatistics, Mayo Clinic, Rochester, MN, Medical Oncology, Mayo Clinic, Rochester, MN, TCCOP, Toledo, OH, St. Joseph Mercy Health System, Ann Arbor, MI ASCO, Chicago, June 2, 2008

  2. Philosophical Question Is there a relationship between a cancer patient’s self-reported baseline quality of life (QOL) and overall survival (OS)?

  3. Background • Theoretical framework from Wilson and Cleary (Ferrans, 2005) • A recent literature review (n=13,874) indicated that in 36 of 39 studies indicated that at least one patient-reported outcome (PRO) was significantly associated with overall survival (Gotay, JCO, 26: 1355 -1363, March 2008)

  4. Prognostic Evidence: Simple PROs • Single-item symptom distress prognostic for lung cancer survival(Degner, JPSM, 1995) • Simple single-item overall measure of QOL(Sloan, JCO, 1998)

  5. Prognostic Evidence: the next step • Research to date has focused primarily on: • Individual studies • Multi-item PROs • Advanced Disease • This led us to explore pooled analysis across multiple studies

  6. Methods • Studies were conducted either at the Mayo Clinic Cancer Center or in the North Central Cancer Treatment Group • 3704 total patients (24 clinical trials)

  7. Studies Included • 8 GI cancer treatment studies • 5 cancer control studies • 6 lung cancer treatment studies • 2 QOL assessment studies • 3 other studies (various treatment trials)

  8. QOL Assessment:Uniscale - Visual Analog Scale Please mark with an ‘X’ the appropriate place within the bar to indicate your rating of this person’s quality of life during the past week. Lowest Quality X Highest Quality |-------------------------| (Please mark one ‘X’ within the bar) Overall QOL at baseline (0-100 scale, 0 =low, 100=high)

  9. Uniscale-NAS(Numeric Analog Scale) This is a reliable and valid measure for cancer patient populations (Sloan, 2002; Huschka, 2005; Locke, 2007) VAS and NAS are psychometrically equivalent

  10. Methods • Overall survival (whole study group and per study) was tested for association with overall QOL defined as either: • clinically deficient (score 0-50) or • not clinically deficient (score 51-100) (Sloan, Value in Health, 2007) • scoring cut-off validation(Butt, JPSM, 2008; Temel, J Thorac Oncol, 2006) • Cox proportional hazards models adjusted for the effects of performance score, race, site, age and gender

  11. Overall Patient Characteristics (N=3704) • RaceWhite 3376 (91%) Black/African American 188 (5%) Hispanic 78 (2%) Asian 26 (1%) American Indian/Alaskan Native 19(0.5%) Native Hawaiian 6 (0.2%) Other 11 (0.3%) • Age (Median, Range) (63, 18-95) • % Female 44

  12. Overall Patient Characteristics (N=3704) • Performance ScoreMissing 704 0 1161 (31%) 1 1685 (45%) 2 154 ( 4%) • Major Tumor SiteGI 2298 (62%) Lung 598 (16%) Breast 274 ( 7%) GU 178 ( 5%) Other 332 ( 9%) Unknown 24 ( 1%)

  13. Distribution of Baseline QOL Scores N=3198 N=506 NotClinically Deficient QOL Clinically DeficientQOL

  14. Baseline QOL Scores by Trials Clinically Deficient

  15. Survival Time vs. QOL Scores Not Clinically Deficient QOL Clinically Deficient QOL 10% of 3198 pts 24% of 3198 pts 5% of 506 pts 12% of 506 pts

  16. Survival Curves(Clinically Deficient QOL vs not Clinically Deficient QOL) nCD QOL CD QOL Survival Time (Years)

  17. Median survival (months) across sites * Not reached (projected)

  18. Multivariate Cox Model for Survival

  19. Discussion • Simplicity of the single item • Demonstrates superior sensitivity over multiple item measures as prognostic indicator (Huschka, 2005 for example) • For more detailed clinical investigation, further assessment would be necessary or interventions initiated.

  20. Results Validated in Other studies Presented at this meeting

  21. Conclusion • There is a strong and demonstrable relationship between baseline QOL and OS for patients on cancer clinical trials • QOL is a strong and independent prognostic factor for OS independent of PS in a wide variety of oncology patient populations

  22. What could the findings mean for future clinical trials? • Stratify for future randomized trials • Improve efficiency of trial, remove confounding of QOL impact on treatment outcomes which may not be balanced across treatment arms

  23. What could the findings mean for patients? • Use as trigger/screening tool for future clinical treatments • Improve cancer patient QOL throughout the disease process by: • preventing the onset of the QOL deficit • tailoring individualized treatments for QOL in the same manner as treating the tumor

  24. Thank you • For further information: • tan.angelina@mayo.edu

  25. Survival Curves (PS=0)(Clinically Deficient QOL vs not Clinically Deficient QOL) nCD QOL CD QOL Survival Time (Years)

  26. Survival Curves (PS=1,2)(Clinically Deficient QOL vs not Clinically Deficient QOL) nCD QOL CD QOL Survival Time (Years)

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