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Simple persistence (or adherence) to therapy

One of the more innovative parts of the 1997-98 model described in the Marketing New Pharmaceutical Products example was its method of tracking patient flow. At the time, pharmaceutical marketing was heavily oriented toward traditional primary research focused on small physician/patient panels and drug utilization. The Ventana model tracked these things, but it also tracked flows of patients as they moved onto and off of therapy. To do this, it was necessary to infer the nature of these patient dynamics. Ventana was able to indirectly measure them from the types of prescription data widely available at the time – so-called NRx and TRx data sold by industry data vendors.

Some managers were uncomfortable with indirect measures of patient persistence on therapy. They were used to direct measurement of important market parameters. Consequently, they made a special purchase of blinded data on individual patients. The data confirmed Ventana’s inferences. This gave credibility to Ventana methods, but more important, it suggested the use of a new source of information about patients. Indirect measurement can only work in certain situations, and more details of treatment are knowable with the patient-level perspective.

Two years after Ventana showed the marketing value of the patient perspective, data vendors were providing a new syndicated data product that replicated Ventana’s initial approach to patient-level data. This was probably an independent development motivated by different factors. The important fact is that blinded patient-level data is now in common use by pharmaceutical companies, and the current challenge is interpreting what it means correctly. The data afford important advantages, but it can be misinterpreted if used without reference to other types of data, and Ventana has seen significant evidence of this. While the vagaries of human motivation and memory are not relevant to this type of data, issues of patient sampling as well as the data rules used to aggregate individual patient histories introduce significant interpretation problems.

Defining persistence, adherence and compliance for episodic or intermittent conditions

Over the last decade, Ventana has continued to develop its patient-level data approach, treating increasing more complex patient flows. The pharmaceutical information services industry has moved faster to profile even more complex treatment scenarios.

The key difference between what the industry does and what Ventana does is that data vendors largely restrict their offering to one type of data, and they sell it by therapy area. Data vendors allow their customers to define the data rules, but they typically set bounds on what is possible and they presume expertise about the implications of data rules. Often, this expertise is not in evidence. Because Ventana builds systemic models of markets, we cannot isolate our attention to one kind of data. Our focus is on the consistency of data across many data types ranging from patient and physician survey/opinion data through aggregate data on markets (e.g., incidence/prevalence of disease and national script data) to patient-level detail. For us, understanding inconsistencies between data sources is critical, and insight emerges from identifying and explaining inconsistencies.

Ventana analysis has surprised, but has ultimately won over thought leading physicians. In particular, explicit recognition of periods when patients were not on therapy has proven important in understanding the dynamics of drug treatment and the magnitude of unmet medical need. Extant patient-level data tends to focus on the dynamics of treatment, largely ignoring the characteristics of periods when patients are not using prescribed medicine. Inattention to this blind spot can (and has) caused medical experts to both overestimate and underestimate the size of unmet medical need depending on the dynamics of a particular disease and therapy. For example, the nature of the medical condition makes the requirements for—and interpretation of—patient-level data on schizophrenia and migraine patients fundamentally different.

Ventana continues to work with industry data vendors to improve their offerings relating to patient flow. We compete and cooperate in the growing information services industry.

Synthesis of disease progression over lifetimes

Ventana’s most ambitious projects involving patient-level data require the synthesis of virtual life histories from the fragments of data on real individuals that are available. Typically, in the United States it is difficult to get large samples of patient histories (say hundreds of thousands or millions of patients) for more than 5-6 years. People change health plans and physicians too often to make long-term data linkages reliable. In Europe, much longer histories are available, but they are not yet close to providing birth-to-grave coverage. For conditions like diabetes or the umbrella condition “metabolic syndrome” (diabetes, hypertension, dyslipidemia and obesity), the evolution of conditions and treatment over decades is necessary to understand it properly. Using methods similar in philosophy to indirect measurement, Ventana has used models to transform patient-level information for the limited windows of observability on real patients into longer histories on virtual patients. Profiling and trying to explain these histories using simulation models has changed one pharmaceutical company’s views on metabolic syndrome. Where the data can support it, Ventana recognizes this as an important research area. Currently, the state and pricing of most vendor data in the United States makes such research prohibitively expensive.

