Insights informing strategies for optimizing the collection of clinical trial data

Abstract Although past research has quantified the proportion and types of procedures that support clinical trial endpoints, little is known about the volume and nature of data collected by these procedures and their impact on participant and site burden. Tufts CSDD and 15 TransCelerate Biopharma sponsor companies convened to collect data from 105 multi-therapeutic protocols with a primary completion date after 2018. Overall data volume by study and data volume by participant were analyzed and procedures were categorized into core, standard and non-core based on the endpoints they supported. Study results show that total data volume continues to grow with 5.9 million datapoints now collected on average per phase III protocol, up 11% annually since 2020. Non-core procedures comprise 17.8% of total phase II and 16.2% of total phase III procedures per protocol, a downward trend from the proportion of non-core procedures observed in 2020. Nearly half (46%) of phase II and 35% of phase III non-core procedures gather data for exploratory and future use purposes. On average, 6.6% and 12.6% of procedures supporting core and standard endpoints from phase II and phase III protocols are deemed non-essential (i.e., procedures determined by the clinical team or protocol authors as being performed in excess of the number of times required to demonstrate a clinical outcome), and their associated data represents 8.2% to 17.1% of the total datapoints collected. Combined, nearly one-third of all procedures and datapoints collected per protocol is classified as non-core or are non-essential procedures supporting core standard/required endpoints with more than half of this data associated with clinical and patient-reported questionnaires. As much as 30% of participant and site burden is associated with noncore procedures or are non-essential procedures supporting core, standard/required endpoints. The implications of these results and potential strategies to optimize the collection of protocol data are discussed in this analysis.