How to choose market share techniques for a new product forecast
How to choose market share techniques for a new product forecast Vision in Business – Lisbon, Portugal By Rafaat A. Rahmani President [email protected] Thursday 15th November 2007 Copyright Lifescience Dynamics Ltd. 2007 Agenda About About us us Introductions Introductions Quick Quick ‘n’ ‘n’ Dirty Dirty for for early early phase phase Analogue Analogue based based Monté Monté Carlo Carlo Simulation Simulation External External expert expert opinion opinion Fixed Fixed product product profile profile Variable Variable product product profile profile Tips, Tips, sources, sources, wrap-up wrap-up Copyright Lifescience Dynamics Ltd. 2007 2 Agenda About About us us Introductions Introductions Quick Quick ‘n’ ‘n’ Dirty Dirty for for early early phase phase Analogue Analogue based based Monté Monté Carlo Carlo Simulation Simulation External External expert expert opinion opinion Fixed Fixed product product profile profile Variable Variable product product profile profile Tips, Tips, sources, sources, wrap-up wrap-up Copyright Lifescience Dynamics Ltd. 2007 3 We offer comprehensive and end-to-end services An overview of Lifescience Dynamics Market Research Competitive Intelligence Valuation & Modelling Strategic Consulting Emotional & rational Scientific & commercial Market simulation & scenario analysis Strategic advice, review & validation Qualitative & quantitative Policy markers & influencers Drug and disease area forecasting Business planning & implementation Solutions based on comprehensive data, exhaustive analytics, years of experience & multi-disciplinary team efforts 4 Copyright Lifescience Dynamics Ltd. 2007 Agenda About About us us Introductions Introductions Quick Quick ‘n’ ‘n’ Dirty Dirty for for early early phase phase Analogue Analogue based based Monté Monté Carlo Carlo Simulation Simulation External External expert expert opinion opinion Fixed Fixed product product profile profile Variable Variable product product profile profile Tips, Tips, sources, sources, wrap-up wrap-up Copyright Lifescience Dynamics Ltd. 2007 5 A quote to set the scene… “Forecasting is a complex modelling exercise...so why should we make it more complex! "I try to make things as simple as possible, but not simpler" Albert Einstein 6 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Predicting fails, but forecasting is an essential feature of any business Forecasting is more art than science, therefore, is inexact by definition. Forecasting is about predicting future events............so uncertainty can be reduced but can not be eliminated. Forecasting is a set of techniques for understanding markets at a fundamental level. 7 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Forecasting is a decision-making tool In order to grow, a company must consistently make good business decisions (decisions that increase the value of the company). Development of pharmaceutical products is expensive and risky, and it often involves long time horizons. In order to make quality investment decisions, it is useful to know as much as possible about the future value that can be expected from the investment. We create models in an attempt to: • • • • • • 8 Copyright Lifescience Dynamics Ltd. 2007 [email protected] predict market evolution predict the revenue reduce risk and increase accuracy go / no go decisions about new and existing projects? what indications should be targeted? how much can be spend on promotion? What drives sales? Start by asking simple questions • • • • • How many diagnosed patients are out there? How many of them are/will be treated? What are the trends in current therapy? What could happen in the future to impact therapy class? Where does our new product fit in? 1st line, 2nd line, 3rd line Type of patients: mild, moderate, severe • How much of it will the patient consume? Duration of treatment Dosage Compliance • What is the price? Some not-so-simple answers • What will be the uptake of this product 9 Copyright Lifescience Dynamics Ltd. 2007 [email protected] The challenges of forecasting Influence Advocacy Groups Opinion Leaders Purchasers Patients Trusted Colleagues Carers/Relatives Advertising Publications Demand Awareness Colleagues Sales reps Decision Evaluation s S to c k Point of use Generics Formularies bility a l i a v A Hospitals Supply 10 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Wholesalers Protocols of care Decision making Algorithm: Competition, Rx, Alternatives & Substitutes A robust model incorporating key drivers for market penetration • • • • • • Unmet needs Order of entry