Efficiency optimization of the selection period in therapeutic trials

Area of Science:

• Clinical trial methodology
• Health economics
• Biostatistics

Background:

• Clinical trial eligibility determination involves assessing inclusion and exclusion criteria over a defined selection period.
• Patient assessments during the selection period occur at specific time intervals.
• Optimizing the efficiency of the selection period is crucial for cost-effective clinical trial recruitment.

Purpose of the Study:

• To develop and evaluate an approach for enhancing the efficiency of the clinical trial selection period.
• To construct prediction models using early selection period data to forecast subsequent patient exclusions.
• To reduce the costs associated with randomizing a patient in a clinical trial.

Main Methods:

• Utilized data from the Rotterdam Cardiovascular Risk Intervention (ROCARI) trial selection period, which included five patient visits.
• Developed logistic regression models employing data from the initial two patient visits to predict exclusions at the third visit.
• Assessed the impact of prediction models on the costs per randomization.

Main Results:

• Logistic regression models effectively predicted patient exclusions based on data from the first two visits.
• The application of prediction models indicated a potential cost reduction of $52 per randomization.
• This resulted in an estimated savings of over $450,000, representing a 3.6% decrease in recruitment costs.

Conclusions:

• Early data collected during a clinical trial's selection period can accurately predict subsequent patient exclusions.
• Implementing prediction models can significantly increase the efficiency of the selection period.
• This predictive approach is applicable to pilot studies and the initial phases of prolonged patient recruitment.