The Problem with Primary Market Research

Pharmaceutical companies spend millions on primary market research (PMR) every year — and often walk away with the wrong answer. PMR plays a genuine role in the R&D process: interviews with physicians provide insight into treatment paradigms, patient types and “journeys,” and reasonable expectations for the future of a disease. Equinox Group routinely conducts such interviews to inform our modeling efforts. This type of research has been a staple in the industry for decades, making it something people at all levels of organizations understand and can use to extract actionable insights. So, what’s the problem?

Too often, PMR is asked to be something it is not: a replacement for rigorous techniques that can quantify the advantage of a particular drug over another and communicate how that advantage actually translates to patient share potential. Below, we examine the specific instances where PMR comes up short and explain the approaches we take at Equinox Group to overcome these deficiencies.

1.      Primary market research is not dynamic

Given that PMR consists of semi-quantitative interviews, much of the content of these interviews is only applicable so long as important details such as the treatment paradigm, current clinical data, and competitive pipeline remain unchanged. For example, if new post-approval data that come out show a drug to be a much greater improvement over the standard of care than previously thought, such as in the case of Kisqali in HER2-, HR+ 1st line breast cancer, any PMR done involving the current treatment paradigm and efficacy in this population prior to the new data being read out would immediately become outdated and of little use to development teams. So, if a company wants new insights into the potential of their drug in the market, they have no choice but to commit more time and money to additional PMR.

At Equinox Group, we handle this problem by creating dynamic, data-driven models that can instantly deliver new outputs with a few clicks of a button. Any clinical data, launch date, competitor, or price of an agent can be updated in our models as soon as that new information is available, resulting in a new patient share prediction for your agent.

Consider the Kisqali example. At launch, Kisqali showed a mere 0.5% improvement over Ibrance — barely enough to move the needle — and initial sales reflected that.

Figure 1: Kisqali at launch

However, years later Ibrance was found to have disappointing survival data, proving it to have been far less efficacious than previously thought. A slight improvement in efficacy for Kisqali was also shown over this period. As a result, Kisqali actually proved to be far superior to Ibrance and that was reflected in the patient share that it ended up receiving.

Figure 2: Kisqali update

In our model, this shift was captured instantaneously by updating a handful of numbers — a process that took seconds, not months at no additional cost.

2. Primary market research is not unbiased empirical data

As noted above, PMR is effective in obtaining insights from physicians regarding a variety of topics. However, these insights remain opinions — capable of being influenced by personal biases — and do not reach the level of objectivity of empirical clinical evidence. This is especially true in longer interviews, where respondents become fatigued and the quality of their answers deteriorates. Framing effects compound this problem: the way an interview is structured can meaningfully shift the responses given. While exercises such as conjoint analysis can yield a rough estimate of how one additional month of mPFS or a cleaner side effect profile affects commercial success, this method is not optimal.

As a core principle, the qualitative should only be used to predict the qualitative, while the quantitative should predict the quantitative. Results from PMR can suggest whether a drug will obtain “significant” share and dominate the market, but they cannot reliably pinpoint what that share would be.

By looking at historical drug launches and quantifying the effect of the clinical innovation of a drug on its patient share potential, we have a way of discovering the impact of these factors on commercial outcomes strictly based on peer-reviewed clinical data without the need for any guesswork or opinion.

3. Preference share ≠ patient share

While the outcomes of conjoint analyses are not without their uses, at times they are misinterpreted. Perhaps the most important of these outcomes is preference share – an estimate of the percent of physician’s that would choose a given TPP among the profiles of all relevant drugs in the market. Assuming that the interviews are conducted in a way that minimizes bias, the insights gained regarding the relative strengths of TPPs are actually of great value. However, they cannot be used as a proxy for patient share. This is in large part due to the fact that these interviews are incapable of capturing the intricate market dynamics that contribute to a drug’s share. Furthermore, the TPPs that are being assessed lack the necessary detail and often do not consider important factors such as order of entry or price. Therefore, just because we can obtain an estimate for what percent of prescribers favor TPP A over TPP B, it does not necessarily mean that we can know what share either of these agents will obtain. It is also important to note that the subjects of these interviews are, at times, not a nationally representative sample of prescribers.