Competition - generic Competition - branded Protocols of diagnosis and treatment • • • • • • • Therapy area • • • • • • • 11 Copyright Lifescience Dynamics Ltd. 2007 [email protected] • Resistance to switch Proof/believability of offer for new molecule Budgetary pressures Reimbursement Formulary approval Healthcare reform / policy Advocacy Groups Patients’ requests DTC Market access Product profile Product perception Compliance Number of indications Number of markets – US/EU/JP (Brazil/China/India??) Price Product related Company related • • • • • Promotion Field force effectiveness Image of company within the therapy areas Strength of therapy franchise Types of long term forecasting Inputs Features 12 Implications Copyright Lifescience Dynamics Ltd. 2007 [email protected] Scenario Planning Advanced model – What model if analysis Super express model Express model Secondary research 9 9 9 9 Epidemiology/procedure-based 9 9/2 9 9 2 9/2 9 9 2 2 9/2 9 Rx-based 2 9 9 9 Analogue used 2 9 9 9 Competitive intelligence 2 2 9 9 Multiple products 2 9/2 9 9 Monté Carlo 2 9/2 9 9 Dynamic (VB based) 2 9/2 9 9 Multiple product profiles 2 2 9/2 9 Web based 2 2 2 9 Speed +++ + - --- Flexibility --- + ++ +++ Cost implications +++ + -- --- Primary research Juster Scale with PRF Primary research Conjoint with PRF Agenda About About us us Introductions Introductions Quick Quick ‘n’ ‘n’ Dirty Dirty for for early early phase phase Analogue Analogue based based Monté Monté Carlo Carlo Simulation Simulation External External expert expert opinion opinion Fixed Fixed product product profile profile Variable Variable product product profile profile Tips, Tips, sources, sources, wrap-up wrap-up Copyright Lifescience Dynamics Ltd. 2007 13 Market share estimation is most important aspect of forecasting Market share estimate cause most discussion and challenges Typically market shares & prices tends most sensitive to final revenues Generally market share estimates are based on guess work and therefore, the least robust number in a forecast Market share is function of many variables – some in management control but largely controlled be competitors and underlying marketing issues. There are some rule of thumb for market share estimations as listed below. • Order of entry • Promotion • Copyright Lifescience Dynamics Ltd. 2007 [email protected] The more you promote, the greater your market share but there is a limit Acceptance rates 14 First product gets the largest share in a new class of drugs As new competitor enters a new market, it is accepted more quickly than the previous one Challenges facing market share calculations Time delay / Response time Non-linear responses Feedback Residual / Carry on sales 15 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Cause & effect Model development is an iterative process Primary and secondary research analysis Experience & Intuition Define model structure (patient flow) Build model framework / Refine model framework All inputs are variable and can be changed: • When new data become available • To model different scenarios • To assess the impact of small changes 16 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Insert new data / alter variables Interpretations Review outputs Insert data Typical modelling steps 5 4 3 Run live data & Check against known disease areas, back of envelop Calculations and other brokers estimate, sales data Sanity check Input dummy data such as 1 sufferer per 1,000; Check the logic & test put some outliers Workout a mathematical relationship Epi pop.'s x % diagnosed x % drug-treated x patient share % (for drug) x drug price x days or cycles of therapy x compliance rate Understand the interaction and identify what 2 1 17 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Observe relationships & data need data would be required Understand the disease area, patient flow Define the problem patient segments, referral, treatment algorithms Refine the model and improve data based on initial results 5 4 Re-visit problem based on results? 3 Run live data & Check against known disease areas, back of envelop Calculations and other brokers estimate, sales data Sanity check Input some dummy data such as 1 sufferer per 1,000 Check the logic & test Put some out layers Workout a mathematical relationship Epi population's) x % Diagnosed x % Drug-treated x Patient share % (for drug) x Drug price x days or cycles of therapy x compliance rate Understand the interaction and identify what 2 1 Observe relationships & data need Data would be required Understand the disease area, patient flow Define the problem 18 Copyright Lifescience Dynamics Ltd. 2007 [email protected] patient segments, referral, treatment algorithms Refine data/ assumption / seek an alternative Patient based model should be built around patient flow Funding Seek Treatment GP / PCP / FP Diagnosis Tests & Diagnostics OTC Hospital Rx Filling ( 20 Care Pharmacy) Chronic Vs. Acute Rx Algorithm Dosing Compliance S/E Complications On going maintenance 1. 