Implication of HEOR and RWE Models

Implications from HEOR and RWE Models for Biopharmaceutical Commercial Analytics

3 mins read

The following blog is an expanded summary of proposed research accepted for delivery at the upcoming 22nd ISPOR Annual International Meeting, Research Poster Presentations - Session I, entitled “Health Care Use & Policy Studies”, to be held in the John B. Hynes Convention Center (Level 2) in Boston on Monday May 22, 2017 from 8:30am-2:30pm. The poster author discussion hour will be from 1:00-2:00pm. 

Biopharmaceutical industry trends show an increased focus on specialty medicines. Specialty medicines account for 35% of US drug total spending, while half of total spending growth is on new drugs available for <2 years, and with oncology comprising 35% of all 2015 new drug launches.1 Biologics in the US comprise less than 1% of filled prescription but a growing share of the 28% of total drug spending.2 Further, manufacturers have been using government incentives through the Orphan Drug Act of 1983 to launch new drugs to treat rare diseases that would not otherwise be economically viable. One estimate is that more than 500 orphan drugs have been approved since the act’s passage.3 Drugs approved to treat rare diseases also made up 40 percent of new drugs approved by the FDA in 2016 and nearly 50 percent of new drugs approved in 2015.4 Prices for orphan drugs are also estimated to be much more expensive, for example, $140,443 average cost per patient year for an orphan drug compared to about $27,756 for a non-orphan drug.5 Finally, the latest average R&D costs and risks to be bring a new drug to market is around $2.6 billion. R&D costs and risks for biologics have even higher inherent failure rates than non-biologics.6

However, business and societal questions have been raised about the practicality of raising revenue mainly through price increases.7-8 What is becoming clear is that the current practice of biopharma pricing of specialty medicines is not economically sustainable in the long run. There is a growing gap between rising costs of pharmaceutical R&D for drug innovation and individual/societal willingness and ability to pay for this innovation. Numerous commercial complications arise from launching specialty medicines catering to orphan drug-like patient populations. Drug pricing and demonstration of value problems are especially acute with targeted personalized anti-cancer medicines.9-11 The question this research presentation will address is what can biopharma companies do to empirically demonstrate value for specialty medicines when conducting sales and marketing?

Traditional commercial model design that emphasizes unit sales growth will not suffice. The literature lacks practical mechanisms companies can use to bridge implications from health economics & outcomes research (HEOR) and real world evidence (RWE) models with commercial operations for successful demonstration of drug value that benefits patients and the healthcare system. This proposed research presentation will provide a conceptual framework that combines traditional HEOR/RWE models with commercial analytics (defined as commercial model design, payer/patient/sales/marketing analytics, commercial analytics innovation center, and cloud information management) to support informative sales and marketing activities of specialty medicines. The result will be a more effective demonstration of drug value through sales and marketing by improving health outcomes, cost-effectiveness, and overall healthcare spending.

Commercial analytics activities are becoming interdependent as opposed to distinctly-operating functions. Payer/patient analytics will be the principal emphasis and drive all commercial decisions leveraging outcomes from HEOR/RWE modeling. All remaining analytics will be to support payer/patient outcomes. A new approach to commercial analytics is needed, requiring greater alignment among these activities, an open-system framework in solving commercial problems, data environment constructed to support these activities, and leadership/organizational changes.



1. IMS Institute for Healthcare Informatics. Medicine use and spending in the U.S.: a review of 2015 and outlook to 2020. Parsippany, NJ: April 2016.

2. Morton F, Stern A and Stern S. The impact of the entry of biosimilars: evidence from Europe (21 July 2016). Harvard Business School Technology & Operations Management Unit Working Paper No. 16-141, published online 24 July 2016, last revised on 25 January 2017, available at SSRN: or (accessed 13 February 2017). 
3. PhRMA Chartpack: biopharmaceuticals in perspective. Washington, D.C.: Spring 2016.

4. Tribble S. Sen. Grassley Launches inquiry into orphan drug law’s effect on prices. Kaiser Health News, published online 10 February 2017, available at (accessed 23 March 2017).
5. Tribble S. GAO will investigate skyrocketing prices for orphan drugs. NPR, published online 22 March 2017, available at (accessed 23 March 2017).
6. DiMasi J, Grabowski H and Hansen R. Innovation in the pharmaceutical industry: new estimates of R&D costs. Journal of Health Economics 2016; 47: 20-33.
7. Rockoff J. Pricey drugs are hurdle for new biotech CEO. Wall Street Journal 2016; June 7: B1-B2.

8. Walker J. Drug makers raise prices despite protests.  Wall Street Journal 2016; July 15: B1-B2.

9. Bach P. A new way to define value in drug pricing. Harvard Business Review, published online 6 October 2015, available at (accessed 18 August 2016).

10. Howard D, Bach P, Berndt E, et al. Pricing in the market for anticancer drugs. Journal of Economic Perspectives 2015; 29: 139-162.
11. Schnipper L, Davidson N, Wollins D, et al. American society of clinical oncology statement: a conceptual framework to assess the value of cancer treatment options. Journal of Clinical Oncology 2015; 33: 2563-2577.

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