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A New Methodology to Estimate Drug Cost Avoidance in Clinical Trials: Development and Application
Sebastián García-Sánchez1, Roberto Collado-Borrell1, Eva González-Haba1
1Pharmacy Department, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
Background:
Oncology clinical trials can lead to relevant financial savings in drug acquisition for healthcare providers. Considerable methodological heterogeneity is observed among previous studies estimating these savings.
Methods:
We developed a methodology to estimate the economic benefit obtained from the enrollment of patients into clinical trials through the analysis of drug cost avoidance. We designed a decision algorithm to determine if a clinical trial is associated with drug cost avoidance. This algorithm is based on five scenarios according to the availability or not of standard treatment, the presence or absence of a control arm (placebo or active treatment), and the provider of the medication. We considered as reference the cost of the standard treatment that the patient would have received in routine clinical practice. We standardized drug doses and treatment durations according to the literature. Costs were considered from a National Health System perspective. We applied this methodology at a single, research-active University Hospital in 2019. A cost avoidance analysis per trial and patient was carried out on cancer patients.
Results:
We analyzed 140 trials in which 198 patients were recruited. Drug cost avoidance was found in 120 trials (85.7%). The estimated total drug cost avoidance amounted to over €3,200,000. Melanoma and genitourinary tumors were the tumor types associated with the highest cost avoidance. The average drug cost avoidance per patient was €16,245.
Conclusion:
We describe a standardized method to estimate drug cost avoidance in clinical trials. We have applied it to all ongoing oncology clinical trials in our center. This methodology could be valuable for other centers to analyze the potential saving of clinical trials.
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