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WhitePaper Market Research Meets Market Reality: A Dynamic Approach to Discounting Physician Preference Share 2011 Kim Morneau MARKET RESEARCH MEETS MARKET REALIT Y Market Research Meets Market Reality: A Dynamic Approach to Discounting Physician Preference Share As the pharmaceutical market has evolved at tremendous speed, so too have our measures for understanding physicians’ preference for treatment options – but the same cannot be said of approaches for discounting preference share. Experience shows that the key to success here lies in a dynamic approach grounded in both market research and market reality… Understanding physicians’ preference towards treatment choices The founding principle Conjoint methodology For the last three decades, one key quantitative approach has enabled us to better understand physicians’ preference for treatment options: conjoint methodology. Introduced by Green and Rao in ‘71, conjoint studies allow physicians to view various combinations of product attributes intended to represent potential ‘target product profiles’ (TPP’s). After viewing each TPP, physicians are asked to indicate their level of preference, and/or rank them based on their likelihood to prescribe.1 In doing so, they offer us some insight into how that product will perform in-market. 2 Copyright ©2012 Ipsos. All rights reserved. MARKET RESEARCH MEETS MARKET REALIT Y Evaluating the different discount methods The second generation Discrete choice models This approach served us well enough until the ‘90’s, when a new innovation fuelled the next generation of conjoint. Thanks to the introduction of web-based data collection and software for analysing responses, ‘Discrete Choice Models’ were born. Regardless of survey design, market researchers unanimously agree that preference share does not equal peak market share. The reason? Unlike the real world, the test environment is devoid of any market barriers, be they physician awareness, formulary access, competitive counter detailing, promotional spend, or others. Although a fairly similar animal to the conjoint design, the discrete choice model has one key difference. While both methodologies enable physicians to view a series of potential TPPs for a new drug (in which product attributes are always varied), a discrete choice model requires the physician to choose the profile they most prefer. So how do we account for the market barriers that will inevitably be present inmarket? The ‘rule of thumb’ method Useful but limited The most simplistic approach is ‘rule of thumb’. This sees claimed preference share discounted by 50-70%, a figure derived by comparing claimed preference share from market research, and actual market share at the point of peak sales. This is based upon finding the average variance from a large sample of case studies. In forcing the physician to choose a single TPP instead of providing a rating for each, discrete choice is thought to more accurately reflect preference among a specific set of product attributes. There still remains an inherent difficulty, however; physicians rarely select a single drug to treat all patients. Instead, they choose from a small ‘armamentarium’ that best matches patient characteristics and product attributes. The benefits of this approach are fairly clear; it’s easy to use, and the calculation can be done quickly with limited data and expense. A fusion of the two The choice-based model The drawback, however, is its inability to correlate, directly, the discount rate with influencing factors such as the level of promotional spend or the number of competitors in the marketplace. Additionally, it implies that all physicians will become prescribers of the product, which we know is not necessarily true. In attempts to overcome this limitation, the past ten years have seen further evolution of the conjoint methodology into a fusion of traditional conjoint and discrete choice. This is known as the ‘choice-based model’. Like its predecessors, the choice-based model exposes the physician to a series of TPP’s with varying product attributes. After viewing each TPP, the physician completes an allocation table. Herein, they distribute their next one hundred patients with a certain condition among all available treatment options in the competitive set, including the test product. The result is what we call ‘preference share’. Specifically, this measures the depth to which physicians are interested in prescribing the product in question. According to a retrospective analysis conducted by Sobel and Brodsky, while this approach may be fairly accurate in aggregate, it tends to be vastly incorrect for individual observations.2 This is certainly a useful measure, but one that’s still flawed; in spite of the constant evolution of methodologies for predicting preference share, we still see in all conjoint designs an overstatement of modelled preference relative to actual market share. Acceptance of these overstated results can have significant consequences for an organisation. It may inappropriately prioritise one molecule over others in a R&D pipeline, result in the purchase of unprofitable asset, or cause an incorrect allocation of promotional resources. The key, then, is to find an effective approach to discounting survey results – but, unfortunately, these have failed to evolve at the same pace… 3 Copyright ©2012 Ipsos. All rights reserved. MARKET RESEARCH MEETS MARKET REALIT Y Getting more sophisticated Measuring breadth and depth There is a more sophisticated approach that is commonly used in the pharmaceutical industry, which accounts for both breadth and depth of prescribing. This calibration typically discounts both the percentage of physicians who are likely to prescribe the product (breadth) and the share of patients they will prescribe it