Invited Paper D Industry perspective regarding outcomes research in oncology

Kati Copley-Merriman M.S., M.B.A.: Affiliation:
Senior Director/Site Leader Pfizer Inc.
Joseph Jackson Ph.D.: Affiliation:
Group Director Bristol-Myers Squibb
J. Gregory Boyer Ph.D.: Affiliation:
Assistant Executive Director Pharmacia Corp
Joseph C. Cappelleri Ph.D.: Affiliation:
Director, Biostatistics Pfizer Inc.
Robert DeMarinis Ph.D.: Affiliation:
Assistant Vice President Wyeth-Ayerst Research
Joseph DiCesare M.P.H., R.Ph.: Affiliation:
Executive Director Novartis Pharmaceuticals Corp.
M. Haim Erder Ph.D.: Affiliation:
Director Amgen Inc.
Jean Paul Gagnon Ph.D.: Affiliation:
Director Aventis Pharmaceuticals Inc.
Lou Garrison Ph.D.: Affiliation:
Vice President and Head F. Hoffman-La Roche AG
Kathleen Gondek Ph.D.: Affiliation:
Director Bayer Corp.
Kim A. Heithoff Ph.D.: Affiliation:
Director Schering-Plough Pharmaceuticals
Tom Hughes Ph.D.: Affiliation:
Director Eli Lilly and Company
David Miller Ph.D.: Affiliation:
Vice President GlaxoSmithKline
Margaret Rothman Ph.D.: Affiliation:
Executive Director Johnson & Johnson Pharmaceutical Services, LLC
Nancy Santanello M.D., M.S.: Affiliation:
Executive Director Merck Research Laboratories
Richard Willke Ph.D.: Affiliation:
Senior Director/Group Leader Pharmacia Corp
Bruce Wong M.D.: Affiliation:
Vice President Bristol-Myers Squibb
Joseph Lipscomb: Affiliation:
National Cancer Institute, Bethesda, Maryland
Carolyn C. Gotay: Affiliation:
Cancer Research Center, Hawaii
Claire Snyder: Affiliation:
National Cancer Institute, Bethesda, Maryland

Book contents

Get access

Summary

Introduction

The goal of treatment for many persons with cancer is not cure but improvement or maintenance of functioning and well-being during their remaining period of life. This is particularly true for patients with advanced or metastatic cancers. Trials to produce evidence of effectiveness or for regulatory approval may include patient assessments of benefit as well as classical clinical endpoints used in oncology settings. These patient assessments of treatment benefit may or may not be related to the traditional measures of treatment success such as survival, tumor shrinkage, or time to tumor progression. For this reason, additional outcome measures to estimate benefit or risk/benefit trade-offs have been developed. Outcomes measures in this category of health assessment are referred to as patient-reported outcomes (PROs) because they are used to collect data directly from the patient.

It is increasingly recognized that the patient's perspective is unique and represents a valuable contribution to drug evaluation and treatment processes. This is particularly important when studying the effects of treatments on cancer symptoms such as pain and fatigue, outcomes not accurately measured by observers. Recent changes in the health care system have greatly empowered patients who are now considered partners rather than passive consumers. To maximize their contribution, they need to be informed about the outcomes associated with treatment. Patients are not always concerned with the same questions as treating physicians or clinical researchers.

Type: Chapter
Information: Outcomes Assessment in Cancer
Measures, Methods and Applications
, pp. 623 - 638

DOI: https://doi.org/10.1017/CBO9780511545856.032 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Lindley, C., Vasa, S., Sawyer, W. T.et al. (1998). Quality of life and preferences for treatment following systemic adjuvant therapy for early-stage breast cancerJournal of Clinical Oncology 16(4):1380–7CrossRef Google Scholar PubMed

Bezjak, A., Ng, P., Skeel, R.et al. (2001). Oncologists' use of quality of life information: results of a survey of Eastern Cooperative Oncology Group physiciansQuality of Life Research 10(1):1–13CrossRef Google Scholar PubMed

Haes, J. C. J. M., Knippenberg, F. C. E., Neijt, J. P. (1990). Measuring psychological and physical distress in cancer patients: structure and application of the Rotterdam Symptom ChecklistBritish Journal of Cancer 62:1034–8CrossRef Google Scholar PubMed

Chang, V. T., Hwang, S. S., Feuerman, M.et al. (2000). The Memorial Symptom Assessment Scale Short FormCancer 89:1162–713.0.CO;2-Y>CrossRef Google Scholar PubMed

Aaronson, N. K., Ahmedzai, S., Bergman, B.et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: a quality of life instrument for use in international clinical trials in oncologyJournal of the National Cancer Institute 85(5):365–76CrossRef Google Scholar

Cella, D. F., Tulsky, D. S., Gray, G.et al. (1993). The Functional Assessment of Cancer Therapy Scale: development and validation of the general measureJournal of Clinical Oncology 11(3):570–9CrossRef Google Scholar PubMed

