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Patient/Epidemiology Based Forecasting for Pharmaceutical Industry
MarketsandMarket?s integrated patient-based forecasting solution for pharmaceutical industry mainly for pipeline products.
Our approach is combination of forecasts based on epidemiology and actual patients on products. The key primary and secondary research analytics underlying the forecast include:
- Epidemiology analysis
- Treated patients best fit trending
- Polypharmacy ratio analysis
- Competitive landscape analysis
- Analogue analysis for uptake,time to peak,and generic erosion
- Conjoint / discrete choice / attribute analysis
- Calibration based on actual sales and impact of various events
The treated patient pool potential is determined by prevalence, incidence, diagnosis, treatment, cure, and mortality rate. The treated patient pool is also back-calculated simultaneously from historic and present product sales. Trending and smoothening takes into account seasonality and the impact of other events on sale. The conjoint / discrete choice analysis uses primary research to estimate peak patient share for new products. In absence of primary research due to budget / time constraints, an attribute analysis is done using secondary research and consumer inputs to estimate peak patient share for new products.
The peak patient share calculation mechanism is calibrated based on shares of existing products. Ratio of treated patient pool, based on patient on products and epi, gives polypharmacy ratio. Analogues are identified based on disease area, TPP, MoA, order of entry, and market dynamics to estimate time to peak, shape of uptake curve, generic erosion, and impact of other events. Share exchange / cannibalization is done based on fair-share approach or inputs from brand team along with secondary and primary research.
Simple and transparent VB- and Excel-based forecast models: Models are developed based on forecast complexity and the functional need of end-user. Our team has expertise in designing and developing VB- and Excel-based forecast / market models.
Strong quality checks: - Quality checks are built up at each stage to ensure foolproof estimates, including triangulation of epi data from multiple sources, calibration based on polypharmacy and actual historic sale, calibration of peak patient share estimation model or peak patient share based on share. Quality checks embedded in the Excel / VB formula in the forecast model.
We have strong team of analysts with experience in executing advanced forecasting projects for both pipeline and in-market products for the top pharma companies globally.
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