for (depth). Usually, this calibration methodology applies different discount factors depending on the level of commitment the physician has indicated about adopting the product into his or her armamentarium. Here’s a basic example: MD Calibration Factor Preference Share Calibration Factor Claimed Preference Share Peak Share Intention To Prescribe % of MDs “Definitely will” 25% X 0.9 X 36% X 0.9 = 7.3% “Probably will” 42% X 0.4 X 26% X 0.3 = 1.3% “Might or might not” 19% X 0.05 X 15% X 0.1 = 0.1% “Probably will not” 10% X 0.0 X 10% X 0 = 0% “Definitely will not” 4% X 0.0 X 5% X 0 = 0% Adjusted Peak Preference Share 8.7% The disadvantage of this type of calibration technique is that it takes a significant amount of data to achieve reasonably accurate discounting factors. Experts recommend, at minimum, preference share data from primary market research studies going back at least five years spanning multiple brands, therapeutic categories and physician specialties – and, on top of this, actual market share for the same brands from a secondary data provider.3 Given that calibration factors can vary by therapeutic class, physician specialty and geographic location of the respondent, we need this level of data for each country in which we wish to calibrate preference share. 4 Copyright ©2012 Ipsos. All rights reserved. MARKET RESEARCH MEETS MARKET REALIT Y A new way of discounting preference share The inherent problem Historic analogues and why more isn’t always better Regardless of how sophisticated we become at deriving calibration factors, and how much granularity we can achieve, both of these methods suffer from the same inherent downfall: their reliance on historic analogues to derive a calibration factor… A truly dynamic approach Identifying market barriers, local and global There is, however, a more dynamic approach to preference share conversion – that is, to identify the barriers likely to hinder prescribing behaviour and estimate the percent of physicians or patients who will be impacted by each barrier on an ongoing basis. Some market researchers tend to believe that the larger the number of observations used to derive conversion factors, the better. We’ve all seen leading research companies tout 20+ years of examples, spanning 30+ therapeutic categories, clearly inferring that their robust number of observations will lead to a more accurate calibration factor. In some cases, this may be true; in others, being so focused on the past can actually skew the findings. This approach should account not only for the global barriers that will be present in all geographic markets and therapeutic areas, but also the localised barriers that most brands encounter. These local barriers are specific to the healthcare system where the product is being marketed, and also the distribution channels through which it is dispensed. The table below outlines some of the most common barriers that cause market share to be lower than claimed preference share. To explain: the calibration factors derived from these data sets, even the more targeted ones, are only meaningful if the pharmaceutical business model, and the framework in which physicians make treatment choices, are the same now as they were when the analogues were derived. By contrast, we know that in many geographic markets – both developed and emerging – the regulations for promoting pharmaceuticals, as well as reimbursement policies, have changed significantly over the past 10 years. For example, the introduction and evolution of the EFPIA code in Europe, and of the PhRMA code in the US, have significantly restricted the sales and marketing tactics employed by pharmaceutical manufacturers. In our experience, this, in turn, has impacted both the percentage of physicians who become aware of new products, and the speed at which physicians adopt them into their treatment paradigm. Global Barriers Local Barriers Physician Awareness Product Availability Physician Adoption Product Affordability Incorporating market reality Applying real-world knowledge In a perfect marketplace, each barrier would equal 0% thus causing no deflation to preference share. However, with the exception of orphan drugs, branded pharmaceutical products rarely compete in a market free of restrictions. What’s more, the magnitude of each barrier changes throughout the life of a drug. For example, during the first six months that a product is available, we can see significant changes to the rates of each barrier on a weekly or monthly basis, especially for physician awareness and product availability. Physician adoption and product affordability, however, tend to evolve more slowly during the first 18 - 24 months. 5 Copyright ©2012 Ipsos. All rights reserved. MARKET RESEARCH MEETS MARKET REALIT Y The 4 key barriers A closer look Physician awareness tends to build in a logarithmic pattern, with the majority of brands exceeding 50% awareness by the eighth month in-market.8 In addition to cumulative detailing reach, our experience shows that unmet medical needs in a therapy area also dictate how fast awareness will build. For example, awareness of a new oncology brand typically builds the fastest, with many physicians aware of the brand even before the start of promotion by the manufacturer. Barring differences in unmet medical needs, however, there appears to be nominal variance in awareness uptake across therapeutic classes. To better understand this approach, let’s look at each of the barriers in turn: 1. Physician awareness: a fundamental barrier Physician awareness of the brand is the most fundamental barrier to gaining market share. After all, if the physician cannot recall the brand name unaided, it is unlikely to be part of his or her set of treatment options when writing a prescription. A good example of this can be seen in a retrospective analysis of more