Health Economics: Data Mining - Pharmaceutical Executive

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Health Economics: Data Mining
Why No Stage of Drug Development and Marketing in the Brave New World of Biologics Can Be Without It


Pharmaceutical Executive


Throughout this evolution in plan design, research into patient sensitivity to out-of-pocket co-payments has been measured mainly using payments made for the drug received, rather than using the prices of competitor drugs that might have been prescribed for the same condition. In other words, payers use formularies to manage prescription-drug utilization—but there are no good studies that indicate how much patient variation in prescription-refill behavior is due to drug-benefit design compared with other factors.

The most glaring omission, from an economic standpoint, is the failure to control for a patient's ability to pay as measured by factors like income or net worth. It is reasonable to assume that patients with lower incomes are more sensitive to co-pay differentials. This raises potential issues of differential access to novel treatments based on a consumer's income level. There is also growing evidence that price sensitivity is affected by the nature of the condition being treated.

A recent study in Health Affairs of benefit design's effect on use of biologics found that cancer patients were relatively insensitive to co-pay differences. The fact that out-of-pocket co-payments for biologics and other targeted products can be several hundred dollars per month makes it even more important to understand the buying behavior of consumers.

THE MOVE TO POSTMARKET STUDIES

As useful as retrospective data can be in estimating a product's value for cost, ultimately payers, consumers, and regulators want stronger clinical evidence. Similarly, although much can be learned from modeling refill behavior against co-payments and income, the best way to understand the reasons for medication nonadherence is to ask the patient.


Enhanced Medical Claims Database
The administrative data typically used to describe drug-use patterns do not contain the variables that explain what motivates patient decision-making. Did patients discontinue their medication because (a) it was too expensive, (b) they didn't feel that it was working, (c) side effects, (d) they hated feeling dependent on it, or (e) some other reason. You need to survey patients to get answers.

Understanding medication adherence, particularly for patients with chronic conditions, is critical not only because adherence is necessary to obtain the clinical effect. It is also essential to the drugmaker—building patient use of one's product is partly accomplished by minimizing discontinuation or switching. So understanding the reasons for nonadherence helps manufacturers develop strategies to support patient compliance.

The situation is complicated further by the fact that it is the physician who writes the prescription. Data on physician prescribing patterns is available, and drug companies routinely use it in their communications with doctors. But the growth of consumer-directed healthcare has increasingly involved patients in decision making. Still, little is known about the doctor–patient interaction and how it influences prescribing patterns.

Such needs for more detailed clinical and behavioral information can best be met by primary data collection. As a result, the pharmaceutical industry is moving rapidly to design and implement late-phase studies containing safety, health-economics, and patient-reported outcomes, and real-world effectiveness measures. Moreover, the FDA is under increasing pressure to require manufacturers to monitor real-world drug safety after their products are approved for marketing. Such studies can range from simple product registries with no comparison group to cohort studies validating safety signals found in medical-record claims to Phase IV trials.

Use of retrospective healthcare data may make the recruitment of investigators and patients more efficient, provide a preliminary assessment of protocol feasibility, and help determine the sample size needed to detect statistically significant differences in outcomes. The data also have a variety of other uses, including determining the length of follow-up needed to detect certain endpoints, such as hospitalization risk. In fact, many features of a study protocol can be tested this way.


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