Healthcare Prediction Modeling

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Methods of Prediction: Main Concepts

Predictions in clinical research are fundamental techniques that can benefit patients’ outcomes and medical practices. Prediction research is the process of predicting future outcomes, based on certain patterns, markers, and variables (Waljee et al., 2014). Accurate prediction models can indicate the future course of a treatment or the risk of developing an illness. Note that in contrast to the popular explanatory research, predictive research does not tackle the problem of causality or preconceived theoretical concepts. Forecast models simply use statistical methods and data mining to predict future clinical outcomes. To be more precise, predictions rely on numerous scientific methods which are part of statistical inference. Usually, statistical inference techniques are used to draw conclusions from data sets and include procedures, such as regression analysis, linear regression, and vector autoregression models.

Predictions can shape not only healthcare practices but societies in general. From weather forecasts to stock market performance, predicting the future is an essential factor for success. Yet, in clinical settings, methods of prediction are paramount as they can literally save lives. By implementing accurate methods of prediction and forecast, scientists can explore the predictive properties of certain biomarkers and patients’ characteristics, which can allow them to predict numerous clinical outcomes (e.g., rehospitalization, predictability of patient beds, risk of developing a disease, etc.). Therefore, it’s not surprising that predicting health outcomes is vital to patients and families, as well as professionals and governments.

2. Types of Methods of Prediction and Validation

Methods of prediction vary between research fields, and there are numerous multivariable prediction models which tackle vital aspects, such as model development, validation, and impact assessment. Note that the most traditional predictive approach is the Bayesian approach. However, with the increasing influence of machine learning and artificial intelligence algorithms, experts can implement other sophisticated models and software programs, such as the powerful random-forest approaches (Waljee et al., 2014). Machine learning algorithms are beneficial in the identification and analysis of potential, unexpected, and marginal predictors.

In addition, it’s interesting to mention that an analysis conducted by Bouwmeester and colleagues (2012) identified and classified five types of medical studies with different models of prediction:

  • Predictor finding studies: These studies explore in detail which predictors independently contribute to the actual prediction (e.g., medical diagnosis).
  • Model development studies without external validation: This approach is based on the development of prediction models (e.g., predictive techniques to guide patient management) by assessing the different weights per predictor.
  • Model development studies with external validation: Similar to the model development studies above, these studies are based on models tested across external datasets, with a focus on external validation (e.g., temporal validation).
  • External validation studies with or without model updating or adjustment: These studies focus on assessing and adjusting previous models of prediction based on new participant data as well as new validation data.
  • Model impact studies: These studies explore the actual effect of the models of prediction on health outcomes and healthcare practices.

Note that the research team concluded that prediction models must involve external validation and impact assessments in order to be successful.

3. Methods of Prediction: Developing, Validating and Assessing Models

Although validation is a vital part of any prediction model, there are several steps that predictive research follows. The first step is the development of a predictive model. Note that the selection of irrelevant predictive variables can lead to poor performance. Missing data is another crucial aspect which experts must consider. Validation, as explained above, is one of the most important factors for success. The model performance should undergo internal validation (e.g., splitting the dataset) and external validation (e.g., new patients’ data). Apart from internal and internal validation, there are several types of validation practices: temporal (including new subjects from the same institute), geographical (focusing on another institute, city, etc.), and transmural (exploring different levels of care, e.g., primary care) (Janssen et al., 2009). Sadly, when validation shows poor performance, researchers usually develop a new predictive model instead of adjusting for errors. By not updating old models of prediction, prior knowledge is left behind, and predictive research often becomes particularistic.

The final step of predictive research is assessment: researchers must assess the performance of their predictive model. This goal can be achieved through numerous additional tests, such as calibration, discrimination or reclassification (Waljee et al., 2014).

