Risk Analysis Takes a Central Role

Introduction
The pharmaceutical industry is characterized by high risks and uncertainty throughout the business system. One estimate is that pharmaceuticals can be as much as 50% riskier than the Standard & Poor’s (S&P) 500. Also, research by Tufts University² suggests that only one-third of new drugs recoup their R&D investment, a sobering fact given high attrition rates, long development times and a cost of US$0.9 – $1.7 billion to bring a new drug to market³. In addition, the crisis in financial markets which is forcing governments to increase debt will strain industry economics further. The stakes for pharmaceutical companies to proactively understand and manage risk and deliver innovative and cost effective products are therefore increasing, and this also applies to companies developing medical devices and diagnostics.

The goal of this white paper is to illustrate how probabilistic risk analysis can provide a powerful tool to better understand and manage risks to which the pharmaceutical and medical device industry is exposed. We will first provide a brief overview of quantitative risk analysis, followed by a description of how probabilistic risk analysis can help unravel the complex interactions between risk drivers and provide insight to support better decision making. We will also offer a number of example areas where risk analysis may be particularly useful and finish the paper with conclusions and recommendations for next steps.

What is Quantitative Risk Analysis and how can it help you?
In order to understand quantitative risk analysis, it is useful to contrast it with more traditional ‘deterministic’ analysis. A deterministic analysis is basically a process where the analyst makes a single-point estimate of all the inputs into his or her analysis, which is a de facto ‘base case’. For example, when analyzing the NPV⁴ of a potential new product, the analyst would estimate a single-point fixed-value assumption about the prevalence of the disease, the market share, price, competitive climate etc. Of course there is often great uncertainty about such parameters and thus the analyst may run several scenario analysis. The goal of such scenario analysis would be to get an understanding of the ‘worse-case’ as well as ‘best-case’. (In a further step, the ‘base case’ may be supplemented with a tornado diagram to show sensitivity to key assumptions). There are, however, a number of problems with deterministic analysis. Chief among them are that deterministic analysis provides very limited⁵ understanding of the financial risks of an investment, new development project or a licensing deal, and does not provide any insights into the probabilities of achieving a certain IRR or other financial threshold or a potentially catastrophic outcome. In addition, it does not help uncover the main risk drivers that determine the risk of investment.

Quantitative risk analysis (QRA) however allows the analyst to specify and take into account how much uncertainty there may be around each and every one of the inputs in the analysis. A great advantage of QRA is therefore that we are not ignoring the risks and uncertainties, but are taking them into account. Because we are allowing for risks and uncertainties in the inputs, the outputs of the analysis better represent the risk and uncertainty of the project. For example, when performing a QRA, we are now able to determine the financial risks associated with a project and understand for example the probability of an IRR higher than the corporate cost of capital. In addition, QRA allows us to understand what risk factors cause the most risk and thus should get the most attention in any risk mitigation efforts.

So, how does QRA actually work? While a detailed description of QRA is beyond the scope of this white paper, the most common technique used to perform quantitative risk analysis is called ‘Monte Carlo Simulation’. This technique allows us to essentially run thousands of scenarios that provide us with an understanding of the range of possible outcomes of our analysis. A Monte Carlo model can therefore be seen as a model that takes into account likelihoods or chances that some risks and uncertainties may or may not happen. The output from a quantitative risk analysis model can answer many questions such as: “What is the probability that the IRR will exceed our corporate cost of capital?.”

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