Abstract WP86: Data Collection Impacts on Study Costs: A Real-World Analysis
Megan Tessmer, Jessica Staloch, Christopher D Streib, Luis Silva, Jessica Gieseke, Elizabeth Nierengarten, Abbey Staugaitis- Advanced and Specialized Nursing
- Cardiology and Cardiovascular Medicine
- Neurology (clinical)
Introduction: Costs are a major barrier to conducting high-quality clinical trials, yet specific determinants of operational costs are understudied. Budgets are frequently planned assuming additional data fields increase cost in a proportional manner. We compared estimated costs for adding data fields to real-world costs as data fields increase.
Methods: We studied the impact of data fields on operational costs for three Phase III acute stroke clinical trials of similar design, clinical complexity, and logistics. We retrospectively reviewed records of coordinator time logged for completion of data fields per study during 2021 and 2022. Study data management hours included time for data entry, query resolution, and data verification as requested by the study monitor. Our standard rate per hour was used to calculate actual cost for each clinical trial. We estimated the cost of a single data field by dividing the averaged cost per subject by the total data fields collected for the least expensive study. We used this value to estimate cost and compared that to the actual costs.
Results: Study 1 was the least expensive study, and thus used to calculate the estimated cost of a single data field. There were 277 data fields and a cost of $513 per patient, the cost per data field was calculated as $1.85.
Study 2 had 775 data fields collected per subject and should have an estimated cost of $1,436.40. The actual cost was $2,291.57 which is 1.6 times higher than expected.
Study 3 had 2245 data fields collected per patient and should have an estimated cost of $4,155.30. The actual cost was $20,176.65 which is 4.86 times higher than expected. Results are summarized in Figure 1.
Conclusion: Our analysis of real-world study data management costs demonstrate that additional data fields have a disproportionate increase in cost. Assuming a proportional increase could lead to inaccurate estimates with large budget and operational cost impacts when a study is implemented.