2. 3. 4. 19 Copyright Lifescience Dynamics Ltd. 2007 [email protected] It will go away Try home remedy OTC See GP or A&E (Hospital) Initial Treatment Prescription Rx Filling ( 10 Care Pharmacy) Cure Vs. Treatment Funding Not feeling well / symptoms lat ed re t ke ar M Rx data 20 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Price ts en ev CI & Analysis re tu Fu Market Opportunity + Treated Patients Reconciliation Reconciliation & & Trend Trend analysis analysis with with curve curve fitting fitting re lat ed Final Forecast Pr od uc t Patients/Epi Patient consumption Schematic overview of modelling process and data flow Robust approach marries epidemiology, Rx, primary data For example: Population Growth Rate Prevalence, Incidence, Diagnosed Rates Target Indication & Epidemiology Product profile Vs Competition Æ Peak Shares, Cannibalisation Factors by segment Treatment Parameters DOT / Rx Rx Market Share projected for each product class $/Day for Cox2i $/Day for each $/Rx for NSAIDs product class & Analgesics 21 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Historical (to date) Rx Data by Drug Rx 000’s IMS Rx Data Conjoint & primary research data Forecast Modelling Peak year share Years to peak Shape of the curve Curve Fitting, Uptake Curves, Bass Diffusion Forecast Rx Pricing Market Shares converted back to Rx 000 $ Rx Converted to Gross & Net Revenue Share modelling and curve fitting Availability of data and access to computers has meant that econometric methods are readily available to model and forecast market share. • However, controversy exists over their usefulness Market researchers have historically had a number of tools like focus groups and surveys to gather intelligence for new product acceptance. Most companies have few systems in place allowing managers to incorporate marketing assumptions, industry knowledge, market research, and prior product performance in a quantitative long run forecasting framework. One solution is the development of a process based upon diffusion theory and the product life cycle. 22 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Projection of World-Wide PC Demand, 1999-2010-Data From Bill Gates, Newsweek Actual Worldwide PC Shipments, 1981-1999 and Fitted and Projected Shipments, 1981-2010, m=3.384 Billion, p= .001, q= .195 Peak 2008 Shipments Includes Replacements (Upgrades) 180 160 697 Million Units Shipments through 1999 M illions of Units 140 120 100 80 60 40 20 Year World Wide PC Shipments 23 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Fitted World Wide PC Shipments 09 20 07 20 05 20 03 20 01 20 99 19 97 19 95 19 93 19 91 19 89 19 87 19 85 19 83 19 19 81 0 There are a number of curve fitting algorithms To perform curve fitting • Define a function • • • Copyright Lifescience Dynamics Ltd. 2007 [email protected] The function is then minimised to the smallest possible value with respect to the parameters. The parameter values that minimise the function are the best-fitting parameters. In some cases, the parameters are the coefficients of the terms of the model Types of curves • • • • • 24 Which depends on the parameters That measures the closeness between the data and the model Diffusion Curves Gompertz Curve - A growth curve Linear / multiple-linear regression Exponential curves such as Weibull Gaussian (Normal) Distribution Choose models that will be easy to administer If there are too many variables, or could not define a function then choose a model-free fit • Neural networks • Cubic splines Choose model based on the following criteria • • • • • 25 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Predictive value Ease of implementation Efficiency Ease of explanation Defensibility New market models (Bass-type Models) Copyright Lifescience Dynamics Ltd. 2007 [email protected] Innovators: those ‘very likely’ to take up a concept immediately - measures used vary, but elite ‘top box’ scorers Imitators: non-rejectors, who will eventually acquire Required input: Year 1 penetration (y=innovators, from ad hoc research) Final penetration (x=innovators+imitators - from ad hoc) Coefficient (z1) for innovators (0.005 = half percent) Coefficient (z2) for imitators (0.4 = typical value) Equation (actual numbers): Year 1 = innovators Year 2 = (y*z1) + ([z2-z1]*previous year total) ([z2/y] * previous year total2) The Diffusion Curve The Bass Diffusion Model describes the diffusion of new technologies into consumer markets and is being used extensively in Pharma as well. In the Bass Model, there are two characteristic types of consumer: • • Innovators - make purchasing decisions based on own evaluation of the pros and cons of the new technology. Described as being “Internally Influenced.” Imitators - make purchasing decisions based on the example of others, and who only adopt a new product when their contemporaries have deemed it valuable. Described as being “Externally Influenced.” 