COST-EFFICIENT DESIGN OF EPIDEMIOLOGICAL STUDIES USING DISCRETE EVENT SIMULATION MODEL – EPISOL STUDY SIMULATOR 1.0

Purposes: 1. To design a study simulator that mimics the processes of recruitment and follow-up of participants in epidemiological studies. 2. To use the study simulator to assist in determining cost-efficient designs for a clinical trial under specified assumptions.  

Methods: We designed a discrete event simulation model, the EpiSol Study Simulator. Data from Surgical Treatments Outcomes Project for Dysfunctional Uterine Bleeding (STOP-DUB) and Longitudinal Study of Ocular Complications of AIDS (LSOCA) were used to calibrate the model. To achieve the first objective, the plausibility inspection method assessed how well the EpiSol Study Simulator simulated baseline and follow-up characteristics of participants, cost of reimbursements and achieved power. Sensitivity analyses altering the correlation coefficient between follow-up visits and the mean difference in EQ-5D scores at follow-up were conducted for the STOP-DUB. For the second objective, we compared different follow-up intervals (3, 6, and 12 months).  

Results: The EpiSol Study Simulator scored an average percent difference of 5.5 between the simulated and observed estimates with a percent range of (0.38, 14) for the STOP-DUB hysterectomy group and an average percent difference of 4.26 between the simulated and observed estimates with a percent range of (0.59, 12) for the STOP-DUB endometrial ablation group. The simulated total cost of reimbursement for the STOP-DUB was $529K which underestimated the reported cost by just $55K. In the LSOCA, the average percent difference was 2.6 between the simulated and observed estimates with a range of (0.13, 6). The simulated total cost of reimbursement for the LSOCA was $17.3 million which overestimated the cost of reimbursement by $2 million. The estimated study costs were lower when the correlation coefficient of observations within an individual over time was lower. Holding sample size constant, we can observe significant differences in the mean outcome (EQ-5D score) of 0.025 when the correlation coefficient was below 0.3 for the STOP-DUB treatment groups. Power was reduced when the investigators varied follow-up interval from 3 months to 12 months and the correlation coefficient was set at 0.70.  

Conclusion: We propose incorporating EpiSol Study Simulator into the process of designing epidemiological studies because it allows investigators to examine and update assumptions about the optimal sample size, number of visits, study duration, and expected differences in effect sizes.