Acquandro, C. (2001). Important issues in patient reported outcomes (PROs) research. Quality of Life Newsletter, MAPI Research Institute, No. 27

PDR@ Electronic Library™ Copyright (©) 2002 Medical Economics Company, Inc

Khayat, D. (2000). The main causes of quality of life in cancer patientsInternational Journal of Pharmaceutical Medicine 14:70–3CrossRef Google Scholar

Sabbatini, P. (2000). Treating cancer symptoms improves quality of life: Part I: treatment of anemia and fatigueInternational Journal of Pharmaceutical Medicine 14:88–91CrossRef Google Scholar

Roila, F., Cortesi, E. (2001). Quality of life as a primary end point in oncologyAnnals of Oncology 12 (Suppl. 3):S3–6CrossRef Google Scholar

Ahles, T. A., Saykin, A. (2001). Cognitive effects of standard-dose chemotherapy in patients with cancerCancer Investigation 19(8):812–20CrossRef Google Scholar PubMed

Dam, F. S. A. M., Schagen, S. B., Muller, J. M.et al. (1998). Impairment of cognitive function in women receiving adjuvant treatment for high risk breast cancer: high dose versus standard dose chemotherapyJournal of the National Cancer Institute 90(3):210–18Google Scholar PubMed

Wienie, M. H., Diets, E. R. (1995). Neuropsychological assessment of cognitive functioning following chemotherapy for breast cancerPsycho-Oncology 4:61–6CrossRef Google Scholar

Schagen, S. B., Dam, F. S. A. M., Muller, M. J.et al. (1999). Cognitive deficits after postoperative adjuvant chemotherapy for breast carcinomaCancer 85(3):640–503.0.CO;2-G>CrossRef Google Scholar PubMed

Ahles, T. A., Saykin, A., Furstenber, C.et al. (2002). Cognitive impact of standard-dose chemotherapy in long-term survivors of breast cancer and lymphomaJournal of Clinical Oncology 20(2):485–93CrossRef Google Scholar PubMed

Santanello, N. C., Baker, D., Cappelleri, J. C.et al. (2002). Regulatory issues for health-related quality of life — PhRMA Health Outcomes Committee Workshop, 1999Value in Health 5:14–25CrossRef Google Scholar PubMed

Chassany, O., Sagnier, P., Marquis, P.et al for the European Regulatory Issues on Quality of Life Assessment Group (2002). Patient-reported outcomes: The example of health-related quality of life — A European guidance document for the improved integration of health-related quality of life assessment in the drug regulatory processDrug Information Journal 36:209–18CrossRef Google Scholar

Curran, D., Fayers, P. M., Molenberghs, G. et al. (1998). Analysis of incomplete quality of life data in clinical trials. In Quality of Life Assessment in Clinical Trials: Methods and Practice, ed. M. J. Staquet, R. D. Hays, P. M. Fayers, pp. 249–80. New York, NY: Oxford University Press

Fairclough, D. L. (2002). Design and Analysis of Quality of Life Studies in Clinical Trials. New York: Chapman & Hall/CRC

Zhang, J., Quan, H., Ng, J.et al. (1997). Some statistical methods for multiple endpoints in clinical trialsControlled Clinical Trials 18:204CrossRef Google Scholar PubMed

Guyatt, G. H., Osoba, D., Wu, A. W.et al. (2002). Methods to explain the clinical significance of health status measuresMayo Clinic Proceedings 77:371–83CrossRef Google Scholar PubMed

Osoba, D., Rodrigues, G., Myles, J.et al. (1998). Interpreting the significance of changes in health-related quality-of-life scoresJournal of Clinical Oncology 16:139–44CrossRef Google Scholar PubMed

Cella, D., Eton, D. T., Fairclough, D. L.et al. (2002). What is a clinically meaningful change on the Functional Assessment of Cancer Therapy-Lung (FACT-L) Questionnaire? Results from Eastern Cooperative Oncology Group (ECOG) Study 5592Journal of Clinical Epidemiology 55:285–95CrossRef Google Scholar PubMed

Harrell, F. E. Jr., Shih, Y. C. (2001). Using full probability models to compute probabilities of actual interest to decision makersInternational Journal of Technology Assessment in Health Care 17:17–26CrossRef Google Scholar PubMed

Hornberger, J. (2001). Introduction to Bayesian reasoningInternational Journal of Technology Assessment in Health Care 17:9–16CrossRef Google Scholar PubMed

Spiegelhalter, D. J., Myles, J. P., Jones, D. R.et al. (1999). An introduction to Bayesian methods in health technology assessmentBritish Medical Journal 319:508–12CrossRef Google Scholar PubMed

Sheingold, S. H. (2001). Can Bayesian methods make data and analyses more relevant to decision makers? A perspective from Medicare. 2001International Journal of Technology Assessment in Health Care 17:114–22CrossRef Google Scholar

Winkler, R. L. (2001). Why Bayesian analysis hasn't caught on in healthcare decision makingInternational Journal of Technology Assessment in Health Care 17:56–66CrossRef Google Scholar PubMed