II. Predictions in Observational Studies and Randomized Controlled Trials

  1. Observational Studies and Randomized Controlled Trials

The abundance of research methods and study designs can help experts explore a wild range of settings, populations, and phenomena. Nevertheless, observational studies and randomized controlled trials are the most popular and effective types of studies which help professionals evaluate treatment outcomes. Note that in observational studies, outcomes are observed just after a certain intervention. In randomized controlled trials, on the other hand, randomization is used to reduce bias and measurement errors (Braun et al., 2014).

Consequently, researchers claim that randomized trials are more effective than observations because randomization procedures can eliminate bias and nuisance (Trotta, 2012). At the same time, some research topics cannot be tested via a randomized trial as such studies would be unethical. Imagine randomizing non-smokers to smoking groups! It’s no surprise that the quality of the study design is the most important factor for research success.

  1. Survival Analysis and Censoring

Since medical research and epidemiological studies involve the measurement of the occurrence of an outcome, prediction models become paramount. In particular, survival analysis or lifetime data analysis focuses on the measurement of time to event (e.g., outcome). Time to event can be fatal (e.g., death), time to a clinical endpoint (e.g., disease), positive (e.g., discharge from hospital), and neutral (e.g., cessation of breastfeeding) (Prinja et al.,  2010). Note that time to event can be measured in days, months, years, etc.

Nevertheless, in survival data, the end of the follow-up period won’t occur for all patients (Altman & Bland, 1998). In fact, this is a phenomenon, known as censoring, which researchers must consider. Censoring occurs when information on time to outcome event is not available (e.g., due to loss to follow-up or an accident). We should mention that there are three types of censoring: right, left, and interval. Let’s have a look at a study about breastfeeding mothers (Ishaya & Dikko, 2013). In case participants are still breastfeeding after their last survey, this is known as the right censoring. Left censoring occurs when mothers enter the study after they’ve stopped breastfeeding. Interval censoring, on the other hand, is when mothers stop breastfeeding in between two successive check-ups.

2.1. Prediction Models and Measurement Error in Time to Event Data

Although clinical research is based on precise data and regulated research, there are many unknowns and datasets which are prone to measurement error. As stated above, predicting survival outcomes can be a challenging task prone to problems, such as censoring. To tackle such challenges, measurement error must be considered in any prediction model. For instance, Meier and colleagues developed a model for measurement error by repeated testing. They based their model on validation data and adjusted proportional hazards methods, with event data being a primary outcome. On the other hand, Braun and colleagues (2014) analyzed various scenarios for time to event data measured at a one-time point, with event data used as a covariate. Note that their adjusted prediction model was based on error-free time to event data, while the actual implementation of the model employed error-prone time to event data. To be more precise, Braun and colleagues found out that in Mendelian risk prediction models, self-reported family history is not always accurate. Therefore, the research team concluded that both sensitivity and specificity should be assessed to avoid distortions in predictions.

Note that Mendelian risk prediction models include meta-analyses; in fact, there are various powerful software packages (e.g., BayesMendel R package) which can be utilized to calculate predictions. In particular, the BayesMendel R package employs the R programming language. The package allows researchers to integrate the Mendelian models, evaluate the probability that an individual carries certain genes, and predict the risk of a disease based on a patient’s family history (Chen et al., 2004). In addition, the BayesMendel R package covers several models: BRCAPRO about breast and ovarian cancer, MMRPro about the risk of Lynch Syndrome, and PancPRO about assessing individuals at risk of pancreatic cancer.

2.2. Multivariate Survival Prediction Models and Mendelian Risk Prediction Models

The measurement error in time to event data applies to various multivariate survival prediction models and different types of predictions (e.g., error-free data, information prone to error, and adjustments). Simulation scenarios, such as the sophisticated Monte Carlo technique, can be utilized in research, including simulations for both values of sensitivity and specificity. Note that a low sensitivity indicates an underreporting of events and a low specificity to correspondents to an over-reporting of events.

