Sample Size Determination for Pilot Studies Based on Event Thresholds: A Framework for Evaluating Feasibility of Research Processes

Background: Pilot studies are essential for assessing feasibility and operational processes before large-scale research. This study presents a Poisson-based framework for determining the minimum pilot sample size based on discrete event thresholds. Methods: The number of events represents occurrences such as errors, deviations, or missing data that indicate whether a process is functioning as intended. Setting a maximum tolerable threshold for these events provides a clear decision rule for proceeding or refining study procedures. A Poisson-based model was applied for sample size calculation. Results: Based on α ≤ 0.05, the findings show that sample sizes vary depending on the study conditions, namely the maximum tolerable threshold and event rate. Sample size increases with the number of thresholds, while other parameters remain fixed. When α ≤ 0.05, sample sizes ranging from 12 to 26 are generally sufficient for studies involving three to five maximum tolerable thresholds, assuming an event rate of 10.0%. Larger sample sizes may be necessary when a greater number of thresholds are involved or when the acceptable event rate is low. Conclusions: The framework of sample size determination facilitates the early identification of potential issues and supports informed decision-making prior to conducting a full-scale study. In general, a sample size of 25 to 30 units is considered adequate for a pilot study, consistent with existing literature. However, larger sample sizes may be necessary when a greater number of thresholds are involved or when the acceptable event rate is low.