100% Each type of consumer has a characteristic uptake curve. The overall adoption curve (diffusion) is a blend of the two curves, based on the relative preponderance of each consumer type. 90% 70% 60% 50% Copyright Lifescience Dynamics Ltd. 2007 [email protected] Launch Peak Share 40% Years to peak 30% 20% 10% 0% 2005 27 Type of market 80% 2010 2015 2020 CI helps in improving input for modelling future events A event as anything which can affect the course of a market. Events include: • • • • • • • • • • • • • • • 28 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Changes in labelling for existing products Changes in price Changes in promotion Diagnostics Loss of exclusivity New class of drug New clinical study publications New dosage – E.g. OD to once a week New indications New presentation New product launches New types of drug - combination Off-label use Restricted use Vaccine Events are described by an index date and a set of parameters determining the time evolution of the system. The mathematical function that models the process is referred to as a Diffusion Curve. Key inputs required Diffusion theory uses a number of equations that produce the S-shaped curve resembling a product life-cycle • • • Introduction Phase Growth Phase Maturity Phase One popular formula is called a Logistic Curve. • • The curve is nonlinear. Its shape is based upon three pieces of information. Saturation level > Peak share at 100% awareness level Location of the inflection point (that point in time where the growth rate is maximised) Intensity of the introduction Phase. > Unmet needs – Described as a delay factor, this intensity usually has a numerical value ranging from zero to one. A factor close to zero implies a significant amount of pre-selling - i.e. pent up demand. – A factor close to one means that sales might be delayed because of distributional considerations, a tight advertising budget, or delays in training the sales force. 29 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Typical sales curves EU 5 - Market Share & Gross Revenue (Revenue on Right Hand Scale) $150,000 100% 90% 80% 70% $100,000 $ 000 % Share 60% 50% 40% $50,000 30% 20% 10% 0% $0 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 30 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Order of entry Order of entry effect is proportional to first two entrants (current market) • e.g., 4th entrant = 14 / (46 + 23) = 14/69 = 0.203 i.e., calibrated share estimate multiplied by 0.203 Generic order of entry model Nos of competitors Share 2nd 3rd 4th 5th 6th 31 Copyright Lifescience Dynamics Ltd. 2007 [email protected] 1 2 3 4 5 6 100% 67% 53% 46% 41% 37% 33% 27% 23% 20% 18% 20% 17% 15% 14% 14% 13% 12% 11% 10% 9% Agenda About About us us Introductions Introductions Quick Quick ‘n’ ‘n’ Dirty Dirty for for early early phase phase Analogue Analogue based based Monté Monté Carlo Carlo Simulation Simulation External External expert expert opinion opinion Fixed Fixed product product profile profile Variable Variable product product profile profile Tips, Tips, sources, sources, wrap-up wrap-up Copyright Lifescience Dynamics Ltd. 2007 32 Modelling uptake based on an analogue Identification of key drivers and then ranking and rating them • • Hospital vs. community product Presentation • • • • • • Oral, injection, IM and Sub-cut Dosage Unmet needs Chronic aymptomatic Reimbursement (currently well reimbursed) Co-pay differential Disease severity? Model on similar products e.g. in CNS • Usually difficult to find a good analogue ? Anti-psychotic drugs – Risperdal & Zyprexa ? Anti-Parkinson's drugs – Requip & Celance 33 Copyright Lifescience Dynamics Ltd. 2007 [email protected] IMS commercial Analogue database Agenda About About us us Introductions Introductions Quick Quick ‘n’ ‘n’ Dirty Dirty for for early early phase phase Analogue Analogue based based Monté Monté Carlo Carlo Simulation Simulation External External expert expert opinion opinion Fixed Fixed