It’s interesting to mention that Braun and colleagues (2014) integrated their measurement error adjustment method into a Mendelian risk prediction model not only regarding survival outcomes but time to event data all at the same time. What’s more, the research team revealed that adjusted data might be poorly calibrated in the low-risk deciles, which may affect practical outcomes, such as insurance policies and clinical decisions. It’s not a secret that insurance coverage is a complex problem – some health conditions might take a long time to be spotted or might be clouded by nuisance (Sommers et al., 2017). On top of that, insurance in clinical research is not fully explored by ethical committees.

3. Observations and Propensity Scores

            Despite the popularity of randomized clinical trials in medical research, observational studies are among the most powerful research methods employed in medical practice. In clinical settings, there are retrospective and prospective observations. Retrospective studies involve the analysis of past records to collect relevant prognostics. In prospective studies, on the other hand, information is collected during treatment and follow-up. While prospective studies are time-consuming and costly, they help experts deal with bias and missing data. (Smith, 1990).

Interestingly, propensity scores are often utilized to analyze observational data. Propensity scores can be defined as the probability that a patient has been assigned to an intervention, given their covariates. As a result, such methods reduce imbalances in baseline covariate distributions between groups. There are various methods which can be employed. For instance, Rosenbaum and Rubin created a method to stratify patients based on their propensity scores and used the average effect across strata. Rosenbaum also used propensity scores to weigh individual observations and match subjects by their propensity scores. As a result, controls and cases had similar covariate values. Braun and colleagues (2014) took a different approach: they used propensity scores without having covariates balanced by treatment assignment. They showed that propensity scores are beneficial in research scenarios when there are multiple confounders; the team proved that such approaches might reduce the dimensionality. Also, Braun and colleagues show that propensity scores are more reliable than standard regression – in fact, in model misspecification, standard regression would lead to bias and errors.

3.1. Measurement Error and Observational Studies

Measurement errors are a normal part of medical research. Methods, such as likelihood-based approaches, regression calibration, and Bayesian approaches, can be used to adjust for measurement error. It’s interesting to mention that misclassification of treatment assignment will lead to error in exposures as well as propensity scores (Braun, 2014). Actually, propensity score methods are widely used to estimate measurement error in confounders, including missing confounders. In fact, from using regression calibration to developing a consistent inverse probability weighting estimator, numerous techniques have been utilized to adjust for measurement error in confounders.

Since treatment assignment in observational studies can be measured with error, Braun and team focused on another fundamental problem: measurement error in the exposure variables/treatment assignments. They adjusted for measurement error in the propensity score using validation data. Then, the team used the adjusted scores to adjust for measurement error in the treatment effects, using external validation. Note that Braun and colleagues used four propensity score methods: stratification, inverse probability weighting of the likelihood, matching, and covariate adjustment. The research team (2014) evaluated the proposed likelihood adjustment by comparing the estimates of treatment effect for true treatment assignment, error-prone treatment assignment, and the likelihood adjustment. Note that during simulations, the team used one data set for the main study and another for validation dataset.

III. Methods of Prediction: Conclusion

            Methods of predictions are an important part of clinical research which can evaluate the risk of developing a disease. Interestingly, some popular risk scores are Apgar score, Acute Physiology and Chronic Health Evaluation (APACHE) score, Framingham risk score, etc. (Janssen et al., 2009). While the development of new models is crucial, validation is among the most important aspects which must be conducted before application in practice. It’s not a secret that the distribution of predictors might vary between samples and populations – the lack of accuracy, face validity or user-friendly approach can simply lead to poor practice. On the other hand, the adjustment of prediction models is another vital aspect which experts must consider. As described above, Braun and colleagues (2014) revealed the need for adjustments in survival data and time to event data. Last but not the least, assessment is also fundamental. In fact, impact studies can test the effects of models of prediction on provider behavior.

With the implementation of technology in healthcare and the improvement of electronic health record systems, electronic predictive models are becoming more and more attractive. Nevertheless, methods of prediction should only complement clinical judgment and if possible, recommend practical decisions to improve patients’ well-being.

