Introduction: While the global incidence of HIV-1 vertical transmission has reduced greatly, a persistent and unacceptable number of new infant infections continue to occur through breastfeeding. Long-acting antiretroviral agents, including injectable drugs and broadly neutralizing monoclonal antibodies, given as prophylaxis to infants hold great potential to eliminate HIV-1 transmission. However, given the low frequency of HIV-1 vertical transmission during breastfeeding in trial settings, efficient and innovative study designs will be necessary to evaluate the efficacy of agents administered to infants as prophylaxis. Key Design Considerations: We provide an overview of study design considerations and assess alternative specifications that may improve the efficiency of future trials to evaluate the efficacy of a novel agent administered to infants in preventing breastfeeding acquisition of HIV-1. We highlight key attributes of the study estimand (intervention, target population, outcomes and endpoints, intercurrent events, and population-level summaries) and discuss nuances in their specification with respect to improving efficiency. We explore alternatives to the classic randomized clinical trial framework, including augmented clinical trials that incorporate external data to supplement clinical trial data. We also provide an overview of Bayesian and transportability-based statistical analysis approaches for analyzing such augmented clinical trials. Modifications to the study design in the context of the estimand framework alone will not be sufficient to address challenges introduced by the low frequency of transmission events. Alternative study designs that incorporate external data are becoming increasingly acceptable and may help alleviate resource constraints. Conclusion: A multi-faceted approach will be required to design efficacy trials for postnatal prophylaxis. This work provides a guide for thinking through key study design decisions.
Background: In Oncology, prognostic biomarkers are commonly used to tailor treatment intensity to a patient's risk of disease progression. Several recent Phase III trials embed two studies within one protocol, comparing standard versus de-intensified therapy in good-prognosis patients and intensified versus standard therapy in poor-prognosis patients, using a single fixed biomarker threshold to classify patients into good and poor prognosis. Such designs are clinically intuitive but rely on a prespecified cut-point for which there can be substantial uncertainty as to the optimal value.Methods: We propose a flexible threshold, biomarker-based design in which patients with intermediate biomarker values are randomized to three arms (control, de-intensified, intensified), while those at the extremes are randomized between control and de-intensified therapy (low biomarker) or intensified (high biomarker). The design jointly tests non-inferiority of de-intensified therapy in lower-risk patients and superiority of intensified therapy in higher-risk patients, while estimating the optimal biomarker thresholds. Family-wise Type I error is controlled through a two-stage bootstrap testing procedure.Results: In simulation settings where the alternative non-inferiority and superiority hypotheses hold over broad overlapping biomarker regions, the proposed flexible threshold biomarker design has greater power than a fixed threshold design while maintaining appropriate Type I error control. When the optimal decision rule matches the prespecified fixed cutoff, the two designs perform similarly. This reflects a bias-variance trade-off, in which fixed threshold designs perform well when the cutoff is correctly specified, whereas the flexible threshold biomarker design is more robust when the optimal threshold is uncertain or misspecified.Conclusions: The proposed flexible threshold biomarker design offers a robust and efficient framework for trials seeking to tailor treatment intensity using a prognostic biomarker. The approach is broadly applicable to other diseases in which both escalation and de-escalation of therapy are of clinical interest and there is clinical equipoise among the three treatment options for patients with intermediate biomarker values.
Background: Detecting discrepancies between registered and published trial outcomes currently requires manual expert review, limiting surveillance to small samples. No scalable automated system exists for continuous monitoring of outcome reporting integrity across the clinical trial literature. We aimed to develop and validate an automated screening pipeline for primary outcome discrepancies and to characterise its performance at scale.Methods: We developed an automated pipeline that retrieves trial registrations from ClinicalTrials.gov, matches them to publications via PubMed and screens for primary outcome discrepancies using a term-matching algorithm. We applied the pipeline to all 46,365 completed interventional trials with posted results (2015-2025) and validated performance against manual expert review of 123 trial-publication pairs, with independent large language model re-review as a second rater.Results: The pipeline processed 46,365 trials in under 48 h, matching 27,555 (59.4%) to publications and confirming 13,595 pairs via trial identifier verification. Automated screening classified 10,231 (75.3%) as concordant and flagged 3353 (24.7%) for review. Against manual review of 123 pairs, the screen achieved 100% specificity and 16.7% sensitivity (1 of 6 discrepancies); the flag's positive predictive value could not be estimated because the validation sample contained only one flagged pair. Independent large language model re-review showed 99.2% agreement (Cohen's kappa = 0.91). Manual review estimated a true discrepancy rate of 4.9% (95% confidence interval = 1.6%-8.9%), predominantly minor modifications (timeframe extensions, operationalisation changes) rather than substantive outcome switching. Automated flag rates were not associated with primary outcome statistical significance (21.0% vs 20.6%; p = 0.745).Discussion: Automated screening can efficiently triage tens of thousands of trials for outcome reporting surveillance, deprioritising 75% of pairs as concordant and concentrating manual review on the flagged subset. The pipeline provides a foundation for continuous, near-real-time monitoring of outcome reporting integrity that could be integrated into journal peer review or regulatory workflows.
Background/aims: This study had two objectives: (1) to determine the effectiveness of audit and feedback interventions for improving recruitment and retention in randomised controlled trials (hereinafter referred to as trials) and (2) to identify and code behaviour change techniques in the reported audit and feedback interventions. Methods: A systematic review was conducted to identify the effect of audit and feedback intervention (alone or in combination) on participant recruitment and/or retention compared to other strategies. Audit and feedback targeting clinical practice was excluded. Embase and MEDLINE databases were searched up to 9 January 2026. Interventions were coded using Behaviour Change Technique Taxonomy version 1. The results are presented in a narrative form. Results: Four studies met the eligibility criteria: three randomised and one non-randomised evaluation. Due to the heterogeneity of interventions and outcomes, a meta-analysis was deemed inappropriate. None of the randomised evaluations demonstrated a statistically significant effect of audit and feedback on recruitment outcomes; the non-randomised study reported an increasing trend in recruitment. The risk of bias was low for one and unclear for two trials. The observational study was at a critical risk of bias. None of the studies applied behavior change techniques explicitly. Twenty-one behavior change techniques were identified at least once across both audit and feedback and co-interventions. Ten behavior change techniques were identified in audit and feedback interventions only (mean = 4.8, standard deviation = 2.4). The behavior change technique identified most often in audit and feedback interventions was ‘2.7. Feedback on outcome(s) of behaviour’ (n = 4, 100%; e.g. emails with league tables describing recruitment performance). Conclusions: This review identified audit and feedback evaluations used to improve recruitment/ retention in a trial context and identified which behavior change techniques are currently used and their frequency. Only one study conceptualised its intervention as audit and feedback and utilised existing audit and feedback literature. Our analysis and behavior change technique coding were restricted to the limited reporting of intervention designs. Further research is needed to understand how audit and feedback is currently being used and whether it can be effective in improving recruitment and retention in trials.
