Association between decentralised clinical trial adoption and trial duration: a retrospective cross-sectional study...

To examine the association between decentralised clinical trial (DCT) adoption and trial duration in metabolic disease trials.

Retrospective cross-sectional analysis using analyst-curated metadata from the GlobalData Clinical Trials Database, matched with ClinicalTrials.gov records via unique identifiers.

Industry-initiated phases 1-3 trials for metabolic diseases involving the USA (first patient enrolment 2015-2023).

444 trials (phase 1: n=140; phase 2: n=155; phase 3: n=149).

The primary outcome was clinical trial duration (CTD), defined as the interval from first patient in (FPI) to last patient last visit. The secondary outcome was the primary completion period (PCD-FPI), used for sensitivity analysis.

Among 444 trials, 124 (27.9%) were identified as DCTs. Adoption differed significantly across clinical phases (phase 1: 11.4%; phase 2: 29.7%; phase 3: 41.6%; [Formula: see text]). Two-way analysis of variance showed that clinical phase was significantly associated with CTD ([Formula: see text]), whereas the main effect of DCT adoption was not significant ([Formula: see text]). Phase 2 and 3 DCTs exhibited numerically shorter mean durations (17.3 vs 18.3 months; 23.4 vs 25.0 months), but these differences did not reach statistical significance ([Formula: see text]). In phase-stratified regression analyses, DCT status remained non-significant across all phases. Older adult inclusion was associated with shorter CTD in phase 3 [Formula: see text]). Sensitivity analysis using PCD-FPI yielded consistent findings.

DCT adoption was not significantly associated with shorter trial duration in metabolic disease trials after adjustment for clinical phase and clinical characteristics. These findings may reflect the current stage of DCT implementation, in which operational complexities may coexist with theoretical expectations of efficiency. Further evaluation in more mature implementation settings may clarify whether decentralised approaches are associated with improved efficiency.