IV. References:

Altman, D., & Bland, D. (1998). Time to event (survival) data. BMJ. Retrieved from

Bouwmeester, W., Zuithoff, N., Mallett, S., Geerkings, M., Vergouwe, Y., Steyerberg, E., Altman, D., & Moons (2012). Reporting and Methods in Clinical Prediction Research: A Systematic Review. PLOS Medicine, 9(5).

Braun, D. (2014). Statistical Methods to Adjust for Measurement Error in Risk Prediction Models and Observational Studies. Retrieved from

Chen, S., Wang, W., Broman, K., Katki, H., & Parmigiani, G. (2004). BayesMendel: an R Environment for Mendelian Risk Prediction. Statistical Applications in Genetics and Molecular Biology, 3.

Ishaya, D., & Dikko, H. (2013). Survival and Hazard Model Analysis of Breastfeeding Variables

on Return to Postpartum Amenorrhea in Rural Mada Women ofCentral Nigeria. IOSR Journal of Mathematics, 8, p. 1-9.

Janssen, K., Vergouwe, Y., Kalkman, C., Grobbee, D., & Moons, K. (2009). A simple method to adjust clinical prediction models to local circumstances. Canadian Journal of Anesthesia, 56(3).

Prinja, S., Gupta, N., & Verma, R. (2010). Censoring in Clinical Trials: Review of Survival Analysis Techniques. Indian Journal of Community Medicine 35(2), p. 217-221.

Smith, R. (1990). Observational studies and predictive models. Anesthesia & Analgesia, 70, p. 235-239.

Sommers, B., Gawande, A., & Baicker, K. (2017). Health Insurance Coverage and Health – What the Recent Evidence Tells Us? The New England Journal of Medicine, 377, p. 586-593.

Trotta, F. (2012). Discrepancies between observational studies and randomized controlled trials. Retrieved from

Waljee, A., Higgins, P., & Singal, A. (2014). A Primer on Predictive Models. Clinical and Translational Gastroenterology, 5(1).

rNPV: Approaches to net present value (NPV) in pharmaceutical research and development (R&D)

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Pharmaceutical Research and Development & Commercialization of Biotechnology

Pharmaceutical research and development (R&D) is a challenging niche in which experts must find a balance between financial revenue and people’s well-being. It’s not a secret that drug discovery is a complex and expensive process. On top of that, the field of biotechnology is expanding progressively and developing at a rapid pace, which forces researchers and venture capitalists to fight numerous obstacles (e.g., competition, risks, delays, and costs). There are many experimental unknows and ethical regulations that apply to the industry, which adds more to its complexity. At the same time, medical research is a precise science: there’s clear data, statistical models, and well-defined stages of research and clinical trials. This transparency can help scientists and sponsors collaborate and commercialize biotechnology with the sole purpose to improve healthcare practices.

Nevertheless, sometimes even great ideas are never shared with the world as scientists cannot market their discoveries and products. As a result, some life-saving medications and treatments may have been lost in piles of documentation and insufficient funding. Thus, how can a novel discovery make its way to being manufactured, commercialized, and adopted in healthcare? The answer is in the successful approach and implementation of the net present value (NPV) and its alternatives (“Net Present Value – NVP”).

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Net Present Value, Risks, and Revenue

Industries worldwide rely on forecast-based valuations and predictions (“NPV vs. rNPV”). The net present value is one of the main indicators of the profitability of a project, which, in fact, is a preferred model by many investors. In simple words, the net present value approach is based on the present value of cash inflows and outflows over time and accounts risk by discount rates. A positive net present value of a project means that the earnings will exceed the costs, which will justify the actual investment. Note that the main assumption is that money received today is more valuable than money received tomorrow, as it can be invested and profitable now (Stewart et al., 2001). Thus, the net present value model aims to calculate what tomorrow’s cash flow would be worth now.