product product profile profile Variable Variable product product profile profile Tips, Tips, sources, sources, wrap-up wrap-up Copyright Lifescience Dynamics Ltd. 2007 34 Key features of MC simulations Monté Carlo simulation is a form of simulation • • • Monté Carlo Simulation Monte Carlo simulation helps make better decisions • • • Copyright Lifescience Dynamics Ltd. 2007 [email protected] models interdependencies sensitivity analysis simulations without simulation, a spreadsheet model will only output a single number Suppliers • • 35 randomly generates values for uncertain variables over and over to simulate a model without simulation, a spreadsheet model will only reveal a single (most likely) outcome automatically analyses the effect of varying inputs on outputs of the modeled system. @Risk, www.palisade.com Crystal Ball, www.crystalball.com MC works on the uncertain variables in the model Each uncertain variable is defined with a probability distribution. • Peak share in worst scenario • Peak share in most optimistic scenario • Peak share in most likely scenario Distribution of uncertainty is an equation that describes shape and range 36 Copyright Lifescience Dynamics Ltd. 2007 [email protected] The Monté Carlo Simulation For each input the model user defines a distribution (in terms of minimum, most likely and maximum values) The Monté Carlo simulation can be run 5000 times from which a mean result can be calculated with 90% confidence intervals. Patients Peak Share Result Launch date & Others... 37 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Monté -Carlo Simulation Forecasting Model Total GW406 sales until 2020 £ 000s 1.200 1.000 0.800 Probability Price Normal Distribution 0.600 Total Global GW406 Sales until 2020 0.400 0.200 0.000 16,035,000 17,435,000 18,835,000 20,235,000 21,635,000 Revenue Contribution The model provides a user friendly tool for forecasting the sales of key new products INPUTS Total sales per product are derived from: • a) patient epidemiology and Rx to calculate total drug treated patients • b) market environment and product characteristics to calculate total prescriptions per product • c) price per day to calculate the total sales per drug The market share calculations are based on Discrete Choice peak shares derived from primary market research and benchmarks are used to model key competitors 38 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Excel style database, and viewed and edited using an Excel front end Allows the user the ability to create a series of different scenarios in different databases Key outputs (per product) from the models include Rx, Rx share, revenue per product and revenue share. Offers a transparent approach for storing data and assumptions used to generate the forecasts The outputs can be reported to any level of segmentation included in the model. OUTPUTS The flexibility of the model allows assessment of numerous scenarios Ability to run multiple scenarios and to save scenarios powered by Crystal Ball Monte Carlo Simulation • • • 39 Copyright Lifescience Dynamics Ltd. 2007 [email protected] prioritisation of key variables generation of Tornado Diagrams ability to view sales report for any year based on cumulative probability All the key drivers are assigned a range of variability to account for error margins: the lowest, the highest and the mid-point from the primary market research. These three elements will be used to simulate market scenarios using ‘Monte Carlo Simulation’. Key outputs from ‘Simulation’ will be sensitivity analysis and stretched ‘S’ curve of cumulative total sales Capability to analyse not only the historical market for each therapeutic area, but also to predict the evolution of the market with accuracy by incorporating a multitude of future market events Agenda About About us us Introductions Introductions Quick Quick ‘n’ ‘n’ Dirty Dirty for for early early phase phase Analogue Analogue based based Monté Monté Carlo Carlo Simulation Simulation External External expert expert opinion opinion Fixed Fixed product product profile profile Variable Variable product product profile profile Tips, Tips, sources, sources, wrap-up wrap-up Copyright Lifescience Dynamics Ltd. 2007 40 The hypothesis underpinning primary research for uptake * Standard errors Studying a small sample Amplifying and applying it on the universe * * 0 50 100 Sample size 41 Copyright Lifescience Dynamics Ltd. 2007 [email protected] 150 200 Overview of a typical forecasting project with external expert opinions Market Survey: Utility Model: Market Model: Market Model: Front End Data, Peak share Presentation: of