1. On the Trail of Improved Cancer Outcomes: An Outcomes Research Example Arthur R. Williams, PhD, MA, MPA Director, Center for Health Outcomes and Health Services R esearch

2. Outcomes Researchers Health Outcomes Researcher Nurse Researcher Health Care System HELP!!

3. What is Outcomes Research? “Outcomes research seeks to understand the end results of particular health care practices and interventions. End results include effects that people experience and care about, such as change in the ability to function. … By linking the care people get to the outcomes they experience, outcomes research has become the key to developing better ways to monitor and improve the quality of care. “ -- Agency for Healthcare Research and Quality, March 2000 www.ahrq.gov

4. Outcomes Research can be viewed as a subarea of Health Services Research. Where Health Services Research is focused “on how people get access to health care, how much care costs, and what happens to patients as a result of this care. The main goals of health services research are to identify the most effective ways to organize, manage, finance, and deliver high quality care; reduce medical errors; and improve patient safety. “ -- Agency for Healthcare Research and Quality, February 2002 www.ahrq.gov also AcademyHealth www.academyhealth.org

5. A Health Services Research Paradigm circa 2003

9. Health Services Research contains elements of evidence based medicine, clinical epidemiology, psychology, medical sociology, organizational behavior (understudied), quality and safety analyses (industrial engineering), advanced multivariate statistics, and, especially, health economics. Patient Shared Decision-Making

10. TENDENCIES IN OUTCOMES RESEARCH 1.Research generally arises from a specific clinical issue or problem. 2.Use (often) of quasi-experimental designs. 3.Use (often) of administrative or existing data sets. 4.Use of complex statistical procedures, which may be consequence of 2 and 3. 5.Use of measures of both outputs (and process) and outcomes. 6.Use of highly multi-disciplinary teams.

11. Improving Cancer Outcomes Using the Therapy-Related Symptom Checklist (TRSC) for Adult Oncology Patients Arthur R. Williams, PhD, MA, MPA Phoebe D. Williams, PhD, RN, FAAN TRSC Research Team* *Team consisted of economist/statistician, nurse researcher, 6 oncology nurses, 3 physicians, computer analyst, biostatistician, nutritionist, and other clinical staff at a Cancer Center within a community hospital system in small town/rural Wisconsin. Tendency 6: MULITIDISCIPLINARY TEAM

12. Initial Reason for Study of Symptoms (1984-1986) 3 Advanced Practice Nurses at the University of Florida believed under-documentation of patient symptoms was impeding symptom management and patient recovery. Researched and published : 1.8 symptoms per patient documented in medical records yet patients reported 11.0 symptoms on average of concern to them. Youngblood M, Williams PD, Eyles H, Waring J, Runyon S. (1994). A comparison of two methods of assessing cancer therapy-related symptoms. Cancer Nursing; 17(1): 37-44. Tendency 1: CLINCAL ISSUE

13. FOLLOW-UP: Calibration, Testing, and Development of a 25-Item Therapy-Related Symptom Checklist (TRSC) for Adult Oncology Patients Many publications; Some summarized in Barry MJ, Dancey JE (2005). Instruments to measure the specific health impact of surgery, radiation, and chemotherapy on cancer patients (pp.201-215). In: Lipscomb, J., Gotay CC, Snyder C (Eds.), Outcomes Assessment I n Cancer: Measures, Methods, and Applications. Cambridge University Press, London.