To calculate the net present value of a novel discovery, experts can follow an established formula which considers various factors (e.g., the net cash inflow during a certain net cash flow, total initial investment costs, discount rate, and the number of time periods). If a biotech company wants to market a drug, firstly, they’ll estimate the future cash flows and discount those eventual cash flows into a present value amount. Then, they’ll explore predictions and figures further. Although the field of life science is a specific area, financial decisions and marketing strategies are alike in other industries. Therefore, let’s set another example, which will help us understand the net present value model in real life. Imagine you want to buy a shop, and you’ve made all the required calculations, cash flow predictions, and discount rates – which gives you a sum of $250,000. The owner, however, is willing to sell their property for $150,000, which for you presents a positive net present value investment. As a matter of fact, if the owner is willing to sell for $150,000, then, this is a $100,000 net gain for you. Note that the net gain is the intrinsic value of the purchase or the investment. In other words, in this scenario, this is a good deal for the buyer. It’s not surprising that biotech companies also aim for good deals.

Risk-adjusted Net Present Value and Biotechnologies

When it comes to biotechnology and pharmaceutical companies, though, revenue and investments cannot always follow a clear formula and positive predictions. As explained above, researchers and bioentrepreneurs face numerous challenges – sadly, a drug may never reach the market. At the same time, drug development is often well-regulated, following defined phases and rules. This helps experts employ statistical analyses, which can estimate failures, probabilities, and success rates (“NPV vs. rNPV”). As a result, biotechnology companies often rely on risk-adjusted net present value (rNPV) instead of the standard net present value model. Note that the net present value and the risk-adjusted net present value may coincide only when risk is estimated and, of course, eliminated.

The risk-adjusted net present value model is a more realistic approach, which accounts for delays, risks, and revenues – during the early research and development stages and through the actual development (Stewart et al., 2001). Just like with any other industry, only a fair evaluation will help companies market and sell their products, which can only benefit society.

Risk-adjusted Net Present Value, Alternative Models, and Biotech Investment

Relying on assumptions can lead to errors, so apart from investment costs, discount rates, and revenue – risks and variables should be estimated. Experts agree that the risk-adjusted net present value model is a more realist approach to biotech projects than the net present value, which can help bioentrepreneurs market a product and save lives. Nevertheless, medical research is full of obstacles, risks, and delays: many projects are limited to Phase I, data is highly secured, time frames are unclear, and post-discovery is rarely discussed. In fact, time is crucial. For instance, since any Phase III may last from three to five years, four years is a good frame for the risk-adjusted net present value model to be effective (Svennebring & Wikberg,  2013).

What’s more, researchers and sponsors have started to apply various alternatives based on mathematical models, which can help them explore all the potential risks and benefits of a new product and its success rates. Some suggestions are to use numerous mathematical formulas, e.g., apply different probability rates during different research stages or analyze more than one compound for development (Svennebring & Wikberg, 2013). Apart from the risk-adjusted net present values, the payback method is another popular metric. However, the payback method doesn’t account for the time value of money, which means it cannot be applied to long-term clinical trials. The internal rate of return, on the other hand, is another financial alternative, which can be employed in biotechnology projects because it’s based on annual calculations and comparisons (“Net Present Value – NPV”). Unfortunately, many parties do not utilize proper methods and rely on unrealistic models and expectations, which leads to low quality, high prices, and poor healthcare practices. Sadly enough, revenue is the main motivator: mainly start-ups risk and invest in early projects, while big companies rely on safety.

Monte Carlo Simulations, Risk-adjusted Net Present Value, and Big Pharma

The recent tendency for big pharma to invest in established products, which have passed the early stages of development, might be profitable but not progressive. What’s more, many standard risk-adjusted net present value models are way too optimistic. Thus, the stringent net present value approach (rpNVP) is a more effective model, which reflects the dynamics of large portfolios and leading pharmaceutical companies and investors (“Risk-adjusted NPV is Notoriously Fallible,” 2015).