than 300 brands launched since the EFPIA code was introduced in Europe; less than 6% of the brands achieved physician awareness greater than 90%.4 Similar results can be seen for brands launched in the US since 2003 when PhRMA guidelines were rolled-out. If we can approximate the percentage of physicians who are likely to become aware of the product through detailing and other promotional avenues, we can estimate the percentage of overstatement due to ‘imperfect awareness’. Accordingly, preference share should be discounted by the percentage of physicians who will not become aware of the product. Simply put: the physician cannot prescribe the product if he or she is unaware of it or unable to recall the product name. Despite restrictions on promotion, however, physicians’ cumulative spontaneous brand awareness continues to be highly correlated to cumulative detailing reach during the first 24 months that the brand is in-market (Pearson Correlation = 0.942). To put some figures to this: of the brands launched during the past 20 years, less than 10% detailed more than 80% of the physicians in the prescribing universe.5 In fact, on average, marketing teams deploy enough sales force to cover around 60% of prescribers in the marketplace, who manage 70-80% of the patients within the disease state6. However, this does not necessarily mean that awareness will automatically be capped at 60-70%. It is common to see physician awareness of a brand exceed the percentage of physicians in the universe who received a detail by 7-10%.7 This can be attributed to the other ways in which a physician may become aware of the product: reading about it in a medical journal, seeing a presentation of the data at a clinical symposium, hearing about it from a colleague, etc. 6 Copyright ©2012 Ipsos. All rights reserved. MARKET RESEARCH MEETS MARKET REALIT Y 2. Physician adoption: multiple influencing factors What causes some products to be adopted within months of launch and others to take several years? The variation in physician adoption can be attributed, in part, to certain product characteristics. When it comes to oncology compounds, our experience shows that adoption rates are impacted by many factors, including the number of alternative treatments, tumour type, availability of a biomarker test, therapeutic class, and more… Although physician awareness is driven largely by detailing reach, the rate to which physicians begin prescribing a new brand is mainly driven by the characteristics of the product and, to a lesser degree, by detailing frequency. A recent analysis of oncology compounds launched in the US and 5 European markets since 2003 showed three distinct trajectories for physician adoption – depending on the market environment and certain product characteristics. On average, peak share for the first indication of these compounds occurred around 2.4 years after launch. Others followed a more traditional 5-year-to-peak trajectory. A proportion of these brands, however, achieved peak share for the initial indication in less than 18 months.9 Number of treatment alternatives: For a first indication, the number of treatment alternatives is one of the most influential factors on the speed of adoption. When the number of competitive alternatives is limited to two or less, physicians begin prescribing the new drug quickly and peak sales are generally achieved in under two years. However, when a new drug enters a crowded marketplace and offers little improvement in either efficacy or safety, physician adoption is inevitably slower. Under these conditions, achieving peak sales can take upwards of three and a half years.10 Tumour type: Tumour type also plays a role in determining how quickly physicians adopt a new oncology drug. Newly-approved drugs to treat solid tumours reach peak share in approximately 2 years, while drugs to treat haematological cancers take nearly a year longer.11 Availability of a biomarker test: Similarly, the availability of a biomarker test to predict the likelihood of response to certain drugs also impacts the rate of physician adoption. Whilst the introduction of biomarkers into the marketplace has allowed physicians to better segment their patients, it also appears to slow physician adoption down. A drug with a biomarker test typically takes about six months longer to reach peak share than one without a qualifying test.12 One hypothesis for this is that the biomarker pre-empts physicians from using that drug for all patients, and there is a time lag between testing and treatment for those specific indications. Source: Ipsos Healthcare Global Oncology Monitor Therapeutic class: Finally, the rate of physician adoption can also vary by therapeutic class. Historically, physicians have more readily started prescribing new products for diseases in which there is a visible symptom that can be monitored to ensure the drug is working – and, of course, when there are limited consequences if the product does not perform in line with clinical trial results. After 24 months in-market, over 40% of physicians who were aware of new drugs for pain management and birth control had prescribed the product for at least one patient; by contrast, only 24% of the physicians who were aware of new respiratory and metabolic products had ever prescribed them.13 7 Copyright ©2012 Ipsos. All rights reserved. MARKET RESEARCH MEETS MARKET REALIT Y End of 24 Months % Aware % Prescribing Conversion Rate Pain management 72% 33% 46% Cardiovascular 70% 23% 33% Gynaecology 67% 28% 42% Metabolics 72% 17% 24% Neuroscience 77% 24% 31% Respiratory 75% 18% 24% Urology 70% 22% 31% 3. Product availability: a straightforward barrier – or is it? Product availability, also known as distribution, refers to the percentage of hospitals, pharmacies and physician offices that will stock the drug. In contrast to physician