A framework for understanding cancer comparative effectiveness research data needs

Abstract

Objectives: Randomized controlled trials remain the gold standard for evaluating cancer intervention efficacy. Randomized trials are not always feasible, practical, or timely and often don't adequately reflect patient heterogeneity and real-world clinical practice. Comparative effectiveness research can leverage secondary data to help fill knowledge gaps randomized trials leave unaddressed; however, comparative effectiveness research also faces shortcomings. The goal of this project was to develop a new model and inform an evolving framework articulating cancer comparative effectiveness research data needs.

Study design and setting: We examined prevalent models and conducted semi-structured discussions with 76 clinicians and comparative effectiveness research researchers affiliated with the Agency for Healthcare Research and Quality's cancer comparative effectiveness research programs.

Results: A new model was iteratively developed and presents cancer comparative effectiveness research and important measures in a patient-centered, longitudinal chronic care model better reflecting contemporary cancer care in the context of the cancer care continuum, rather than a single-episode, acute-care perspective.

Conclusion: Immediately relevant for federally funded comparative effectiveness research programs, the model informs an evolving framework articulating cancer comparative effectiveness research data needs, including evolutionary enhancements to registries and epidemiologic research data systems. We discuss elements of contemporary clinical practice, methodology improvements, and related needs affecting comparative effectiveness research's ability to yield findings clinicians, policy makers, and stakeholders can confidently act on.

Figure 1

The cancer care continuum and context of quality in which comparative effectiveness research and patient-centered outcomes research is conducted. Adapted and reprinted with permission from the American Association for Cancer Research: JG Zapka, SH Taplin, LI Solberg, MM Manos. (2003) “A Framework for Improving the Quality of Cancer Care: The Case of Breast and Cervical Cancer Screening.” Cancer Epidemiology, Biomarkers, and Prevention. 12(1):4–13.

Figure 2

A prevailing model, cancer outcome predictors. Originally presented in: Geraci, JM, et al. (2005). “Comorbid Disease and Cancer: The Need for More Relevant Conceptual Models in Health Services Research.” Journal of Clinical Oncology. 23(30):7399-404. Reprinted with permission. © 2008 American Society of Clinical Oncology.

Figure 3

Proposed new model: Measures for patient-centered cancer outcomes research using observational data.