To support long-term projects and rare diseases, experts can integrate Monte Carlo simulations to access factors, such as price, peak market share, accessible market matter, and most of all, various research and development budgets. The model is used to assess the probability of different outcomes and random variables in order to understand the impact of risk and uncertainty. Applying Monte Carlo simulations to the risk-adjusted net present value model gives numerous outcomes and probability distributions (incl. histogram plot and Tornado plot), which will help researchers and investors in the field of life sciences collaborate and support each other. In fact, the Monte Carlo method is a systematic analysis which is sensitive to multiple parameters and which is beneficial in the analysis of uncertainties – factors that can make this model a leading technique in the field of biotechnology.

Drug Discovery, Development, and Marketing

Since healthcare is a complex process, embracing the whole picture and presenting realistic models can only benefit healthcare. Let’s say that a rare disease, such as non-small cell lung cancer, has been targeted. Then, novel treatments should be identified – which is the process of drug discovery. To be more precise, drug discovery starts with the target being identified and ends with the beginning of Phase I. After that, research focuses on drug development and optimizing the compounds and their properties (e.g., absorption, distribution, metabolism, elimination, and toxicology – ADMET). When compounds are synthesized effectively and can act as a novel drug, research can continue, in both animals and humans. In addition, predictive models can support researchers in the further synthesize of compounds (Svennebring & Wikberg, 2013). As a matter of fact, drug manufacturing doesn’t stop there – aspects, such as post-discovery and marketing can determine if a drug will reach patients and become a leading product.

Therefore, when it comes to funding decisions, marketers and big pharmaceutical companies should realize that all four stages of research (Phases I, II, III, and IV) are worth funding as they can pay off. Revenue can be achieved only through applying effective net present value models and/or their alternatives, which target all aspects, risks, and outcomes of research.


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But… What Do Numbers Mean?

To understand the problem of investment in the niche of biotechnology, let’s set an example which can help us put new life into financial formulas and net present value models. Imagine that a research organization has made a discovery that can help many people struggling with asthma and that could be worth millions. Their next step is clear: they’ll have to convince sponsors to invest in their research by utilizing a precise model (such as the risk-adjusted net present value model) and by considering various aspects (e.g., scientific progress, good management practices, and intellectual property). In fact, a beneficial suggestion is to start with the evaluation of the final step of the funding process – the royalty paid from the estimated annual revenue of the new medicine (Stewart et al., 2001.). In the scenario presented by Stewart and his colleagues, the annual market for asthma is approximately $5.8 billion. Nevertheless, since the competition in the sector is high, the share of the new drug will be around 5%; in other words, the annual gross return will be $290 million. Note that 60% usually goes to manufacturing and marketing companies, 5% is the royalty for the organization that discovered the drug, and 35% is for the biotechnology company that develops the drug. So, let’s say that a patent expert claims the new medicine will beat the competition in the field for the next 18 years. Still, companies need to consider the fact that it takes around eight years to conduct clinical trials and get the drug approved by the regulatory bodies. Consequently, it means that the potential payoff for the biotechnology company will be around 10 years (18 years protected from competition minus 8 years needed for research gives us ten years of revenue). The annual revenue is, as mentioned above, 35%, which in our example equals $100 million. In the end, the final payoff is $1 billion (10 years times $100 million each year gives us $1 billion of total revenue).

This scenario is way too optimistic, though. It’s not a secret that often biotechnology companies and investors have no sufficient revenue. Factors, such as costs, risks, and time, can interfere with the presented models and expected earnings. Clinical trials may require additional costs and filings; they also are subject to many errors and risks. In fact, a study may never reach Phase III of a clinical trial and some drugs may never reach the market. Thus, risk-adjusted costs need to be considered. Time is also a major risk for success or failure. As explained above, money received today is more valuable than money received tomorrow. Note that the amount tomorrow’s money may lose in value annually is the so-called discount rate. Since clinical trials are time-consuming, discount rates can significantly affect investments and biotech companies. When we apply all these factors to the example given by Stewart and colleagues, the new drug designed to treat asthma has a lower value, of around $18 million. These figures are more realistic and can be helpful in any investment decisions (e.g., if a company will pay milestones or a royalty on sales).

