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Background Best practices for clinical trials stipulate that statistical analysis plans (SAPs) need to be finalized before initiation of any analysis. However, there is limited guidance about when changes to SAPs are acceptable and how these changes should be incorporated into the research plan with appropriate documentation. Methods We conducted a survey of 12 pragmatic clinical trials (PCTs) in the Pain Management Collaboratory that evaluated nonpharmacological interventions for pain to assess the following SAP information: (1) location of statistical analysis details, (2) types of statistical analyses planned, (3) sponsor requirements, (4) templates used for development, (5) publication plan, (6) changes since trial launch, (7) process of documenting changes, and (8) process of updating the trial registry. Results All 12 PCTs provided details of their SAPs for the primary outcomes in the institutional review board–approved trial protocol; 8 included plans for secondary outcomes, and 6 included plans for tertiary/exploratory outcomes. Most PCTs made SAP changes after trial initiation, many as a result of COVID-19–related issues. Eleven of the PCTs were actively recruiting participants. Changes were made to sample size, study design, study arms, and analytical methods, all before the data lock/unblinding. In all cases, justification for the changes was documented in the trial protocol or SAP, signed off by the trial biostatistician and principal investigator, and reviewed/approved by an institutional review board, data and safety monitoring board, or sponsor. Conclusions We recommend that SAP changes can be acceptable up to the time of data lock/unblinding. To maintain full transparency and necessary rigor, clear documentation of such changes should include details, rationale, date(s) such changes were implemented, and evidence of approval by relevant oversight bodies. [ABSTRACT FROM AUTHOR]

The article focuses on monitoring and reporting adverse events (AEs) in pragmatic clinical trials (PCTs) testing nonpharmacological pain management interventions. Topics discussed include the challenges of AE data collection and reporting in PCTs, the variability of monitoring methods across trials, and recommendations to improve AE reporting and consistency in pain-focused PCTs.

Objective Despite careful design of clinical trials, unforeseen disruptions can arise. The PICOTS (Patient population, Intervention, Comparator, Outcomes, Timepoints, Setting) framework was used to assess disruptions in pain management research imposed by coronavirus disease 2019 (COVID-19) within the Pain Management Collaboratory. Methods Rapid qualitative methods were employed to identify trial disruptions due to COVID-19 in 11 pragmatic clinical trials of nonpharmacological approaches for pain management. The PICOTS framework was applied by investigators of 4 Collaboratory trials selected to cover 4 types of trial designs (individually randomized, stepped-wedge, cluster, sequential multiple assignment randomized trial—SMART). Interviews with the lead investigators of these trials were completed, and findings were presented/discussed on video calls over a 6-month period (March-August 2021) from which themes/lessons learned were identified and consensus reached. Results Investigators indicated that patient populations remained generally stable. A major COVID-19 trial disruption was moving from in-person to virtual care affecting delivery of interventions/comparators and outcome assessments. The resultant mixed-mode of care delivery created issues with intervention fidelity posing analytic challenges. COVID-19 also induced ongoing/intermittent delays and other barriers to accessing primary and specialty care at some facilities, creating research capacity issues affecting delivery of experimental interventions requiring sustained, reliable participation of clinical partners. Study designs most affected by COVID-19 were stepped-wedge (intervention/comparator changing over time), cluster (increased site variability inflating intracluster correlation), and SMART (second-stage randomizations disrupted); stratified individually-randomized trials were less vulnerable because of individual-level randomization. Conclusions PICOTS provides a framework for assessing the impact of trial disruptions in a structured manner. Given the COVID-19 experience, it is important for researchers to consider the potential impact of future trial disruptions during study planning. [ABSTRACT FROM AUTHOR]

Objective Most pragmatic trials follow the PRagmatic Explanatory Continuum Indicator Summary (PRECIS-2) criteria. The criteria specify unobtrusive measurement of participants' protocol adherence and practitioners' intervention fidelity but suggest no special monitoring strategies to assure trial integrity. We present experience with adherence/fidelity monitoring in the Pain Management Collaboratory (PMC) and provide recommendations for their monitoring in pragmatic trials to preserve inferences of treatment comparisons. Methods In November 2021, we surveyed 10 of 11 originally funded PMC pragmatic trials to determine the extent to which adherence and fidelity data were being monitored. Results Of the 10 PMC trials, 8 track adherence/fidelity. The electronic health record is the most frequent source for monitoring adherence (7/10) and fidelity (5/10). Most adherence data are used to monitor participant engagement with the trial intervention (4/10) and are reviewed by study teams (8/10) and often with a data and safety monitoring board (DSMB) (5/10). Half of the trials (5/10) reported using fidelity data for feedback/training; such data are not shared with a DSMB (0/10). Only 2 of 10 trials reported having prespecified guidance or rules around adherence/fidelity (eg, stopping rules or thresholds for corrective action, such as retraining). Conclusions As a best practice for pragmatic trials, we recommend early and regular adherence/fidelity monitoring to determine whether intervention delivery is as intended. We propose a 2-stage process with thresholds for intervening and triggers for conducting a formal futility analysis if adherence and fidelity are not maintained. The level of monitoring should be unobtrusive for both participants and those delivering the intervention; resulting data should be reviewed by an independent DSMB. [ABSTRACT FROM AUTHOR]

Background: Clostridioides difficile infection (CDI) is the most common cause of healthcare-associated infections in US hospitals with 15%-30% of patients experiencing recurrence. The aim of our randomized, double-blind clinical trial was to assess the efficacy of capsule-delivered fecal microbiota transplantation (FMT) versus placebo in reducing recurrent diarrhea and CDI recurrence. The secondary aim was FMT safety assessment., Methods: Between 2018 and 2022, Veterans across the Veterans Health Administration system with recurrent CDI who responded to antibiotic treatment were randomized in a 1:1 ratio to oral FMT or placebo capsules. Randomization was stratified by number of prior CDI recurrences (1 or ≥2). The primary endpoint was clinical recurrence by day 56, defined as >3 unformed stools daily for ≥2 days with or without laboratory confirmation of C. difficile, or death within 56 days., Results: The study was stopped due to futility after meeting pre-specified criteria. Of 153 participants (76 FMT, 77 placebo) with an average age of 66.5 years, 25 participants (32.9%) in the FMT arm and 23 (29.9%) in the placebo arm experienced the primary endpoint of diarrhea and possible or definite CDI recurrence or death within 56 days of capsule administration (absolute difference 3.0%; 95% CI [-11.7%, 17.7%]). Stratification by number of recurrences revealed no statistically significant differences. There were no clinically important differences in adverse events., Conclusions: FMT therapy vs. placebo did not reduce CDI recurrence or death at 56 days. There were no meaningful differences in adverse events between treatment groups., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2024.)