awareness and adoption, product availability is a far more straightforward barrier, and one that pharmaceutical manufacturers can more easily estimate. In geographic markets where drugs are dispensed primarily through retail pharmacies – US and Europe, for example – distribution tends to be less of a barrier. This is because most pharmacies are willing to stock multiple brands in a drug class and can order additional stock within a day or two. However, product availability plays a larger role in countries where pharmaceuticals are primarily dispensed through hospitals, such as China, or at the physician’s office, as seen in Singapore. When drugs are dispensed through institutions or private practices, it is not uncommon to find the brand selection limited to two, sometimes three, options per drug class. This is in part due to storage space constraints but also, in some markets, a desire to limit the amount of cash tied up in inventory. How do we accurately capture the effect that limited product availability will have on physician usage? Simply, the percentage of expected distribution should be weighted to account for the percentage of patients who are treated at the institutions or physician offices that will carry the product. For example, if a new Hepatitis B drug will be available in 60% of the Level III hospitals in China, and those same hospitals are known to treat 80% of all HBV patients in China, the weighted distribution rate is 80%. Subsequently, preference share should be discounted by 20% to account for the patients who are treated at institutions where the new drug will not be available. 8 Copyright ©2012 Ipsos. All rights reserved. MARKET RESEARCH MEETS MARKET REALIT Y 4. Product affordability: growing in importance This holistic approach would be further complemented by the inclusion of a patient component; sufferers of the condition treated by product X can provide feedback around their willingness to accept certain prices that are tied to the tier statuses already being modelled. The integration of these three factors – physician feedback, payer probabilities and patient price elasticity – provides the most complete picture of how product affordability will impact preference share and a subsequent US-based forecast. Finally, product affordability is emerging as a more meaningful market barrier in both developed and emerging markets. Below are just a few of the pricing and reimbursement policies that have recently been put in place around the world; they point to the growing concern about the affordability of pharmaceuticals: • Healthcare reform legislation passed and currently being implemented in the US Outside of the US, however, barriers created by product affordability may be more straightforward; many governments dictate which products will be reimbursed under the national healthcare system and which ones will not. Moreover, if a brand is included on a National Reimbursement Drug List, there is typically little to no expense to the patient and subsequently, product affordability is 100%. By contrast, if the government has not agreed to reimburse it, the patient bears the full expense and product affordability may become the most influential barrier of all. • A freeze on prices for three years in Germany (from August 2010) • 2.5% price reduction on generics and introduction of generic tenders by AIFA in Italy • Introduction of mandatory 7.5% price cuts on innovative drugs provided through the public health system in Spain In many cases, new and expensive specialty drugs are either denied reimbursement coverage by national healthcare systems and private insurance plans, or linked to high out-of-pocket co-pays by the patient. In such cases, many physicians will opt for a more affordable brand that will cause less financial burden to the patient. Let’s take China as an example: as of 2009, China’s National Reimbursement Drug List (NRDL) included 2,127 medications, of which 1140 are Western Medications.14 For these 1140 drugs, product affordability is not a barrier; the Chinese government has negotiated pricing with each respective manufacturer to enable the cost of the medication to be covered by the national and/or provincial governments. However, as multi-national pharmaceutical companies continue to expand distribution of their branded products into China, many will not be granted placement on the NRDL. The desired price will exceed what the government can pay, or acceptable generic alternatives will be available. Then, in other cases, the product may eventually be added to the NRDL but will have to wait until the Ministry of Health reconvenes to review all new drug applications and amend the list. It is therefore imperative to understand the likelihood and timing of a new product’s inclusion on the NRDL – as well as the proportion of patients willing to fill the prescription if they have to pay cash for it. Without this knowledge, preference share becomes just that: a preference that may or may not be reflected in the marketplace. In the US, the impact that a new drug’s formulary position will have on prescribing is most easily approximated through primary market research. The questionnaire is structured such that physicians are initially shown the base-case TPP, and instructed to assume the product will have full market access. After exposure, physicians are asked to record their preference share for this scenario. Subsequently, they are asked to assume that the TPP remains the same but with the formulary status changed to one of four scenarios: Tier 2 (no restrictions); Tier 2 (with restrictions); Tier 3 (no restrictions); Tier 3 (with restrictions). The four reimbursement scenarios are rotated and the physician is asked to record his or her preference share after viewing each scenario. In some cases, the formulary access scenarios may be segmented