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Pharmacoeconomic evaluation of new drugs is becoming increasingly important for health care planners. Favourable or unfavourable comments about cost-effectiveness could play a significant role in determining whether certain drugs or classes of drugs get widely used.
A recent study sought to review economic analyses of new drug treatments in oncology. The authors searched MEDLINE and Health STAR databases (1988-1998) for original English-language research articles of cost or cost-effectiveness analyses of 6 breakthrough cancer drugs in 3 categories. These included hematopoietic growth factors, serotonin antagonist emetics and taxane chemotherapeutic agents. In the studies analysed, a novel treatment was compared to either standard treatment or placebo.
The authors looked at qualitative conclusions regarding the new drug. Favourable or neutral comments included statements such as the new drug "reduces costs" or "is cost-equivalent" or "may be cost-effective". Unfavourable comments included statements such as the new drug has "higher costs" or is "not cost-effective".
Data from their paper have been re-fashioned to produce Table 1 below.
Data Sets Unfavourable Comments Favourable/Neutral A 9 15 B 1 19
This was a problem written as a TRIPSE for my 4th year Pharmacology course. I expected students to apply their knowledge of pharmacoeconomic issues to a new problem. Students were presented with two sets of data that appeared to reach different conclusions regarding a new drug. There were many different explanations possible (different perspectives, different sources of funding for the studies, different outcomes being measured etc). Having identified possible explanations, students were required to suggest possible avenues for further exploration. The data were abstracted from a publication in JAMA 282: 1453-1457, 1999. The authors were in reality comparing pharmacoeconomic analyses sponsored by pharmaceutical companies with those sponsored by non-profit organisations. This was a difficult problem to write since I had to extract the information without giving away too many clues. Unfortunately, the wording was quite ambiguous and students were quite confused by the term data sets. Although many students did identify perspective or source of funding as a potential explanation, they thought that data set A comparing the novel drug to a standard drug whereas the drug was being compared to a placebo in B. I had not thought of that explanation, but seemed reasonable enough given the ambiguity in the wording.
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