We avoid these issues by deriving our patient share projections from a consistent, analytical framework that weights clinical innovation (which includes price), order of entry, and competitive environment according to the results of our extensive work with historical drug launches.

4. Primary market research is expensive and labor intensive

Finally, PMR projects often take months and impose large costs upon biopharmaceutical companies. As mentioned above, these costs may compound as new information makes additional research necessary.

In comparison, Equinox Group’s models can be completed in as little as 6 weeks and include two years of after-sales service from the project start date. Because our models are driven by published clinical data rather than primary fieldwork, they can be updated in real time by the client as new information becomes available — without incurring additional research costs every time the market shifts.

If you’d like to see more about this framework, we’d be glad to walk you through a live example. Feel free to schedule a meeting.

Since 1995, Equinox Group has provided analytics to support R&D decisions at biopharmaceutical firms, assessing the potential of drugs from discovery to launch, and anywhere in between. Equinox Group specializes in predicting the commercial performance of drug programs in all stages of research and development, delivering quantitative insights regarding:

• Disease Area Strategy

• Business Development Decisions

• Market Access

• Patient Share Forecasting

• Epidemiology and Patient Flow

Plan for the Best, Prepare for the Worst - Handling Uncertainty in R&D

Where the Uncertainty Lies

Assets in mid to late-stage development are seldom immune to uncertainty surrounding their efficacy and safety profiles as well as that of their competitors. When considering these variables along with launch timing and the potential for reimbursement friction, traditional techniques such as conjoint analysis are rendered ineffective. Such circumstances require far more objective, dynamic, and future-proofed approaches.

A Data-Driven Approach

Using our unmet need framework, Equinox Group consistently quantifies the clinical improvement offered by a drug using a metric that we call Clinical Innovation. This measure has proven to be highly predictive of peak-year patient share and has been the basis of our analyses for over 30 years.

By objectively quantifying the Clinical Innovation of a new drug and all of its relevant competitors, we enable our clients to prepare for all possible scenarios that they will face during development and to seamlessly toggle between them.

Wargaming with Multiple TPPs

By quantifying the uncertainty in a given indication, we can understand its implications with remarkable accuracy.

Consider, for example, a chronic disease with three agents currently in the market. Working alongside the client, we come up with a base case, an upside case, and a downside case for their new drug, taking into account efficacy, safety, side effects, dosing, administration, price, and launch timing. Under the base case, the new drug achieves 20.3% share of drug-treated patients by the end of 2031. Under the upside and downside cases, it achieves 29.3% and 14.7% respectively. As a result, in a world where no competitors are launching in the coming years, we have bounded the share potential between 14.7% and 29.3%. By assigning probabilities to each of the three scenarios, a simple weighted average will yield the expected share by the end of 2031 under the assumed conditions. In this example the modeled probabilities of the base, upside, and downside cases are 71%, 21%, and 8%. This results in an expected value of 21.7% patient-share by 2031.

Figure 1: Launching without a threat

Introducing Competitors

By studying the different scenarios in Figure 1, it is clear that the client agent has moderate potential. Now, consider a highly innovative competitor, which we call Threat 1. With Threat 1 launching one year later than the client agent, it will have considerable, yet diminished, impact on the terminal shares. Now, the client agent 10.4% patient-share in the base case and 19.0% and 6.1% in the upside and downside cases respectively.

Using the same probabilities of 71%, 21%, and 8%, we get an expected value of 11.9% patient-share by 2031. These probabilities can be informed by input of the client organization and approximated through Monte Carlo simulations, which are easily implemented into our framework when handling uncertainty around all relevant product attributes including launch timing.

Analyses like this can be made as complex as needed, allowing for up to 15 different drug profiles, characterizing the variety of potential attributes of your drug as well as all relevant competitors.

Figure 2: Launching with a threat

What About When the Data Changes?

