What is new?
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This conceptual model presents cancer and comparative effectiveness research in the context of a patient-centered, longitudinal chronic care model that better reflects contemporary cancer care, rather than the traditional single-episode, acute-care perspective.
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The model is grounded in the context of the cancer care continuum, and specifies the relevance of intermediate outcomes both as important end-points and also as they moderate treatments.
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The model moves beyond the intent-to-treat principle by recognizing multiple modalities and lines of therapy, their interaction, and feedback, which change over time. It includes illustrative examples of multiple factors associated with treatment decisions and outcomes and extensive measure specification required in the context of non-experimental data.
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Going forward, the model serves as a template for current researchers and a road map for future improvements for registries and other comparative effectiveness research data, pointing out at least two central needs:
Systematically identified, standardized measures to fill data gaps and enhance transferability.
Targets for important, continued investment in improving study design, population sampling, and analytic methods.
The accelerated pace of scientific discovery has yielded rapid advances in cancer care, underscoring the need for timely, evidence-based information to guide implementation of new interventions into clinical practice. Randomized controlled trials are valid tools for conducting comparative effectiveness research and remain the gold standard in determining efficacy of new interventions [1]; however, this research design is not always feasible, practical, or sufficiently timely. Moreover, because of their very selective inclusion criteria and limited enrollment, randomized trials are commonly unable to characterize the myriad combinations of interventions and heterogeneous patient characteristics and thus fall short of informing “real-world” clinical practice [2], [3], [4], [5], [6].
Thanks to advances in information technology and recently developed statistical methods, ever-growing repositories of observational data may be leveraged to conduct cancer comparative effectiveness research. By leveraging rapidly expanding repositories of secondary data collected through patient registries, electronic health records, administrative data, interventional clinical trials, and elsewhere, nonexperimental cancer comparative effectiveness research holds great promise for addressing many of the shortcomings of randomized trials and filling the knowledge gaps they leave unaddressed [7], [8], [9], [10]. However, a primary challenge in using secondary data is comprehensively and confidently characterizing important processes of care and outcomes while effectively controlling for potential confounders. As such, it remains a challenge for cancer comparative effectiveness research to generate valid, timely, and broadly generalizable new information reflecting diverse, “real-world” patients and meaningful outcomes, and many cancer care stakeholders remain skeptical or suspicious of nonexperimental comparative effectiveness research [11], [12], [13]. To overcome these challenges and generate findings with sufficient confidence to meet different evidentiary standards, we must clearly understand data shortcomings and use this knowledge to improve future data and methods.
To inform an evolving framework for understanding cancer comparative effectiveness research data needs, we reviewed prevalent data models and incorporated the feedback of over 70 cancer comparative effectiveness research and outcomes researchers, clinicians, and other stakeholders to develop a conceptual model for examining secondary data in cancer comparative effectiveness research. This model provides a template for informing future data collection and method development efforts relevant to not only secondary data but also prospective research, with the ultimate goal of advancing the utility and acceptability of cancer comparative effectiveness research for clinical and policy-relevant decision making.