• 1
    Clancy CM, Slutsky JR. Commentary: a progress report on AHRQ's Effective Health Care Program (AHRQ update) [serial online]. Health Serv Res. 2007; 42: xi.
  • 2
    Smith S. Preface. Med Care. 2007; 45( 10 suppl 2): S1-S2.
  • 3
    Congressional Budget Office (CBO). Research on the Comparative Effectiveness of Medical Treatments: Issues and Options for an Expanded Federal Role. Publication no. 2975. Washington, DC: Congressional Budget Office; 2007.
  • 4
    Maclure M. Explaining pragmatic trials to pragmatic policymakers. J Clin Epidemiol. 2009; 62: 476-478.
  • 5
    Tunis SR, Stryer DB, Clancy CM. Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. JAMA. 2003; 290: 624-632.
  • 6
    Institute of Medicine. Initial National Priorities for Comparative Effectiveness Research. Washington, DC: National Academies Press; 2009.
  • 7
    Sturmer T, Funk MJ, Poole C, Brookhart MA. Nonexperimental comparative effectiveness research using linked healthcare databases. Epidemiology. 2011; 22: 298-301.
  • 8
    Sox HC. Defining comparative effectiveness research: the importance of getting it right. Med Care. 2010; 48( 6 suppl): S7-S8.
  • 9
    AHRQ. About the DEcIDE Network. Effective Health Care Program 2011; Accessed March 17, 2012.
  • 10
    Aday L, Begley C, Lairson D, Balkrishnan R. Evaluating the Healthcare System: Effectiveness, Efficiency, and Equity. 3rd ed. Chicago, IL: Health Administration Press; 2004.
  • 11
    McDowell I. Measuring Health. 3rd ed. New York: Oxford University Press; 2006.
  • 12
    Lipscomb J, Gotay C, Snyder C. Outcomes Assessment in Cancer: Measures, Methods, and Applications. Cambridge, MA: Cambridge University Press; 2005.
  • 13
    National Cancer Policy Board of the Institute of Medicine. Ensuring Quality Cancer Care. Washington, DC: National Academies Press; 1999.
  • 14
    National Cancer Policy Board of the Institute of Medicine. Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia. Washington, DC: National Academies Press; 2005.
  • 15
    National Cancer Policy Board of the Institute of Medicine. Enhancing Data Systems to Improve the Quality of Cancer Care. Washington, DC: National Academies Press; 2000.
  • 16
    Zapka JG, Taplin SH, Solberg LI, Manos MM. A framework for improving the quality of cancer care: the case of breast and cervical cancer screening. Cancer Epidemiol Biomarkers Prev. 2003; 12: 4-13.
  • 17
    Shah NR, Stewart WF. Clinical effectiveness: leadership in comparative effectiveness and translational research.: the 15th Annual HMO Research Network Conference, April 26-29, 2009, Danville, Pennsylvania. Clin Med Res. 2010; 8: 28-29.
  • 18
    Aiello Bowles EJ, Tuzzio L, Ritzwoller DP, et al. Accuracy and complexities of using automated clinical data for capturing chemotherapy administrations: implications for future research. Med Care. 2009; 47: 1091-1097.
  • 19
    Sox HC, Greenfield S. Comparative effectiveness research: a report from the Institute of Medicine. Ann Intern Med. 2009; 151: 203-205.
  • 20
    Hoffman A, Pearson SD. “Marginal medicine”: targeting comparative effectiveness research to reduce waste. Health Aff (Millwood). 2009; 28: w710-S718.
  • 21
    Etheredge LM. Medicare's future: cancer care. Health Aff (Millwood). 2009; 28: 148-159.
  • 22
    Donabedian A. Evaluating the quality of medical care. 1966. Millbank Q. 2005; 83: 691-729.
  • 23
    Mandelblatt JS, Ganz PA, Kahn KL. Proposed agenda for the measurement of quality-of-care outcomes in oncology practice. J Clin Oncol. 1999; 17: 2614-2622.
  • 24
    Brookhart MA, Sturmer T, Glynn RJ, Rassen J, Schneeweiss S. Confounding control in healthcare database research: challenges and potential approaches. Med Care. 2010; 48( 6 suppl): S114-S120.