Insight Putative profiles Drivers & Diagnostics Competitors, gains & losses 42 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Data Longitudinal share visualisation of & Putative profiles & key competitors Simulation Tool Choice of method(s) depends on product and therapy area considerations Of which these are just a few… New product? No Adaptive conjoint • Early stage products Yes No Bass diffusion based modelling Fixed product profile? Yes 43 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Statistical modelling Choice task • Established therapy areas • Later stage products Juster Scale • Cost effective Process from primary data to market model Process flow Discrete Discrete choice choice or or ACA ACA output output Utilities Utilities Preference Preference shares shares from from Discrete Discrete choice choice or or ACA ACA model model Peak Peak market market share share Market Market model model 44 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Process overview Primary Market Research to capture choices doctors will be making based on product profiles via patient record forms` Yes / No choices made by the doctors will be converted into utilities` Utilities converted to produce preference shares for key indications Brand preference is the sum of the utilities (the part-worth) for each component attribute level Consumers have the greatest preference for products with the highest utility and should choose the product with the highest utility most often Preference shares by indication converted into patient share Market share determined for real and/or hypothetical products among the respondents surveyed Baseline market configuration established Hypothetical product offering profiled Rx are reconciled by re-weighting via IMS data to reflect off-label use in each market Modelled with future events using Bass Diffusion model Time to peak via IMS Analogue Agenda About About us us Introductions Introductions Quick Quick ‘n’ ‘n’ Dirty Dirty for for early early phase phase Analogue Analogue based based Monté Monté Carlo Carlo Simulation Simulation External External expert expert opinion opinion Fixed Fixed product product profile profile Variable Variable product product profile profile Tips, Tips, sources, sources, wrap-up wrap-up Copyright Lifescience Dynamics Ltd. 2007 45 Establishing peak market uptake via primary market research We often use a patient-based approach, where physicians are asked to complete patient record forms, detailing the characteristics and therapy of the last 5 patients they have seen, and then predict their likelihood of using the new product in these specific patients • Increases realism of responses • Avoids discussion of the ‘average patient’ • Provides positioning in patient segments • Highlights where the new product is perceived to fit in the therapeutic armamentarium A score of 8 + on the Juster scale has been shown to be a reliable indicator of future behaviour 46 Copyright Lifescience Dynamics Ltd. 2007 [email protected] The Juster scale has been used to predict purchase rates for a range of items, in different product classes and in all cases, has proved to be a better predictor than purchase intention scales* The Juster Probability Scale 10 9 8 7 6 5 4 3 2 1 0 Certain, practically certain Almost sure Very probable Probable Good possibility Fairly good possibility Fair possibility Some possibility Slight possibility Very slight possibility No chance, almost no chance * Marketing Bulletin, 1994, 5, 47-52 Agenda About About us us Introductions Introductions Quick Quick ‘n’ ‘n’ Dirty Dirty for for early early phase phase Analogue Analogue based based Monté Monté Carlo Carlo Simulation Simulation External External expert expert opinion opinion Fixed Fixed product product profile profile Variable Variable product product profile profile Tips, Tips, sources, sources, wrap-up wrap-up Copyright Lifescience Dynamics Ltd. 2007 47 Conjoint approach is used to if the product profile is flexible • Conjoint analysis and Multidimension scaling (MDS) were developed for measuring ‘human’ perceptions and preferences • • Well suited for measuring the human psychological judgements Decomposition of a set of overall responses to factorially designed stimuli • • Copyright Lifescience Dynamics Ltd. 2007 [email protected] Breaking down a decision of a purchase into many attributes and each attribute with different level of performance Conjoint analysis attempts to explain consumers’ overall choice/evaluation of a marketing stimulus, in terms of the value of its constituent attributes The Conjoint models are used to simulates consumer’s future