Consider the following problem:
A relatively cheap drug (drug A) has been used for many years to treat patients with disease X. The drug is considered quite successful since data reveals that 85% of patients using it have a ‘good outcome’ which means they survive for at least 2 years. The drug is cheap and the overall “financial benefit” of the drug (which assumes a ‘good outcome’ is worth $5000 and is defined as this figure minus the cost) has a mean of $4985.On seeing the data the Health Authority recommends a ban against the use of drug B. Is this a rational decision?
There is an alternative drug (drug B) that a number of specialists in disease X strongly recommend. However, the data reveals that only 65% of patients using drug B survive for at least 2 years. Moreover, this drug is expensive. The overall “financial benefit” of the drug has a mean of just $2777.
The answer turns out to be no. This short paper explains this using a simple Bayesian network model that you can run (by downloading the free copy of AgenaRisk). Moreover, you can also compute the optimal decision automatically using the Hybrid Influence Diagram tool in AgenaRisk.
Fenton N.E. (2018) "A Bayesian Network and Influence Diagram for a simple example of Drug Economics Decision Making", DOI: https://doi.org/10.13140/RG.2.2.33659.77600
The article presents a scenario where a relatively cheap drug (drug A) has been used for many years to treat patients with disease X, and is considered quite successful with a "financial benefit" of $4985. There is also an alternative drug (drug B) that some specialists strongly recommend, but only 65% of patients using it survive for at least 2 years and has a "financial benefit" of just $2777.
ReplyDeleteThe Health Authority recommends a ban against the use of drug B based on the data. However, the author argues that this decision is not rational. The author uses a Bayesian network model to explain the reasoning behind this conclusion, and notes that readers can run the model themselves using a free copy of AgenaRisk. Additionally, the author suggests that readers can use the Hybrid Influence Diagram tool in AgenaRisk to compute the optimal decision automatically. San Luis Obispo DUI Attorney
Overall, the article presents an interesting problem and provides a valuable tool for readers to understand the reasoning behind the decision. By using a Bayesian network model, the author shows that the decision to ban drug B may not be rational, and encourages readers to consider all available data and tools when making decisions. This is particularly relevant in the field of healthcare, where decisions can have significant impacts on patients' lives.