The Future of Biotech: Statistics, Predictions, and Options to Overcome Risks

There’s no doubt that discount rates are an influential factor. Yet, while precise models can help experts evaluate risks and promote their products, many big pharma and biotech companies admit that teams often base their decisions on uncertainty. A survey, based on interviews with 44 CEOs in the field, revealed that 21% used simple cost-plus models and 12% made a guess (Svennebring & Wikberg, 2001). Sadly, in a field where real lives and patients’ well-being may suffer, uncertainty is unacceptable.

Therefore, apart from the discounted cash flow model used to overcome obstacles, there are various practical options, which a company may employ to become more flexible and successful (“Basics of Valuation”)

  • Option to defer: Once an investment has started, it cannot be recuperated. However, postponing can help experts analyze risks and returns and overcome uncertainties. Some companies may wait until the net present value is positive. On the other hand, this step is not recommended in a competitive environment where the first mover advantage is crucial.
  • Option to expand or contract: Experts may decide to change the project. For instance, they may build units, which can close down in case of low demand, in order to cut costs. Testing new markets is also recommended.
  • Option to abandon or license: If the project fails, the company may abandon it by recuperating a salvation value.
  • Option to switch: Another strategy is for the company to switch and move its productions to a cost-effective place. Yet, ethical considerations and cultural differences should be assessed.
  • Option to stage investments: Stage investments are also recommended as they can help experts re-evaluate each stage, which is beneficial in research and development projects.
  • Option to grow: Growth options are also common. Expanding to other countries, clients and products is an effective financial step.

We should mention that in the field of life science, the option to abandon is quite common. However, the dynamics in the field are way too complicated, and it’s not easy to terminate a project. In the end, revenue is not the main goal but improved healthcare and patients’ well-being.

Net Present Value and Life Science: Conclusion

Financial decisions should be based on clear formulas, realistic expectations, and transparent predictions. The net present value model is a common approach, which helps sponsors decide if an investment will be successful. Note that a successful investment means that earnings will succeed costs and a dollar today is more valuable than a dollar tomorrow. At the same time, drug research and development are a challenging field; clinical trials are prone to errors, risks, delays, and unexpected costs. Thus, biotech companies are encouraged to use more sophisticated models, such as risk-adjusted net present value, in order to predict risks and additional expenditures. Note that there are alternative models which suggest using different predictive values for the different stages of research and for the different compounds of a novel drug. In fact, the Monte Carlo simulations model offers a complete understanding of the process and its success rates. Of course, pharmaceutical companies should also develop effective strategies in case an investment turns out to be unsuccessful.

At the same, biotech start-ups and big pharma giants should consider the specific field of their work. Drug discovery and development is not only a scientific experiment or a financial endeavor; it’s not about the reputation of the research institution or the revenues of the investors either. Drug research and development has always been about people’s well-being and improved healthcare. Sponsors need to encourage Phase I studies and embrace risks because a trial may prove to be beneficial from a medical point a view. In the end, finding a balance between well-being and profit is hard but not impossible.


Basics of Valuation. Retrieved from file:///C:/Users/Owner/Downloads/9783642108198-c1.pdf

Net Present Value – NPV. Retrieved from

NPV vs. rNPV. Retrieved from

Svennebring, A., & Wikberg, J. (2013). Net present value approached for drug discovery. Springerplus, 2. Retrieved from

Stewart, J., Allison, P., & Johnson, R. (2001). Putting a price on biotechnology. Retrieved from

Risk-adjusted NPV is Notoriously Fallible (2015, October 26). Retrieved from