White Papers

Inpharmation is founded on using pharma-validated evidence-based methods

All Inpharmation’s pricing, forecasting, and demand market research solutions are based on years of development, calibration, and research.

Below you can browse a selection of interesting white papers. Many of these papers provide head-to-head validation and evidence as to why Inpharmation’s solutions are best placed to provide you with the most reliable insights.

To receive a full free copy of any white paper:

  1. Click on the white paper that most interests you.
  2. Submit a request via the download instructions.

How best to forecast the uptake of new pharmaceuticals: First-in-class products

Creating an uptake forecast for a first-in-class product is fraught with uncertainty. So far in this series of papers on uptake, we have covered how the Bass uptake model distils prescriber behavior into two coefficients: innovation (p) and imitation (q). For a me-too brand, it is simple to determine the values of these two coefficients […]

Read More

How best to forecast the uptake of new pharmaceuticals: Include the competition

Forecasting the uptake of competing products is complex and requires substantial Excel skills. The previously covered Bass model is great for forecasting individual product uptake, but what about when you need to forecast the uptake of your product amongst competition? It isn’t as simple as forecasting the uptake of each competitor individually – you need […]

Read More

How best to forecast the uptake of new pharmaceuticals: The Bass model

Creating a forecast for a new pharmaceutical is daunting but exciting. The forecast is likely to be driven by just a few key assumptions: the treatable patient population, the peak market shares of competing products, and how quickly products achieve these peak market shares — their uptake rate. Getting these assumptions “right” is paramount to […]

Read More

Are you handling the increased complexity of oncology forecasts?

Oncology forecasting has become significantly more complex for two main reasons: First, the knock-on effects of improving efficacy (which we call the tsunami effect). Second, the increasing control that regulators and payers are exercising over which lines of therapy products can be used in (which we call the “shadows and lights” effect). In this article, […]

Read More

Human judgment: Possibly the greatest black box in forecasting

Forecasters are often critical of “black box” forecasts. When a forecaster cannot trace the numbers from input to output using simple math, the forecast is deemed an untrustworthy “black box”. On the face of it, this criticism seems fair. However, the evidence shows that using well-validated forecasting algorithms—rather than relying on human judgment—produces more accurate […]

Read More

Caught in the analogue trap: The unseen dangers of forecasting mimicry

Using products similar to your own to forecast the performance of your product is popular and, on the face of it, intuitive. Unfortunately, analogues often produce terrible forecasts. This article explains the two analogue traps and explores how you can generate more useful forecasts.

Read More

Making conjoint fit for pharma

At Inpharmation, we developed the Pharma-Specific Conjoint platform specifically to address the challenges of reliability and sample size in pharma market research. We transform preference shares into more accurate market shares before finally incorporating the additional jigsaw pieces to generate pharma-validated, forecast-ready market shares. This series of papers will show you the key steps to […]

Read More

Are you accurately forecasting the uptake of first-in-class products?

The speed and uptake of first-in-class products is highly variable. However, it is hard to find analogous products to base your forecast on. This article explains how three drivers impact the shape of uptake curves.

Read More

Going beyond small-sample payer interviews: Reliable and quantitative value pricing research

Speaking to just a handful of payers risks your price strategy suffering from unreliable wide-ranging price responses. Price strategies rely on quantitative metrics, and quantitative metrics must be powered with reliable respondent sample sizes. However, payers can be difficult to recruit into research projects, especially outside of the US. This article investigates what constitutes a […]

Read More

What impact is artificial intelligence having on pharma pricing and forecasting?

Artificial intelligence is having a profound effect in pharma R&D. For example, it is making wonderfully accurate predictions of protein folding. Why is it not having a similarly revolutionary impact on demand and price forecasting? This article explains why.

Read More

When payers around the world behave as if they use QALYs

Many payers do not use QALYs when making pricing decisions. Nevertheless, they behave as if they do. This article explains why the best predictive models often do not reflect reality. We use, as an example, the usefulness of QALYs in predicting payer decisions.

Read More

Are ICER publications having an impact on drug price acceptability in the US?

The Institute for Clinical and Economic Review (ICER) is a US lobby group that looks at the clinical benefit of value-added drugs and recommends “cost-effective” prices. Their definition of cost-effectiveness is not a hard barrier, such as the one used by NICE in the UK, but is a range of thresholds, the highest of which […]

Read More

An objective look at patient allocation exercises and a more accurate alternative

Many forecasts are guided by so-called “patient allocation” exercises. Here, prescribers are asked to predict how many of their patients they would treat with each current product and (usually) one novel product—whose profile they are shown. This seems a sensible approach. However, you must distinguish between research questions (what you want to know) and survey […]

Read More

Multi-criteria decision making analysis for pricing pharmaceuticals

Multi-criteria decision analysis (MCDA) is occasionally used as a tool to predict the price opportunity of new pharmaceuticals. This white paper explains the advantages and disadvantages of the approach.

Read More

Making conjoint fit for pharma: Increase your forecast accuracy with other drivers of share

Conjoint studies frequently assume that product profile is the dominant driver of market share. However, product profile is often not the dominant driver and rarely is it the sole driver of market share. Other factors affect market share too, and you need to combine them with the output from your conjoint. In this final paper […]

Read More

Making conjoint fit for pharma: Transform your preference shares

So far in this series of papers on making conjoint fit for pharma, we have explored how you can increase the reliability of responses and decrease the required sample size of your pharma conjoint studies to produce preference shares. However, market research agencies often advise that the preference shares that come out of conjoint studies […]

Read More

Making conjoint fit for pharma: Shrink your sample size requirements

In the previous paper of this series “Making conjoint fit for pharma”, we discussed why traditional conjoint approaches overburden respondents in pharma market research. In this paper, we explore the impact of sample size on reliability, the challenges of recruiting enough respondents to drown out the noise, and how a fit-for-pharma conjoint platform solves these […]

Read More

Making conjoint fit for pharma: Have mercy on your respondents

Conjoint is a wonderful idea for demand market research. By asking a sample of respondents a series of questions, you get an “X-ray” into their minds. You learn which product attributes are important to them and which are not. You can quantify how different product performance levels contribute to preference and market share. Conjoint is […]

Read More

Scroll Top