with more granularity to reflect various restrictions such as fail-first policies or prior authorisation policies. Using this exercise, it is possible to gauge how much the product’s formulary position will impact physician prescribing. This information is most useful when responses to the four scenarios are weighted to match the formulary access that the pharmaceutical manufacturer expects to achieve. However, manufacturers’ expectations are, at best, a biased estimation of a product’s likelihood to land on any one tier. A less biased approach is actually to survey medical officers who serve as the decision-makers around pharmacy benefits. By gauging their estimation of a product’s likelihood to fall on a certain tier, we can define a probability curve to use in Monte Carlo simulations of the forecast. Clearly, when a manufacturer suspects there is a chance that a new drug will not be granted reimbursement they need to be forearmed with market research that will help them estimate the impact on sales. The best route is to approximate the extent to which this will be a barrier to physician preference share by conducting primary market research among patients. We can then understand patients’ willingness to fill the prescription at various price points, and the resulting price elasticity curve will serve as the means of discounting preference share when the price is set. 9 Copyright ©2012 Ipsos. All rights reserved. MARKET RESEARCH MEETS MARKET REALIT Y Conclusions References A work in progress 1. Green, P.E. and Rao, V.R., 1971, “Conjoint measurement for quantifying judgmental data” Journal of Marketing Research, 8:355-363 It seems clear that the barrier approach to discounting preference share has significant advantages over traditional preference share conversion methodologies; these can be summarised as follows: 2. Sobel, K., &Bodsky, J., 2008, “Translating preference share into market share: rules of thumb that really work”, In: PMRG Annual National Conference 2008, 9-11 March, Phoenix, US 1. Each of the factors causing a reduction in preference share can be examined according to the specifics of the individual product: the characteristics of the drug, the level of promotional support that will be put against it, and the competitive market situation it will be entering. 3. Ziment, 2008, “Calibration of preference share”, In: 2008 Pharmaceutical Management Science Association Conference, Las Vegas, US 4. Data from Ipsos Healthcare Sales Force Effectiveness Monitor, Jigsaw 2. When a range is used for each barrier, a sensitivity analysis can be performed to determine the barriers that are likely to have the greatest impact, either reward or penalty, on brand share depending on the manufacturers marketing plans. 5.Ibid 6.Ibid 7.Ibid 8.Ibid 9. Data from Ipsos Healthcare Global Oncology Monitor (US & EU) 3. By outlining the specific assumptions around each barrier that the brand will encounter in-market, there is transparency around the rationale for discounting preference share. (This may facilitate better understanding and alignment among the broader business team regarding why preference share is being discounted and by how much.) 10.Ibid 11.Ibid 12.Ibid 13.Ibid 14.IMS Market Research Consulting, The 2009 Revision of the National Reimbursement Drug List (NRDL), (Online), Shanghai, Available at http://www.imshealth.com/ imshealth/Global/Content/Document/2009_Revision_ NRDL.pdf) (Accessed 5 August 2011) 4. All of the barriers described in this approach can be tracked after the product has been launched – making it easy for the market researcher to compare actual results against the projections, and adjust market share expectations if necessary. Of course, while this method offers advantages over standard conversion approaches, it is not perfect. In many cases there will be an inter-relationship between the various discounting barriers. For example, those physicians not being detailed and who are unaware of the brand may be employed at institutions where the product does not have distribution. In a perfect world, we would be able to estimate exactly how much overlap exists to ensure that we discount without double counting. However, given the data currently available to researchers, it is unlikely that a precise prediction of all inter-relationships among all the barriers can be known. Accordingly, the researcher may need to rely on ranges for each of the barriers. To conclude, incorporating the impact of market barriers is a significant step forward in discounting preference share. However, precise prediction will only be achieved through continued research into, and evolution of, our discounting approaches. Until then, the barrier approach, with its grounding in both market research and market reality, is an important addition to the pharmaceutical forecaster’s arsenal. 10 Copyright ©2012 Ipsos. All rights reserved. Contact To learn more about how Ipsos can help you with your Healthcare research, please visit www.ipsos.com Or contact: Kim Morneau +1 617 526 0050 [email protected] About Ipsos Healthcare Ipsos Healthcare is a global business division focusing on research in the pharmaceutical, bio-tech, and medical device markets. It is also the leading provider of global syndicated therapy monitor data. Operating in over 40 countries, the team of 600 pharmaceutical market research experts, marketers and client-side brand-builders focus on delivering outcome-oriented research for its clients. Drawing from a broad range of qualitative and quantitative techniques, Ipsos Healthcare offers custom and syndicated research programmes to evaluate motivations, experiences, interactions and influence of stakeholders forming the multi-customer markets which increasingly drive business success in the healthcare industry. Copyright ©2012 Ipsos. All rights reserved. 1 2 - 0 4 - 0 9