buying decision based on their current behaviour 48 Developed by psychologists in the 60’s and refined and adapted by marketers ever since Widely used and validated techniques Multivariate E.g. physician's Rx decision The hypothesis underpinning Conjoint Analysis Symptoms History Cough Wheezing Bronchospasm Breathless on exertion Tight chest Tests •X-Ray •Peak flow sis gno thma Dia As ate der Mo Choices of drugs A,B,C,D,E reading etc. Inhaled steroid Combination Short or Long Acting Leukotriene Antagonists [email protected] • • Replicating choices made by individual doctors to the universe Choices made by today’s doctors will not be that different in the future, given the underlying market assumptions remain the same Rationale: Beta Agonist Choices Copyright Lifescience Dynamics Ltd. 2007 The theory I should Rx Drug A Inhaled Corticosteroids 49 Is effective against the Cough & Wheezing Good safety profile Competitively priced Obtaining importance weightings: Conjoint Models There are two original types of true conjoint: • Pair-wise • Full Concept and a series of hybrids... Computer-based: • ACA • CBC Paper-based: • Scalar Conjoint • CVA (Value analysis) 50 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Agenda About About us us Introductions Introductions Quick Quick ‘n’ ‘n’ Dirty Dirty for for early early phase phase Analogue Analogue based based Monté Monté Carlo Carlo Simulation Simulation External External expert expert opinion opinion Fixed Fixed product product profile profile Variable Variable product product profile profile Tips, Tips, sources, sources, wrap-up wrap-up Copyright Lifescience Dynamics Ltd. 2007 51 Historical Data Two well known & standard sources - IMS and Scott-Levin. Types of Data that they provide : • • • Volume (IMS / Scott-Levin) IMS’s NSP, NPA (US) IMS’s MIDAS (Global) Total Sales (DOL TOT) – Currency sales – Grams, kilo or IU, SU • Allocation (IMS-NDTI, Varispan, Scott-Levin - VONA) • Copyright Lifescience Dynamics Ltd. 2007 [email protected] By year, Quarter, Month, or Week (For long term forecasts – Yearly is preferred) By Country (U.S. data is best, other countries have variable quality) Alternative include: 52 Uses by ICD-10 Code Concomitant Uses (i.e. using with other medications) Availability • Total Scripts (TRx) - Scripts written Average Daily Use (ADU / DaCON) SEC Filings News archives Historical Data (cont.) End goal is converge historical sales data and the relevant target patient populations This entails converting “Sales Volume Data” into patient #s • 1st Step – To agree on Patient-Day of Therapy (DOT) = # of tablets/ml / patient / day • DOT = EUTRx / DaCon (or ADU) or = TRx x Average Days Per Rx. • Assumption on a Compliance* Level Compliance varies from <50% for less critical drugs to >90% for life saving medicines say insulin • Patient-Year Equivalents = DOT / (DOT/Yr x Compliance). Compliance Compliance (or Adherence) refers to a patient both agreeing to and then undergoing some part of their treatment program as advised by their doctor. Most commonly it is whether a patient takes their medication e.g., if a 30-day script is completed by a patient in 60 days, his / her compliance is 50%. 53 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Epidemiology data sources Syndicated reports • • General Sources • • • Copyright Lifescience Dynamics Ltd. 2007 [email protected] Therapy monitors Syndicated Patient Diaries Current Treatments – Ad-hoc • • 54 USRDS, OMIM Current Treatments - Syndicated • • PubMed Lancet, NEJM Medscape Disease specific sites: • Decision Resources Datamonitor Primary market research Custom Patient Diaries CDC - Center for Disease control - USA Italian Statistics Institute Krebsregister Saarland Germany Ministry of Health, Labour and Welfare - UK National Cancer Institute National Statistics http://www.statistics.gov.uk/ Robert Koch Institut UNAIDS - United Nations Programme of HIV/AIDS World Health Organisation Forecasting data sources Level of unmet needs, concomitant diagnoses, their trends, concomitant treatment or procedures • • Future competition • • 55 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Primary market research Custom Patient Diaries Premier healthcare informatics Primary market research IDdB, PharmaProject, R&D Focus Ad-hoc competitive intelligence Marketed treatment regimens • • • • • • Hospitalisation • • Physicians Desk Reference (US) British National Formulary (UK) Rote Liste (DE) Hipocrates’ Vademecum (ES) Vidal (FR) L’Informatore Farmaceutico (IT) Price • • • • • • Redbook (US) MIMS (UK) Rote Liste (DE) Hipocrates’ Vademecum (ES) Vidal (FR) L’Informatore