International Conference on Harmonization. Statistical Principles for Clinical Trials, 1998. Available at: www.ich.org/pdfifpma/e9.pdf Accessed October 31, 2002

Fryback, D. G., Stout, N. K., Rosenberg, M. A. (2001). An elementary introduction to Bayesian computing using WinBUGSInternational Journal of Technology Assessment in Health Care 17:98–113CrossRef Google Scholar PubMed

Fryback, D. G., Chinnis, J. O. Jr., Ulvila, J. W. (2001). Bayesian cost-effectiveness analysis: An example using the GUSTO trialInternational Journal of Technology Assessment in Health Care 17:83–97CrossRef Google Scholar PubMed

Briggs, A. H. (2001). A Bayesian approach to stochastic cost-effectiveness analysis: an illustration and application to blood pressure control in type 2 diabetesInternational Journal of Technology Assessment in Health Care 17:69–82CrossRef Google Scholar PubMed

Claxton, K., Neumann, P. J., Araki, S.et al. (2001). Bayesian value-of-information analysis: an application to a policy model of Alzheimer's diseaseInternational Journal of Technology Assessment in Health Care 17:38–55CrossRef Google Scholar PubMed

Spiegelhalter, D. J., Myles, J. P., Jones, D. R.et al. (2000). Health Technology Assessment 4:1–130PubMed

O'Hagan, A. O., Stevens, J. W., Montmartin, J. (2000). Inference for the cost-effectiveness acceptability curve and cost-effectiveness ratioPharmacoeconomics 17:339–49CrossRef Google Scholar PubMed

O'Hagan, A. O., Stevens, J. W., Montmartin, J. (2001). Bayesian cost-effectiveness analysis from clinical trial dataStatistics in Medicine 20:733–53CrossRef Google Scholar PubMed

Hay, J., Jackson, J., Luce, B.et al. (1999). Panel 2: Methodological issues in conducting pharmacoeconomic evaluations — modeling studiesValue in Health 2(2):78–81CrossRef Google Scholar PubMed

Meyer, J. A. (2002). Assessing the Impact of Pharmaceutical Innovation: A Comprehensive Framework. Washington, DC: New Directions for Policy

Tengs, T. O., Adams, M. E., Pliskin, J. S.et al. (1995). Five-hundred life-saving interventions and their cost-effectivenessRisk Analysis 15:360–90CrossRef Google Scholar PubMed

Lichtenberg, F. R. (2001). Are the benefits of newer drugs worth their cost? Evidence from the 1996 MEPSHealth Affairs 20:241–51CrossRef Google Scholar PubMed

Weinstein, M. C., Siegel, J. E., Gold, M. R.et al. (1996). Recommendations of the Panel on Cost-Effectiveness in Health and MedicineJournal of the American Medical Association 276:1253–8CrossRef Google Scholar PubMed

Meltzer, D. (1997). Accounting for future costs in medical cost-effectiveness analysisJournal of Health Economics 16:33–64CrossRef Google Scholar PubMed

Hutton, J., Brown, R., Borowitz, M.et al. (1996). A new decision model for cost-utility comparisons of chemotherapy in recurrent metastatic breast cancerPharmacoeconomics 9:8–22CrossRef Google Scholar PubMed

Nuitjen, M., McCormick, J., Waibel, F.et al. (2000). Economic evaluation of letrozole in the treatment of advanced breast cancer in postmenopausal women in CanadaValue in Health 3:31–9Google Scholar

Weinstein, M. C., Toy, E. L., Sandberg, E. A.et al. (2001). Modeling for health care and other policy decisionsValue in Health 4:348–61CrossRef Google Scholar PubMed

Brown, M., Glick, H. A., Harrell, F.et al. (1998). Integrating economic analysis into cancer clinical trials: the National Cancer Institute — American Society of Clinical Oncology economics workbookNational Cancer Institute Monographs 24:1–28Google Scholar

Hornbrook, this volume, Chapter 24

O'Brien, this volume, Chapter 25

Bennett, C. L., Smith, T. J., George, S. L.et al. (1995). Free-riding and the prisoner's dilemma: problems in funding economic analyses of phase III cancer clinical trialsJournal of Clinical Oncology 9:2457–63CrossRef Google Scholar

National Institute for Clinical Excellence (2000). Guidance on the Use of Taxanes for Breast Cancer, Technology Appraisal Guidance No. 6, June 2000. London: National Institute for Clinical Excellence

National Institute for Clinical Excellence (2001). Guidance on the Use of Docetaxel, Paclitaxel, Gemcitabine and Vinorelbine for the Treatment of Non-Small Cell Lung Cancer, Technology Appraisal Guidance No. 26, June 2001. London: National Institute for Clinical Excellence

National Institute for Clinical Excellence (2001). Guidance on the Use of Topotecan for the Treatment of Advanced Ovarian Cancer, Technology Appraisal Guidance No. 28, August 2001. London: National Institute for Clinical Excellence