Wanted: More and better specialty data
January 23, 2019
Fact: specialty medications are expensive. Fact: more of these medications enter the market every year. The innovation is to be applauded, but the status quo can’t be sustained.
Solutions? This is where it gets tricky. Stakeholders have been trying various approaches, but costs keep rising and evidence lags behind funding decisions. And that’s not fair to patients, prescribers, manufacturers, or payers.
There’s an elephant in the room and it’s called data. Specifically, we need more data on how specialty drugs perform in the real world. We can then aggregate the data into real-world evidence (RWE) that lays bare a drug’s real value to patients, enabling us to make the tough calls on which treatments to develop, prescribe, and pay for.
Randomized controlled trials (RCTs), as currently designed, serve an important purpose: the data they generate allows regulators to decide which drugs to approve. The problem arises when stakeholders attempt to use the data beyond its natural boundaries. Patients in clinical trials are hand-picked to meet specific and narrow criteria:1 they must have a BMI between X and Y; have diabetes but no heart disease; be free of comorbid conditions requiring medication, and so on. In the real world, patients come in all shapes and sizes, have several medical conditions, and have patient support programs and apps to help them manage their medication regimens. Off-label prescribing adds a further layer of complexity to the clinical picture.
In such real-life scenarios, a drug may perform less reliably than RTC results might suggest. In an analysis of 21 IV cancer therapies involving over 8,000 Ontario patients, for example, population-based studies yielded significantly poorer survival and toxicity outcomes than clinical trials.2 In another real-world study, side effects and suboptimal efficacy led 44% of patients on a first-line leukemia drug—a drug intended for long-term therapy—to switch to another treatment within 3 years.3 On the flip side, some medications show greater muscle in the real world than expected from clinical trials.
We need more—a lot more—data of this type. Much of the real-world data being generated today does not exist in a form stakeholders can effectively use. The data may not apply to the patient scenarios most useful to clinicians and payers. Also missing are the processes that would allow stakeholders to access, interpret, and validate the data so it earns the required trust.
The scarcity of actionable real-world data sets back manufacturers, payers, physicians, and above all patients. Absent data on how a specialty drug performs in a particular type of patient, a prescriber is left to make educated guesses. She prescribes treatment A, waits three months, and when the patient doesn’t improve, switches to treatment B, C, and D—finally stumbling on the door with the prize behind it. Real-world outcomes data could have helped her select the winner first.
With the rumblings of major reforms to contain drug-spend—the Patented Medicine Prices Review Board proposal and national Pharmacare come to mind—the data gap raises louder alarm bells than ever. Only with robust, high-quality data can we create equitable reforms that benefit all stakeholders in specialty drug treatment.
On an encouraging note, policymakers have shown a collective will to bridge the gap. For example, the provincial/territorial Expensive Drugs for Rare Diseases (EDRD) working group has set itself the goal of using more real-world evidence (RWE) to evaluate specialized and complex drugs.5 In the realm of rare disease and cancer drugs, Health Canada and CADTH are working to use RWE more judiciously in the assessment of these medications—and to offer guidance on RWE generation to manufacturers.6 Then there’s the Alberta Real World Evidence Consortium, which seeks to strengthen the province’s RWE ecosystem.7 Even the Pharmaceutical Advertising Advisory Board (PAAB) plans to embrace RWE as a form of evidence.7 In a proposal to amend its Code, PAAB describes four categories of real-world data, including “market dynamic data” to support claims of drug persistence and switching.8
These initiatives offer encouragement, but we have a way to go. We need to move beyond our current fragmented, catch-as-catch-can system and create a RWE-generating infrastructure for specialty medicines. We can begin by leveraging the data that already exists so it becomes a tool for better and more cost-effective patient care.
All the pieces are in place: with a few bold moves by manufacturers and payers, and cooperative dialogue among stakeholders, 2019 can be the year we pull RWE from the ivory tower down to earth.
Unlocking the combinations
Prescribed responsibly, combination therapy involving specialty drugs often works well and may even advance medical science. At the Princess Margaret Hospital (PMH) in Toronto, for example, the Tumor Immunotherapy Program is exploring different combinations of immune-based therapies to mount a stronger attack on cancer.4 This forward-thinking approach cements PMH’s reputation as a cancer treatment pioneer.
In many cases, however, data on combination therapies for chronic diseases stays within the walls of a particular treatment centre. What if we developed a broader, more systematic approach to collecting real-world data on combined specialty treatments? What if we could share the data throughout regions, nationally, and even beyond? Imagine the benefit to industry stakeholders—and above all, to patients.
The perils of automatic pilot
Here’s a typical scenario in the Canadian specialty-pharma landscape: a payer works out a deal with a manufacturer based on a drug’s performance in RCTs and on Health Technology Assessment (HTA) recommendations. Lacking information on the medication’s effectiveness in real-world scenarios, the payer is left to cover the drug, year after year, while patients may miss opportunities for better treatments. This makes as much sense as, say, paying for 64-speed Internet without knowing whether you’re actually receiving the service. Perhaps the speed has gone down to 32, or even 16. How would you know? That’s where data comes in. Only through systematic use of real-world data will the industry stay healthy as it matures.
References
Clay RA. More than one way to measure. Monitor on Psychology. September 2010, Vol. 41, #8. Accessed at https://www.apa.org/monitor/2010/09/trials.aspx
Phillips C et al. Assessing the difference in efficacy and effectiveness of cancer systemic treatment: A comparison of clinical trial (CT) overall survival (OS) and toxicity data with population-based, real world (RW) OS data. J Clin Oncol 2018; 36 Suppl:6581.
How well do people do in the real world? CML-IQ, September 21, 2018. Accessed at http://cml-iq.com/well-people-real-world/
The Princess Margaret feature publication. The Princess Margaret Foundation/UHN. 2017.
“Significant developments in EDRD policy announced by CADTH & the EDRD working group,” MORSE Consulting webinar, November 8, 2018.
CAPT Conference Workshop: Defining “Decision Grade” Real World Evidence (RWE) and its Role in the Canadian Context: A Design Sprint. October 21, 2018.
Alberta Real World Evidence Consortium website. Accessed at https://albertarwe.ca/
PAAB gets real. Life Sciences Bulletin, December 17, 2018. Accessed at https://www.fasken.com/en/knowledgehub/2018/12/paab-gets-real