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Background: A key challenge for many critical care clinical trials is that some patients will die before their outcome is fully measured. This is referred to as "truncation due to death" and must be accounted for in both the treatment effect definition (i.e. the estimand), as well as the statistical analysis approach. It is unknown which analytic approaches to this challenge are most relevant to stakeholders., Methods: Using a modified Delphi process, we sought to identify critical attributes of analytic methods used to account for truncation due to death in critical care clinical trials. The Delphi panel included stakeholders with diverse professional or personal experience in critical care-focused clinical trials. The research team generated an initial list of attributes and associated definitions. The attribute list and definitions were refined through two Delphi rounds. Panelists ranked and scored attributes and provided open-ended rationales for responses. A consensus threshold was set as ≥ 70% of respondents rating an attribute as "Critical" (i.e., score ≥ 7 on a 9-point Likert scale) and ≤ 15% of respondents rating the measure as "Not Important" (i.e., a score of ≤ 3)., Results: Thirty-one (91%) of 34 invited individuals participated in one or both rounds. The response rate was 82% in Round 1 and 85% in Round 2. Participants included eight (26%) personal experience experts and 26 (84%) professional experience experts. After two Delphi rounds, four attributes met the criteria for consensus: accuracy (the approach will identify effects if they exist, but will not if they do not), interpretability (the approach enables a straightforward interpretation of the intervention's effect), clinical relevance (the approach can directly inform patient care), and patient-centeredness (the approach is relevant to patients and/or their families). Attributes that did not meet the consensus threshold included sensitivity, comparability, familiarity, mechanistic plausibility, and statistical simplicity., Conclusions: We found that methods used to account for truncation due to death in the treatment effect definition and statistical approach in critical care trials should meet at least four defined criteria: accuracy, interpretability, clinical relevance, and patient-centeredness. Future work is needed to derive objective criteria to quantify how well existing estimands and analytic approaches encompass these attributes., Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the University of Pennsylvania IRB (protocol # 854392). All panelists provided informed consent to participate. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests., (© 2025. The Author(s).)

Background: Clinical trials aim to draw conclusions about the effects of treatments, but a trial can address many different potential questions. For example, does the treatment work well for patients who take it as prescribed? Or does it work regardless of whether patients take it exactly as prescribed? Since different questions can lead to different conclusions on treatment benefit, it is important to clearly understand what treatment effect a trial aims to investigate-this is called the 'estimand'. Using estimands helps to ensure trials are designed and analysed to answer the questions of interest to different stakeholders, including patients and public. However, there is uncertainty about whether patients and public would like to be involved in defining estimands and how to do so. Public partners are patients and/or members of the public who are part of, or advise, the research team. We aimed to (i) co-develop a tool with public partners that helps explain what an estimand is and (ii) explore public partner's perspectives on the importance of discussing estimands during trial design., Methods: An online consultation meeting was held with 5 public partners of mixed age, gender and ethnicities, from various regions of the UK. Public partner opinions were collected and a practical tool describing estimands, drafted before the meeting by the research team, was developed. Afterwards, the tool was refined, and additional feedback sought via email., Results: Public partners want to be involved in estimand discussions. They found an introductory tool, to be presented and described to them by a researcher, helpful for starting a discussion about estimands in a trial design context. They recommended storytelling, analogies and visual aids within the tool. Four topics related to public partners' involvement in defining estimands were identified: (i) the importance of addressing questions that are relevant to patients and public in trials, (ii) involving public partners early on, (iii) a need for education and communication for all stakeholders and (iv) public partners and researchers working together., Conclusions: We co-developed a tool for researchers and public partners to use to facilitate the involvement of public partners in estimand discussions., (© 2023. The Author(s).)

