In 2025, who will be eating whose lunch?

Data Science morphs out of epidemiology and biostatistics.

(I've been watching the trend for the past 5-8 years. This note is from 2013. Not hard to see where it's going. Try to integrate thinking on this trend with recently circulated article on boundaries of epidemiology in the 20th century.)

Our field continues to focus on narrow-band scientific throughput while data sciences expand bandwidth. Think of that EHR-based case-control study you designed for class as narrow band. Now, re-conceptualize with the frame that a pharma market analyst might bring into play. "Can I use all the information of the health care sector, not just the Dx code in EHR, but also pharmacy records, lab tests, imaging orders, etc., thinking of them as pipes (bands) of information I can integrate to identify unrecognized (and from sales volume standpoint, the not yet treated) cases lurking in the "control" stratum of that study base?"

That is, the problem is not set up to be satisfied with what the EHR Dx code says, by itself. The problem is set up to detect a latent class of potential consumers from which next year's Dx codes either will show zeroes (the incipient case remains unrecognized for yet another year) or ones (bingo! recognition of the Dx indication, without which the sales volume remains flat).

Should you be making plans for a summer internship or sabbatical at Merck? Or Alkhermes?

Example of integrated comparative effectiveness research with sensitivity analyses for possible selection effects; deserves study as an early example of CER in post-PCORI era.

Full article available via e-resources: Morrato et al., 2015