Farmaceutico (IT) Forecasting do’s and don’ts Challenge key assumptions • • • • Price Duration of therapy Compliance Uptake / penetration rates Copyright Lifescience Dynamics Ltd. 2007 [email protected] Historical / past trends Uptake curve Years to peak sales Current & future competition Generics Specific epidemiology Order of launch Price Specific drug use Therapeutic area • • • • Risks (greater in early phases) General spreadsheet related errors • • • • Acute Chronic Cycles of therapies for IVs, Subcut Diagnostic / Device Stage of Development • Country • • • • 56 Data entry errors i.e. typo etc. Logic Errors in linking of cells etc. Calculations / maths etc. Sanity check the data & outputs • • • Back of envelope calculations Broker's reports Similar past / current products Market share techniques based on secondary data Techniques Order of entry models Analogues Internal PM opinions Monte Carlo Combination of internal expert Opinion with Monte Carlo 57 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Benefits & Challenges 9 Quick, easy to use and cost effective > Very good for BD&L opportunities 8 Drugs are not same in efficacy and safety (even if small) 8 Based on past, uses past as the future predictor and ignore market events 9 Quick, easy to use and cost effective > Very good for BD&L opportunities 8 Very difficult to find a good match 8 Based on past, uses past as the future predictor and ignore market events 9 Quick, easy to use and cost effective > Very good for BD&L opportunities 8 Biased and very difficult to defend 8 Based on past, uses past as the future predictor and ignore market events 9 Quick, easy to use and cost effective > Very good for BD&L opportunities 8 Lower and higher limits are based on biased view 8 Very difficult to explain the underlying maths and algorithms 9 Increase robustness and cost effective > Very good for BD&L opportunities 9 Lower and higher limits are based on expert view and bias taken out 8 Very difficult to explain the underlying maths and algorithms Market share techniques based on direct estimation Techniques Benefits & Challenges Quantitative direct estimation 9 1 to 5 years before market launch 9 Paradigm shifts can be studied and anticipated 8 Expensive and time consuming and quality depends on market researchers 9 Build consensus, obtain group thought 8 Dominated by one or two doctors and rest gravitate to their numbers 8 Relative small sample size 9 Helps to understand where the new product will fit Rx Algorithms 8 Inability to recall & review a range of patients, accurately summarising 8 Responses tend to cluster around certain numbers (10%, 20%, and 25%) 9 Robust numbers and defendable 8 Inability to recall & review a range of patients, accurately summarising 8 Responses tend to cluster around certain numbers (10%, 20%, and 25%) Self-completion forms 9 Robust numbers and defendable 8 Inability to recall & review a range of patients, accurately summarising 8 Responses tend to cluster around certain numbers (10%, 20%, and 25%) Expert Interview/KOL Focus Groups Qualitative doctor interviews Errors arising from physicians inability to rapidly recall and review a range of patients and then accurately summarising the results 58 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Market share techniques based on in-direct estimation Techniques Conjoint Juster Scale Conjoint with patient diary Juster Scale with patient diary Delphi techniques 59 Copyright Lifescience Dynamics Ltd. 2007 [email protected] Benefits & Challenges 9 Defendable, well established 9 Incorporates product features and claims 8 Expensive and time consuming and quality depends on market researchers 9Defendable, well established 9 Incorporates product features and claims 8 Less time consuming and quality depends on market researchers 9 Defendable, well established 9 Most closest to real prescribing decisions 8 Responses tend to be higher share than the reality and P&R element missing 9 Defendable, well established 9Most closest to real prescribing decisions 9 Responses tend to be higher share than the reality and P&R element missing 9 Easy and cost effective 8 Inability to recall & review a range of patients, accurately summarising Lifescience Dynamics (Worldwide Headquarters) The Oriel – Thames Valley Court 185 Bath Road Slough Berkshire SL1 4AA England, UK O: +44 (0) 1753 205 126 F: +44 (0) 1753 205 127 Lifescience Dynamics (USA) 304 Park Avenue South, 11th Floor New York, NY 10010 USA O: +1 (212) 926-9290 F: + 1 (347) 523-9639 [email protected] www.lifesciencedynamics.com Thank you Questions? 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