Introduction: Postoperative morbidity and mortality in patients undergoing major emergency gastrointestinal surgery are a major burden on healthcare systems. Optimal management of perioperative intravenous fluids may reduce mortality rates and improve outcomes from surgery. Previous small trials of cardiac-output guided haemodynamic therapy algorithms in patients undergoing gastrointestinal surgery have suggested this intervention results in reduced complications and a modest reduction in mortality. However, this existing evidence is based mainly on elective (planned) surgery, with little evaluation in the emergency setting. There are fundamental clinical and pathophysiological differences between the planned and emergency surgical setting which may influence the effects of this intervention. A large definitive trial in emergency surgery is needed to confirm or refute the potential benefits observed in elective surgery and to inform widespread clinical practice., Methods: The FLO-ELA trial is a multi-centre, parallel-group, open, randomised controlled trial. 3138 patients aged 50 and over undergoing major emergency gastrointestinal surgery will be randomly allocated in a 1:1 ratio using minimisation to minimally invasive cardiac output monitoring to guide protocolised administration of intra-venous fluid, or usual care without cardiac output monitoring. The trial intervention will be carried out during surgery and for up to 6 h postoperatively. The trial is funded through an efficient design call by the National Institute for Health and Care Research Health Technology Assessment (NIHR HTA) programme and uses existing routinely collected datasets for the majority of data collection. The primary outcome is the number of days alive and out of hospital within 90 days of randomisation. Participants and those delivering the intervention will not be blinded to treatment allocation. Participant recruitment started in September 2017 with a 1-year internal pilot phase and is ongoing at the time of publication., Discussion: This will be the largest contemporary randomised trial examining the effectiveness of perioperative cardiac output-guided haemodynamic therapy in patients undergoing major emergency gastrointestinal surgery. The multi-centre design and broad inclusion criteria support the external validity of the trial. Although the clinical teams delivering the trial interventions will not be blinded, significant trial outcome measures are objective and not subject to detection bias., Trial Registration: ISRCTN 14729158. Registered on 02 May 2017., (© 2023. The Author(s).)

Background: Poor retention in randomised trials can lead to serious consequences to their validity. Studies within trials (SWATs) are used to identify the most effective interventions to increase retention. Many interventions could be applied at any follow-up time point, but SWATs commonly assess interventions at a single time point, which can reduce efficiency., Methods: The re-randomisation design allows participants to be re-enrolled and re-randomised whenever a new retention opportunity occurs (i.e. a new follow-up time point where the intervention could be applied). The main advantages are as follows: (a) it allows the estimation of an average effect across time points, thus increasing generalisability; (b) it can be more efficient than a parallel arm trial due to increased sample size; and (c) it allows subgroup analyses to estimate effectiveness at different time points. We present a case study where the re-randomisation design is used in a SWAT., Results: In our case study, the host trial is a dental trial with two available follow-up points. The Sticker SWAT tests whether adding the trial logo's sticker to the questionnaire's envelope will result in a higher response rate compared with not adding the sticker. The primary outcome is the response rate to postal questionnaires. The re-randomisation design could double the available sample size compared to a parallel arm trial, resulting in the ability to detect an effect size around 28% smaller., Conclusion: The re-randomisation design can increase the efficiency and generalisability of SWATs for trials with multiple follow-up time points., (© 2023. The Author(s).)

Background: Access to protocols and statistical analysis plans (SAPs) increases the transparency of randomised trial by allowing readers to identify and interpret unplanned changes to study methods, however they are often not made publicly available. We sought to determine how often study investigators would share unavailable documents upon request., Methods: We used trials from two previously identified cohorts (cohort 1: 101 trials published in high impact factor journals between January and April of 2018; cohort 2: 100 trials published in June 2018 in journals indexed in PubMed) to determine whether study investigators would share unavailable protocols/SAPs upon request. We emailed corresponding authors of trials with no publicly available protocol or SAP up to four times., Results: Overall, 96 of 201 trials (48%) across the two cohorts had no publicly available protocol or SAP (11/101 high-impact cohort, 85/100 PubMed cohort). In total, 8/96 authors (8%) shared some trial documentation (protocol only [n = 5]; protocol and SAP [n = 1]; excerpt from protocol [n = 1]; research ethics application form [n = 1]). We received protocols for 6/96 trials (6%), and a SAP for 1/96 trial (1%). Seventy-three authors (76%) did not respond, 7 authors responded (7%) but declined to share a protocol or SAP, and eight email addresses were invalid (8%). A total of 329 emails were sent (an average of 41 emails for every trial which sent documentation). After emailing authors, the total number of trials with an available protocol increased by only 3%, from 52% in to 55%., Conclusions: Most study investigators did not share their unpublished protocols or SAPs upon direct request. Alternative strategies are needed to increase transparency of randomised trials and ensure access to protocols and SAPs., (© 2022. The Author(s).)