Whether it be new clinical data pertaining to your agent or any of its competitors, the dynamic nature of these models allows client organizations to change the relevant inputs within a matter of minutes with no additional cost. That means no need for funding 50 additional interviews with leading physician’s, no need for creating new stimuli, no guesswork, and no time wasted in order to obtain new patient-share estimates. 

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Equinox Group has fine-tuned these methodologies and others over the past 30 years to help biopharmaceutical companies handle challenges in R&D. Our specialties range from disease-area strategy and market access to patient share forecasting and patient flow modeling.

To learn more about our process, click here to schedule a meeting with one of our practice leaders.

Measuring Risk in R&D

The Problem

It is no secret in the biopharmaceutical industry that a new drug’s clinical improvement is strongly predictive of the share of patients it achieves. Consequently, drugs that offer little clinical improvement expose developers to commercial risk.

Many organizations, while aware of this problem, continue to lack a proven methodology to identify and quantify that risk. This exposes them to the possibility of advancing undifferentiated drugs that will experience low market access and vulnerability to future competition.

The Solution

Equinox Group has quantified the relationship between clinical improvement and patient share by comparing the magnitude of improvement delivered vs. share achieved in the real world in a large set of historical drug launches. Clinical improvement reflects advances in efficacy, safety, tolerability, dosing, and patient outcomes. Our metric for clinical improvement includes all of these domains and measures the percent reduction in medical need the new drug offers relative to the standard of care. We call that measure of improvement  “Clinical Innovation”.

Measuring Clinical Innovation: A Data-Driven Approach

For 30 years, Equinox Group has used a validated, data-driven approach to measure the clinical innovation offered by new drugs across hundreds of indications. See this two-minute video explanation:

The Link to Commercial Potential

Over many years we have re-evaluated the relationship between clinical innovation and patient share; it has been remarkably stable over time. Below is a table of historical drug launches and where they score in the Equinox Group framework.

As a rule of thumb,

• High Innovation: Drugs that offer 10% or more clinical innovation nearly always dominate the branded segments of their markets.

• Medium Innovation: Drugs with 5% to 10% innovation usually compete well.

• Low Innovation: Drugs with 0-5% improvement are in a gray zone and may be seen as undifferentiated, especially by payers.

• Lack of Innovation: Drugs with negative clinical innovation nearly always get low patient shares and disappointing sales.

How Much Clinical Innovation is Enough?

The clinical attributes anticipated in early R&D programs are rarely realized by the end of development. As time goes on, assets often acquire “warts,” whether it be unexpected side effects, lower than expected efficacy, or both. Programs that start with modest clinical aspirations are likely to offer unacceptable levels of innovation at launch.  We call this phenomenon “attribute drift.”

We recommend that drug development teams aim to achieve clinical innovation of at least 5% to reduce exposure to attribute drift and the commercial risks described in the beginning of this paper. Our tools allow teams to tie the expected clinical attributes of a new drug first to its level of clinical innovation, and then to patient share to forecast revenue potential.

The Take-Home

Equinox Group’s proven method to measure clinical innovation and predict peak-year patient share for target product profiles is accurate.  It’s also less expensive, more flexible, and quicker to execute than conventional market research-based techniques.  We use this approach to inform decisions about individual programs (pipeline and BD), to help therapeutic area teams set aspirations, and to inform R&D portfolio assessments.

Who Are We?

Since 1995, Equinox Group has provided analytics to support R&D decisions at biopharmaceutical firms, assessing the potential of drugs from discovery to launch. Our validated techniques will save you time and money, as well as overcome the deficiencies of traditional market research.

We have evaluated thousands of drug profiles across hundreds of indications in oncology, immunology, rare diseases, cardio-metabolic disorders, infectious disease, neurology, pulmonology, urology, ophthalmology, and endocrine disorders. We also have tailored methods to solve the unique analytical challenges that arise in oncology and rare diseases.

Using this methodology, we have developed a suite of tools to inform whatever R&D decisions you’re facing in any stage of development, including: setting indication priorities, supporting go/no-go decisions, assessing market access and pricing potential, share forecasting, and patient flow modeling.