When you have mastered numbers, you will in fact no longer be reading numbers, any more than you read words when reading books. You will be reading meanings.--W.E.B. Du Bois
I am not a statistician. Whew. I lived in fear of the news getting out as I sat with thousands of the nation's best statisticians for close to a week in Baltimore. I joked to a few colleagues that I was embedded--meaning they would protect me if convenient but otherwise I knew what I was getting into when I accepted the opportunity.
Another casualty of "embedded" reporting visible when traveling to not only statistical meetings but medical conferences, health IT meetings, and even policy and health economic discussions--I am observing "war" from just one perspective, not seeing the entire context. These sessions are run by and for experts in the field not among ordinary people, asking questions of all sides. My vantage point has value, but it is hardly a 360 degree perspective. Unless I also engage with different member stakeholders. This reminds me of CME colleagues, or professionals from Pharma, or even agencies. It is hard to make a significant difference if you only represent a single perspective.
The Joint statistics meeting was a "numbers gal" dream. Look at it this way I may not be a botanist, but I am definitely a flower. I follow the rules and accept the philosophy that generated them. I admit that statisticians like to use terms like "Idiot-proof" and "lay-person" like commas when discussing the data landscape but minus the Sturm und Drang, I get their concern.
Now, to be clear, I have studied (and continue to study) statistical principles and debates around ideology and philosophy. Big data has hit everyone by surprise--statisticians included. We have different accessibility issues, data readiness, and above all ideas about the best way to model data for the most accurate insights.
Take for example the graphic from the meeting, Real World Data in Pharmaco-epidemiology. You may notice in this "real world" there are limited if any patient perspectives.
I rely on many types of data sources but the main buckets are RWD as described or data generated by surveys or questionnaires designed to capture data useful in interpreting the patient experience or risk profile. I prioritized attending sessions describing the latest approaches to adaptive and network sampling. We can only make better recommendations when we utilize the right data, right tools, in the right populations.
I am still applying the insights gained to a few current projects that will be improved by alternative models and robust analyses but I wanted to share a TEDx session from one of the speakers at JSM2017. Listen to an application of machine learning and how it has been applied to improving patient outcomes.
Better Medicine Through Machine Learning
I like to leave a little "donut" for readers sticking it through to the end. A high-value practice in the statistical sessions included the role of a discussant. Following several 20 minute data and methodology presentations, a well-informed discussant provided clarity, summary, and often a few challenges to the presenters. A meaningful dialogue with context is a useful learning tool for deciding what might be of benefit to your research project or data challenge.
The extended discussion of the network sampling approach has useful applications to clinical trial recruitment. Think of people as nodes and edges or links as relationships between people. In studies of hard to recruit patients where they may be dispersed or heard-to-reach, tracing the links within networks can be a logical method to add new numbers. This is an example of an adaptive sampling design because your sample will be selected based on the values of the nodes and links you observe during your survey. This has been my philosophy behind an extensive use of auxiliary variables.
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Thank you for making a donution!
In a world of "evidence-based" medicine I am a bigger fan of practice-based evidence.
Remember the quote by Upton Sinclair...
“It is difficult to get a man to understand something, when his salary depends upon his not understanding it!”
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