The quote is part of my email signature. It reminds me of the complexity of decisions being made at the point of care by both healthcare providers and patients. "Patients have unprecedented access to health information, but lack the skills to interpret it", says Dr. Mahajan. I would argue that healthcare providers are in the same boat.
I often query statisticians at conferences such as the Joint Statistical Meetings--"Who is the intended audience for your models and debates?" At times it seems like complex numeracy is left for those of us perhaps with a high degree of numeracy but often not linked to ideologic debates of statistical context.
A big data challenge in consultancy is having to tell a client their "baby is ugly". Data frameworks are created during pre-clinical planning and brand teams often aren't aware of the role they play in positioning or eventual marketing of an intervention or therapeutic. Discussing the limits of the wide-eyed optimism often rampant in drug discovery does not make you a popular member of the discussion. But I would argue, perhaps one of the most valuable.
Full disclosure, these are a few of the graphics I have recently used while discussing these topics with groups either bringing a drug to market, understanding the competitive landscape, or guiding stakeholders through what gets reported and what it actually means to their individual perspectives. Links are included for additional granularity.
One of the retained channels of direct physician communication remains continued medical education or CME. I continue to argue many are wasting the opportunity by relying on self-serving myopic educational offerings while simultaneously limiting data collection and analyses to the "been there, done that" mentality of whatever hegemonic framework they are promoting (monetizing).
The journey to "value-based" care is paved with vague terms like "value" and "innovation". Patients, industry, FDA, and insurance companies for example all have unique tensions around bringing innovative and effective therapies or interventions to patients as quickly as possible.
Oncology, in recent months has been the poster child of "hurry up and wait". Trials are trying to move faster with smaller patient populations often identified by pre-determined markers of success dependent on a heterogeneous group of clinical trial endpoints. How do we sift through the data and clinical findings?
A compelling argument can be made for developing a framework to validate surrogate endpoints.
...surrogates can result in market access for technologies that turn out to offer no true health benefit — or even cause harm —and can result in overestimation of treatment effects (and economic value), which can lead to inappropriate decisions on coverage.
Use of surrogate end points in healthcare policy: a proposal for adoption of a validation framework--Ciani, Buyse, Drummond, Rasi, Saad, and Taylor
You should be left with questions. Lots of questions.
•Can absolute standards of surrogacy be defined?
•Is association approach sufficient or should surrogacy be further explored using causal inference?
•If a surrogate is valid for a specific treatment, is it still valid for other treatments?
•Is constancy assumption reasonable (changing treatment landscape)?
•Can incomplete surrogate be used e.g. for rescue therapy?
The Surrogate Threshold Effect (STE) for EFS as potential surrogate for OS in patients treated for
Acute Myeloid Leukemia (AML)--Buyse, Schlenk, Donner, Burzykowski-- JSM 2017
Here is the hot tip for improving critical thought and analytic approaches. First, it matters because the quality of debate and consideration prior to late-stage clinical trials or drug approvals--the better the outcomes for all stakeholders. Public workshops are the best training ground for improving expertise in the validity or weakness in certain data/statistical models. A recent in-person session of Duke-Margolis Center for Health Policy titled Public Workshop: Scientific and Regulatory Considerations for the Analytical Validation of Assays Used in the Qualification of Biomarkers in Biological Matrices focused on discussion of this white paper.
Bonny is a data enthusiast applying curated analysis and visualization to persistent tensions between health policy, economics, and clinical research in oncology.