While the value of pharmaceuticals/medical devices can be measured by means of QALYs, a certain practical problem is involved. Let us consider the case of assessment of an antihypertensive agent. Assessments of antihypertensives have been based on their effectiveness at lowering blood pressure as determined in clinical trials. By contrast, QALYs-based assessment requires quantification of the extended life expectancy and change in QOL throughout due to the antihypertensive effect. This, in turn, makes it necessary to estimate differences, due to this effect, in respects such as reduction of strokes, the distribution of degree of disability after strokes, and life years according to the degree of disability. The duration of clinical trials required for production approval or application for registration of new drugs is a few months at the most, and only a few years even in mega-trials. This suggests that it would be utterly impossible to obtain all of the data needed for QALYs-based assessment solely from clinical trials.

In pharmacoeconomics, we use models to overcome the limitations associated with clinical trials. Models may be defined as artificial structures for the flow of treatment and long-term disease prognosis. We use models to estimate the occurrence of events, life expectancy, and related costs (medical service costs, nursing care costs, etc.) over a time frame exceeding the duration of the clinical trials (see Figure 4).

Figure 4 Clinical Trials and Model Simulation

A decision tree model is often used for analysis of acute diseases (see Figure 5). It employs a very simple structure, and constitutes a treatment and disease flow from the left to the right. It is constructed on the basis of conceivable scenarios and their probabilities of occurrence. As shown in this figure, an evaluation of the effect of initial treatment may be made on the third day after its initiation. If the treatment is found to be effective at that time, it is continued. If not, a switch is made to a different drug, for example. There is also a difference in the flow line depending on the probability of insufficient effect. The decision tree enables estimation of the probable cost and life years as a percentage in the case of administration of a certain type of treatment. These are referred to as the "expected cost" and "expected life years".

Figure 5 Decision Tree

Analysis of chronic diseases, on the other hand, often utilizes the Markov model (see Figure 6-1). Herein, the prognosis for long-term diseases is divided into several stages, and a simulation is made of the patient's progress through these stages over a certain period of time. Figure 6-1 shows a Markov model concerning the prognosis for a hypothetical disease (disease A). Assuming that the new drug A would reduce the progression rate (and mortality) 50 percent more effectively than the conventional therapy, Figure 6-2 shows the survival curve (over a period of 10 years) resulting from a simulation based on the Markov model. From this simulation, it can be seen that there was a difference of 0.9 years between the two groups in respect of the expected life years.

Figure 6-1 Markov Model

Figure 6-2 Simulation based on the Markov Model (survival curve)

For analysis of diseases in which two or more conditions proceed simultaneously (such as diabetes mellitus), analysts may perform a Monte Carlo simulation.