Protocol transparency is a cornerstone of reproducible health research, yet clinical trial protocols for whole-system interventions such as Ayurveda remain underserved by current reporting standards. SPIRIT 2025 introduced a dedicated open-science section and now comprises 34 minimum protocol items; however, whether this framework—designed by a Delphi consensus involving 317 participants—can accommodate the multi-modal, individualized logic of Ayurvedic whole-system research, and whether its open-science provisions can be operationalized for Ayurvedic trials, has not been articulated. Building on the EQUATOR Network’s framework for reporting-guideline development, we propose a conceptual framework and eleven candidate SPIRIT–Ayurveda extension items for future Delphi evaluation, rather than immediate implementation. The proposed framework integrates reproducibility, transparency, and open-science principles into protocol reporting for whole-system Ayurvedic clinical trials. We synthesize evidence of transparency gaps in registered Ayurvedic trials: only 64.8
Abstract Background Combined p-values derived from summary statistics of baseline variables in randomized controlled trials (RCTs) are expected to follow a uniform distribution. Extreme values indicate similarity or dissimilarity across randomized groups. To date, investigations have been limited to a small number of clinical fields; here, we assess the distribution of combined p-values in a consecutive sample of RCTs. Methods Based on a PubMed search between February 1 and 18, 2022, 568 RCTs were included. Baseline variables were analysed using Carlisle’s method, yielding one combined p-value per RCT. A threshold of combined p-values of p ≤ 0.05 or p ≥ 0.95 was applied. Differences in combined p-value distributions between subgroups were assessed using the two-sample Anderson-Darling (AD) test. Results 26.2% (149/568) of the included RCTs had combined p-values of p ≤ 0.05 or p ≥ 0.95, compared with the 10% expected by chance. Of these, 104 RCTs (18.3%) were found in the upper extreme tail of p ≥ 0.95 and 45 RCTs (7.9%) in the lower extreme tail of p ≤ 0.05. Smaller RCTs (N ≤ 100) had more extreme combined p-values than larger RCTs (N > 100) (AD test p = 0.001). RCTs with > 7 baseline variables had more extreme combined p-values than those with ≤ 7 variables (AD test p = 0.004). A sensitivity analysis showed that 20.6% of RCTs still had extreme combined p-values after controlling for reporting errors related to SE and SD. Another sensitivity analysis showed that some extreme results could be explained by assuming high correlations between baseline variables within an RCT. Conclusion A substantially higher proportion of RCTs in a consecutive sample exhibited extreme similarities or dissimilarities in baseline characteristics across groups than expected by chance (26.2% vs 10%), indicating problems with randomisation. Further investigation into the causes and implications of implausible baseline distributions in RCTs is warranted. Registered 2023-03-20 at https://www.researchweb.org/fourol/project/280487
Patients in intensive care units (ICUs) patients frequently experience recurrent infections associated with prolonged hospitalization and increased mortality. Conventional analyses treating death or discharge as simple censoring may bias recurrence estimates, whereas joint modeling appropriately accounts for recurrent and terminal events. This study discusses models to investigate recurrent ICU-acquired infections using a joint frailty model accounting for terminal events and unobserved heterogeneity, quantifying infection burden and assessing risk factors while adjusting for competing risks. We analyzed a publicly available prospective single-center neuro-ICU cohort using a joint frailty model to assess recurrent hospital-acquired infections and competing terminal events (death and discharge), while evaluating their interdependence. We employed recurrent event analysis approaches, notably total time scales and gap time scales for sensitivity analysis. The model was first implemented with a single composite terminal event and later extended to treat death and discharge as discrete, unique outcomes. All analyses were performed in R version 4.5.1 using the frailtypack package (version 3.7.0). We found individual heterogeneity, frailty variance θ = 0.070, and a high positive dependency on the terminal outcome and recurrent infections α = 4.40 using a joint frailty model with combined terminal events. We examined a significant individual heterogeneity θ = 0.286 in relation to joint modeling for separate terminal outcomes. Similarly, we discovered a high positive association between recurring infections and death (α₁ = 6.86) and recurrent infections and discharge (α₂ = 5.81). There were no statistically significant effects of sex or Charlson Comorbidity Index on recurrent infections in the model with separate terminal events. However, under the composite terminal event model, female patients had a significantly lower hazard of recurrent infections compared with males (HR = 0.90, 95
Researchers often recruit providers through institutional affiliations, clinic leadership, and snowball sampling. However, these methods can restrict sample diversity, depend on self-reported eligibility, and introduce significant selection bias. Administrative datasets (e.g., Medicaid, commercial claims) offer a more systematic method for identifying providers, reducing reliance on peer networks. This paper demonstrates how administrative claims data can be used for recruiting healthcare providers, with this approach exemplified through a case example using New York State (NYS) Medicaid claims to identify high-volume medication for opioid use disorder (MOUD) providers for transition-age (TA) adults (18–25). NYS Medicaid pharmacy claims data from 2022 were used to identify MOUD providers to TA adults. From February to April 2024, 104 high-volume providers (treating n ≥ 5 TA adults) were identified and contacted using a multi-modal outreach strategy including phone, mail, and email. Recruitment strategies were iteratively adapted to provider responses to improve engagement. Of 104 providers contacted, 22 (21.2
Rapid qualitative analysis (RQA) is increasingly used in applied health research to generate timely, practice-relevant findings. While existing literature outlines overarching principles and frameworks, there is limited detailed guidance on how to operationalize team-based RQA in real-world settings. This paper describes a structured, in-person, team-based RQA approach and reports on its use across multiple applied qualitative health research studies. Building on Hamilton’s [1] foundational approach to RQA, we developed and refined a five-phase team-based RQA process—preparing, summarizing, re-organizing, synthesizing, and translating—designed for descriptive qualitative studies requiring timely outputs. The approach was applied across four qualitative health research projects conducted in Ontario, Canada (2021–2023), involving 33–38 interview transcripts per study and 7–10 analysts per team. Data sources informing this methodological paper included field notes, detailed meeting minutes from analysis events, and anonymous feedback from participating analysts (n = 17) with varying levels of qualitative expertise. Feedback was analyzed to identify perceived benefits, challenges, and recommendations. Team-based RQA enabled accelerated data analysis while maintaining analytic rigor and transparency. Key benefits included efficiency and timeliness, with substantial portions of summarization completed during intensive 2–3 day in-person analysis events; enhanced analytic accuracy through real-time collaboration; and increased interpretive depth from cross-functional team composition. The structured use of domain templates, visual workflow tools, and facilitated debriefings supported consistency and coordination across analysts. The approach also contributed to capacity building by fostering experiential learning among novice researchers. Reported challenges included logistical demands, resource requirements, and the intensity of rapid analysis timelines, as well as the need to sustain momentum during post-event synthesis and reporting phases. This paper provides a detailed, operationalized framework for conducting team-based RQA in applied qualitative or mixed methods health research. By specifying concrete procedures, tools, and team structures, the approach enhances reproducibility, transparency, and accessibility. It is particularly well-suited to descriptive studies requiring timely, actionable findings, though careful planning and experienced leadership are essential to ensure rigor and feasibility. Not applicable.
