Related Experiment Video
Updated: Jan 12, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
A review of use of external data and update on reporting standards in Sequential Multiple-Assignment Randomised
Isaac J Egesa1, Laura Bonnett1, Richard Emsley2
1Health Data Science, Institute of Population Health, University of Liverpool, Liverpool, UK.
Background:
The Sequential Multiple-Assignment Randomised Trial (SMART) design is considered the gold standard for developing adaptive interventions, which tailor treatments to individual patient characteristics and responses. While SMART offers a rigorous framework aligned with real-world clinical decision-making, it is often complex, time-consuming, and costly. As interest in SMART design grows, there is increasing recognition for the need to improve its implementation through more explicit guidance and best practices. Efficiency gains may also be possible by incorporating external data to inform their design, conduct, and analysis. This review aimed to identify all published trials using the SMART design, summarise their design, conduct, and reporting practices and evaluate the use of external data in their implementation.
Methods:
We searched PubMed, Medline, PsycINFO, Scopus, and Web of Science databases for all SMART up to June 30, 2024. External data were defined as non-simulated individual patient data collected outside the main SMART to supplement or inform the main trial.
Results:
We included 80 SMART, of which 35 (44%) were completed and 45 (56%) were ongoing. Most trials reported two phases of randomisation (93%), with the primary aim focusing on evaluating main effects (81%) of interventions at the first stage of randomisation. There was inadequate reporting of several key aspects, including sample size estimation, statistical analysis software, allocation concealment, data missingness, multiple testing, sensitivity analysis, and the use of SMART in the title. Seventeen (21%) SMART (4-completed trials and 13-trial protocols) referred to the use of external data from electronic health records (n = 12) and registries (n = 5). External data was used for recruitment (n = 11), outcome measures (n = 6), and to provide baseline covariate information (n = 1).
Conclusion:
SMART designs are increasingly used to develop adaptive interventions across diverse clinical contexts, yet key methodological features and basic components remain inconsistently reported. This limits transparency, reproducibility, and potential for translation into routine care. Although external data are widely used in standard randomised controlled trials, their use in the SMART is still limited, likely due to methodological and infrastructural challenges and the absence of tailored reporting standards. To improve the efficiency and generalisability of SMART designs, expert-led extensions of CONSORT and SPIRIT guidelines are needed, including specific recommendations for reporting external data use. Future research should explore optimal external data sources for informing SMART components and promote interdisciplinary collaboration and training to support high-quality implementation.
More Related Videos
Related Concept Videos
Randomized Experiments
Simple randomization
Simple...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs
Study Design in Statistics
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Study Designs in Epidemiology
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...