  • 25
    Bloomrosen M, Detmer D. Advancing the framework: use of health data—a report of a working conference of the American Medical Informatics Association. J Am Med Inform Assoc. 2008; 15: 715-722.
  • 26
    Bloomrosen M, Detmer DE. Informatics, evidence-based care, and research; implications for national policy: a report of an American Medical Informatics Association health policy conference. J Am Med Inform Assoc. 2010; 17: 115-123.
  • 27
    Manion FJ, Robbins RJ, Weems WA, Crowley RS. Security and privacy requirements for a multi-institutional cancer research data grid: an interview-based study [serial online]. BMC Med Inform Decis Mak. 2009; 9: 31.
  • 28
    Safran C, Bloomrosen M, Hammond WE, et al. Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper. J Am Med Inform Assoc. 2007; 14: 1-9.
  • 29
    Wallace PJ. Reshaping cancer learning through the use of health information technology. Health Aff (Millwood). 2007; 26: w169-w177.
  • 30
    PatientsLikeMe. Available at: Accessed November 11, 2010.
  • 31
    Sturmer T, Glynn RJ, Rothman KJ, Avorn J, Schneeweiss S. Adjustments for unmeasured confounders in pharmacoepidemiologic database studies using external information. Med Care. 2007; 45( 10 suppl 2): S158-S165.
  • 32
    Sturmer T, Schneeweiss S, Avorn J, Glynn RJ. Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. Am J Epidemiol. 2005; 162: 279-289.
  • 33
    Goldberg RM, Sargent DJ, Morton RF, et al. NCCTG Study N9741: leveraging learning from an NCI cooperative group phase III trial. Oncologist. 2009; 14: 970-978.
  • 34
    Abernethy AP, Etheredge LM, Ganz PA, et al. Rapid-learning system for cancer care. J Clin Oncol. 2010; 28: 4268-4274.
  • 35
    Lievre A, Bachet JB, Boige V, et al. KRAS mutations as an independent prognostic factor in patients with advanced colorectal cancer treated with cetuximab. J Clin Oncol. 2008; 26: 374-379.
  • 36
    Lievre A, Bachet JB, Le Corre D, et al. KRAS mutation status is predictive of response to cetuximab therapy in colorectal cancer. Cancer Res. 2006; 66: 3992-3995.
  • 37
    Sargent D. What constitutes reasonable evidence of efficacy and effectiveness to guide oncology treatment decisions? Oncologist. 2010; 15( suppl 1): 19-23.
  • 38
    Cella D, Riley W, Stone A, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. J Clin Epidemiol. 2010; 63: 1179-1194.
  • 39
    Dorans NJ. Linking scores from multiple health outcome instruments. Qual Life Res. 2007; 16: 85-94.
  • 40
    McHorney CA. Use of item response theory to link 3 modules of functional status items from the asset and health dynamics among the oldest old study. Arch Phys Med Rehabil. 2002; 83: 383-394.
  • 41
    McHorney CA, Cohen AS. Equating health status measures with item response theory illustrations with functional status items. Med Care. 2000; 38: 43-59.
  • 42
    Abernethy AP, Zafar SY, Uronis H, et al. Validation of the Patient Care Monitor (Version 2.0): a review of system assessment instrument for cancer patients. J Pain Symptom Manage. 2010; 40: 545-558.
  • 43
    Abernethy AP, Ahmad A, Zafar SY, Wheeler JL, Reese JB, Lyerly HK. Electronic patient-reported data capture as a foundation of rapid learning cancer care. Med Care. 2010; 48( 6 suppl): S32-S38.
  • 44
    Clauser SB, Ganz PA, Lipscomb J, Reeve BB. Patient-reported outcomes assessment in cancer trials: evaluating and enhancing the payoff to decision making. J Clin Oncol. 2007; 25: 5049-5050.
  • 45
    Lipscomb J, Reeve BB, Clauser SB, et al. Patient-reported outcomes assessment in cancer trials: taking stock, moving forward. J Clin Oncol. 2007; 25: 5133-5140.