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In 2001, Ventana was asked to predict the sales performance of a generic drug after the patent on it’s branded equivalent expired. One advantage of Ventana modeling is a better understanding of the root causes of market dynamics. This allows the models to make contingent predictions of unprecedented or unusual market behavior despite using seemingly ordinary assumptions about future market evolution. One example of this was the predication that the generic form (lovastatin) of a mediocre branded cholesterol-lowering drug (Mevacor) would become far more popular in the United States than it was when it was the market-leading drug a decade earlier. Generic drugs are significantly cheaper than branded drugs. Yet, conventional wisdom in the pharmaceutical industry at the time was that the availability of superior branded drugs as well as intense promotion to both physicians and patients virtually guaranteed that generics would at best equal the sales of their branded form when it went off patent. And most likely, generic sales would quickly fade away over time to almost nothing.

The Ventana simulation of the market predicted that sales of the generic would grow steadily after launch instead of dying away, and this forecast proved correct. Interestingly, however, conventional wisdom was so entrenched in peoples’ minds that for a few years after the forecast marketers were still finding reasons that the lovastatin example was an extremely rare case that would not be repeated. Consequently, when later Ventana models of three other pharmaceutical markets predicted similar behavior correctly, the models got credit for surprising predictions that proved correct.

Correct forecasts of sales for new products are usually “surprising,” but these examples only reflect the value of identifying the true causal drivers of market dynamics instead of basing forecasts on simple smoothing of historical sales, static correlations among historical variables, reasoning by analogy, and/or prescriptive models asserting the existence of a simple mathematical formula that explains how innovation always spreads across a customer base. In the pharmaceutical examples cited here, the innovation was not just in the drugs on the market but the managed care system as well. This innovation had been going on for decades, but in the case of managed care, it proceeded as a “trial and error” process that took a while to mature. Ventana’s success in predicting sales came from looking at the business system broadly and recognizing the interaction of innovation in multiple areas.

In 2007-2008 Ventana had an opportunity to re-analyze the marketing issues in play after drug patent expiration across six countries and seven therapy areas from 1995-2007 (27 drug classes; 308 molecules; and 260 generic events). We see the issues and their analysis much the way we saw them in 2001. Facts change, markets evolve and data accumulates; but the necessary model concepts and scope remain essentially the same. The dominant part of the effort required to predict the impact of generic drugs has become the collection, qualification and processing of the ever-growing volume of conflicting data.

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The model provided several important, if somewhat vexing, benefits. The most immediate benefit was a re-evaluation of the market for the product. By using the calibrated decision-support model, the product development team discovered that earlier market assessments, which were based on spreadsheet calculations, had poorly analyzed the spread of the disease and the nature of the competition. Within weeks of using the model, the product development team revised its sales forecast scenarios. The high and middle scenarios declined by a factor of two, while the low scenario fell by a factor of three. The product development team then used the model to better optimize its marketing plan.

The company potentially got a larger and more significant organizational benefit as well. Drawing on events from the modeling experience, Ventana noted that the company’s process for evaluating new products put severe and conflicting pressures on product development teams. The teams were charged with being both enthusiastic developers of a new product as well as their own best critics. Peer review of the market assessments was minimal. Senior executives recognized that they needed a system that provided better institutional checks on the information they used in their new product portfolio analyses.

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Ventana turned the model over to its client to use during and after the drug launch. We received reports from two individuals that the pre-launch model predicted the post-launch sales quite accurately. Ventana was unable to verify these reports directly, but we were able to determine that the model accurately predicted the market share of the company’s drug in its drug class for about 18 months after the launch.

One aspect of the model was almost universally dismissed by the company’s sales department (as distinct from its marketing department). This was regarding the importance of pharmaceutical sales representatives. Company managers believed that the sales force drove sales in all phases of a product’s lifecycle. Ventana’s modeling confirmed the importance of the sales force during and immediately after the launch of a new product, but they also revealed that the dynamics of subsequent sales efficiency were more counterintuitive than most people realized. It took managers a few years to internalize the findings, but by 2002 there was generally good agreement on the impact of promotion to physicians.

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