14. Objective of the Study by the Wisconsin Team: To Determine in an Outpatient Setting If: Documentation of patient reported symptoms, management of these symptoms, and HRQOL can be improved through use of the TRSC. H1: A treatment cohort using the TRSC at clinic visits will show a statistically significant positive increase in HRQOL-LASA compared to a treatment cohort receiving standard of care. H2: A treatment cohort using the TRSC during clinic visits will show a statistically significant larger number of symptoms documented and managed compared to a treatment cohort receiving standard of care. Translational Research

15. Quasi-Experimental Design of Study Sequential non-equivalent cohort design: early cohort serves as study “control” for later “treatment” cohort. Ref: Happ MB, Sereika S, Garrett K, Tate J. Use of the quasi-experimental sequential cohort design in the study of patient–nurse effectiveness with assisted communication strategies (SPEACS). Contemporary Clinical Trials 2008 29:801-808. Statistical analysis using generalized estimating equations (GEE): additional strength through use of repeated measure panel. Ref: Twisk JWR. Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide. Cambridge: Cambridge University, 2003. Tendency 2: QUASI- EXPERIMENTAL DESIGN Tendency 4: COMPLEX STATISTICS

16. Demographics of Study Subjects Of the 128 subjects on which data were collected 2 were excluded for having only one visit and 13 were not able to be staged for their cancer. Stage was considered a critical covariate in the two GEE. Exclusion of these subjects left 113 for the analysis in this paper with 696 observations. The mean number of observations per patient was 5.2. Final “Control” Group n=55 subjects; Final “Treatment” Group n=58 subjects. No statistically significant differences between groups on: Age: Gender; Marital Status; Education; Presence of Significant Other; Stage; Diagnoses; Radiation; Chemo; Both Radio-Chemo. Median Age = 61; Married: 79%; Education: 36% HS or Less; 47% HS+; 16% BS/BA+; Race: 96% Caucasian. Profile reflects populations in small town/rural Upper Midwest.

17. Results of GEE Analysis of HRQOL-LASA on Covariates Response: HRQOLCorr: Exchangeable Variable NameCoefficientStErrp-value constant 16.7528 7.7564 0.031 Baseline QOL.7083.0829 < 0.001 Treatment Group 3.3144 1.3152 0.012. Education -.8092.7233 0.263 Age.0090.0673 0.893 Male -3.2381 1.5391 0.035 Significant Other.1893 1.5507 0.903 Stage.6205.7723 0.422 Radio -2.6490 1.5413 0.086 Chemo -3.6356 2.3372 0.120 Days from Baseline -.0166.0109 0.129 ---------------------------------------------------------------------------------------------------- #observations: 583scale parameter: 62.3707 #iterations: 4 Estimate of common correlation 0.6095 MAIN FINDING

18. Results of GEE Analysis of Total Symptoms on Covariates Response: Number of SymptomsCorr: Exchangeable Variable NameCoefficientStErrp-value constant 2.5747 2.2991 0.263 Treatment Group 3.7597.5939 < 0.001. Education -.1588.2531 0.344 Age -.0310.0328 0.893 Male -.7299.6698 0.276 Significant Other.0226.6699 0.973 Stage.7590.3482 0.029 Radio.0767.4155 0.853 Chemo 1.2776.6814 0.061 Days from Baseline.0005.0031 0.870 Days × Treatment Group -.0151.0072 0.037 ---------------------------------------------------------------------------------------------------- #observations: 696scale parameter: 13.5982 #iterations: 5 Estimate of common correlation 0.6977 Main Finding Important “Process” Finding Tendency 6: MEASURES OF OUTPUTS AND OUTCOMES

19. RESULTS & CONCLUSIONS Treatment group patients had a 7.2% higher population averaged covariate adjusted HRQOL than “control” patients, (3.3 more points on the HRQOL, P =.012.) 116% more covariate and non-covariate adjusted symptoms were documented and managed in the “treatment “group than in the “control” group, (6.14 symptoms versus 2.84, P.40. Use of the patient-friendly TRSC by patients and clinicians improves HRQOL. Documentation and management of symptoms also are improved. Tendency 3: ADMINISTRATIVE DATA SETS

20. DISCUSSION OR POP QUIZ Which of the tendencies, if any, of OUTCOMES RESEARCH are not illustrated in this presentation? What in the table is not a measure of OUTCOME?