  • 46
    Lipscomb J, Gotay CC, Snyder CE. Patient-reported outcomes in cancer: a review of recent research and policy initiatives. CA Cancer J Clin. 2007; 57: 278-300.
  • 47
    Carpenter WR, Peppercorn J. Beyond toxicity: the challenge and importance of understanding the full impact of treatment decisions. Cancer. 2009; 115: 2598-2601.
  • 48
    Hassett MJ, O'Malley AJ, Keating NL. Factors influencing changes in employment among women with newly diagnosed breast cancer. Cancer. 2009; 115: 2775-2782.
  • 49
    Warren JL, Klabunde CN, Schrag D, Bach PB, Riley GF. Overview of the SEER-Medicare data: content, research applications, and generalizability to the United States elderly population. Med Care. 2002; 40( 8 suppl): IV-3-IV-18.
  • 50
    Jutte DP, Roos LL, Brownell MD. Administrative record linkage as a tool for public health research. Annu Rev Public Health. 2011; 32: 91-108.
  • 51
    Sturmer T, Schneeweiss S, Brookhart MA, Rothman KJ, Avorn J, Glynn RJ. Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: nonsteroidal anti-inflammatory drugs and short-term mortality in the elderly. Am J Epidemiol. 2005; 161: 891-898.
  • 52
    Sturmer T, Schneeweiss S, Rothman KJ, Avorn J, Glynn RJ. Performance of propensity score calibration—a simulation study. Am J Epidemiol. 2007; 165: 1110-1118.
  • 53
    Holve E, Pittman P. A First Look at the Volume and Cost of Comparative Effectiveness Research in the United States. Washington, DC: Academy Health; 2009.
  • 54
    Conway PH, VanLare JM. Improving access to health care data: the open government strategy. JAMA. 2010; 304: 1007-1008.
  • 55
    Wagner TH, Murray C, Goldberg J, Adler JM, Abrams J. Costs and benefits of the national cancer institute central institutional review board. J Clin Oncol. 2010; 28: 662-666.
  • 56
    Dilts DM, Sandler AB, Cheng SK, et al. Steps and time to process clinical trials at the Cancer Therapy Evaluation Program. J Clin Oncol. 2009; 27: 1761-1766.
  • 57
    Office for Oregon Health Policy and Research. Policy Brief: All-Payer, All-Claims Data Base. Available at: Brief_AllPayerAllClaimsDatabase_4.30.09.pdf?ga=t. Accessed on July 29, 2010.
  • 58
    US Department of Health and Human Services: Office of the National Coordinator for Health Information Technology. Nationwide Privacy and Security Framework for Electronic Exchange of Individually Identifiable Health Information, December 15, 2008. Available at: Accessed on July 29, 2010.
  • 59
    Organization for Economic Cooperation and Development (OECD). OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data. Available at:,3343,en_2649_34255_1815186_1_1_1_1,00.html. Accessed June 6, 2010.
  • 60
    US Department of Health and Human Services. Health Information Technology. Meaningful Use. Available at: =Community Page&parentid=1&mode=2. Accessed April 16, 2010.
  • 61
    North Carolina Healthcare Information and Communication Alliance. North Carolina Health Information Exchange Council (NC HIE Council) Web site. Available at: Accessed June 2, 2010.
  • 62
    US Department of Health and Human Services: What Is a Medicare Quality Improvement Organization (QIO)? Available at: Accessed May 28, 2010.
  • 63
    Gladwell M. The Tipping Point: How Little Things Can Make a Big Difference. Boston, MA: Little, Brown & Company; 2000.
  • 64
    Clancy C, Collins FS. Patient-centered outcomes research institute: the intersection of science and health care [serial online]. Sci Transl Med. 2010; 2: 37cm18.
  • 65
    Outcome Sciences. Registry of Patient Registries (RoPR). Available at: Accessed January 27, 2011.
  • 66
    US Agency for Healthcare Research and Quality (AHRQ). Registry of Patient Registries. Available at: recoveryawards/osawinfra.htm. Accessed February 6, 2011.