Can you discuss your experience with conducting sensitivity analyses in pharmacoeconomic evaluations?

Yes, I have experience conducting sensitivity analyses in pharmacoeconomic evaluations. In my previous role, I was responsible for analyzing the economic impact of pharmaceutical products using healthcare data and economic models. As part of this analysis, I conducted sensitivity analyses to assess the robustness of the results. I utilized statistical software like SAS and STATA to perform these analyses and interpret the findings. Through this experience, I developed strong analytical and problem-solving skills, as well as the ability to communicate complex information clearly.

Absolutely! In my previous position, I conducted numerous sensitivity analyses as part of pharmacoeconomic evaluations. For example, when evaluating the cost-effectiveness of a specific drug, I would vary key parameters, such as drug cost, efficacy, or disease progression rates, to assess their impact on the analysis. This allowed me to identify the key drivers of cost-effectiveness and evaluate the robustness of the findings. I used statistical software like SAS and STATA to perform these analyses, utilizing regression models, Monte Carlo simulations, and scenario analyses. By conducting sensitivity analyses, I ensured that our evaluations were comprehensive and accounted for uncertainties. Moreover, I documented the process and results in detailed reports, making it easy for stakeholders to understand the implications.

Certainly! Conducting sensitivity analyses in pharmacoeconomic evaluations has been a core aspect of my work. In a recent project, I conducted a sensitivity analysis to evaluate the cost-effectiveness of a new drug compared to the standard of care. I considered multiple variables, including drug efficacy, side effect rates, drug costs, and patient adherence rates. By systematically varying these parameters within clinically plausible ranges, I assessed their impact on the incremental cost-effectiveness ratio (ICER). This analysis allowed us to determine the robustness of the ICER estimate and identify key drivers of cost-effectiveness. To ensure the accuracy of the analysis, I conducted probabilistic sensitivity analyses using Monte Carlo simulations. I simulated thousands of scenarios to capture the uncertainty surrounding the input parameters and produced a cost-effectiveness acceptability curve. This curve provided valuable insights into the probability of the new drug being cost-effective at different willingness-to-pay thresholds. Overall, my experience with conducting sensitivity analyses demonstrates my strong data analysis, economic modeling, and problem-solving skills, as well as my ability to communicate complex findings to stakeholders.