Background: An estimand is a precise description of the treatment effect to be estimated from a trial (the question) and is distinct from the methods of statistical analysis (how the question is to be answered). The potential use of estimands to improve trial research and reporting has been underpinned by the recent publication of the ICH E9(R1) Addendum on the use of estimands in clinical trials in 2019. We set out to assess how well estimands are described in published trial protocols., Methods: We reviewed 50 trial protocols published in October 2020 in Trials and BMJ Open. For each protocol, we determined whether the estimand for the primary outcome was explicitly stated, not stated but inferable (i.e. could be constructed from the information given), or not inferable., Results: None of the 50 trials explicitly described the estimand for the primary outcome, and in 74% of trials, it was impossible to infer the estimand from the information included in the protocol. The population attribute of the estimand could not be inferred in 36% of trials, the treatment condition attribute in 20%, the population-level summary measure in 34%, and the handling of intercurrent events in 60% (the strategy for handling non-adherence was not inferable in 32% of protocols, and the strategy for handling mortality was not inferable in 80% of the protocols for which it was applicable). Conversely, the outcome attribute was stated for all trials. In 28% of trials, three or more of the five estimand attributes could not be inferred., Conclusions: The description of estimands in published trial protocols is poor, and in most trials, it is impossible to understand exactly what treatment effect is being estimated. Given the utility of estimands to improve clinical research and reporting, this urgently needs to change., (© 2021. The Author(s).)

Introduction: In a five-arm randomized clinical trial (RCT) with stratified randomization across 54 sites, we encountered low primary outcome event proportions, resulting in multiple sites with zero events either overall or in one or more study arms. In this paper, we systematically evaluated different statistical methods of accounting for center in settings with low outcome event proportions., Methods: We conducted a simulation study and a reanalysis of a completed RCT to compare five popular methods of estimating an odds ratio for multicenter trials with stratified randomization by center: (i) no center adjustment, (ii) random intercept model, (iii) Mantel-Haenszel model, (iv) generalized estimating equation (GEE) with an exchangeable correlation structure, and (v) GEE with small sample correction (GEE-small sample correction). We varied the number of total participants (200, 500, 1000, 5000), number of centers (5, 50, 100), control group outcome percentage (2%, 5%, 10%), true odds ratio (1, > 1), intra-class correlation coefficient (ICC) (0.025, 0.075), and distribution of participants across the centers (balanced, skewed)., Results: Mantel-Haenszel methods generally performed poorly in terms of power and bias and led to the exclusion of participants from the analysis because some centers had no events. Failure to account for center in the analysis generally led to lower power and type I error rates than other methods, particularly with ICC = 0.075. GEE had an inflated type I error rate except in some settings with a large number of centers. GEE-small sample correction maintained the type I error rate at the nominal level but suffered from reduced power and convergence issues in some settings when the number of centers was small. Random intercept models generally performed well in most scenarios, except with a low event rate (i.e., 2% scenario) and small total sample size (n ≤ 500), when all methods had issues., Discussion: Random intercept models generally performed best across most scenarios. GEE-small sample correction performed well when the number of centers was large. We do not recommend the use of Mantel-Haenszel, GEE, or models that do not account for center. When the expected event rate is low, we suggest that the statistical analysis plan specify an alternative method in the case of non-convergence of the primary method.

Background: Pre-specification of outcomes is an important tool to guard against outcome switching in clinical trials. However, if the outcome is not sufficiently clearly defined, then different definitions could be applied and analysed, with only the most favourable result reported., Methods: In order to assess the impact that differing outcome definitions could have on treatment effect estimates, we re-analysed data from TRIGGER, a cluster randomised trial comparing two red blood cell transfusion strategies for patients with acute upper gastrointestinal bleeding. We varied several aspects of the definition of further bleeding: (1) the criteria for what constitutes a further bleeding episode; (2) how further bleeding is assessed; and (3) the time-point at which further bleeding is measured., Results: There were marked discrepancies in the estimated odds ratios (OR) (range 0.23-0.94) and corresponding P values (range < 0.001-0.89) between different outcome definitions. At the extremes, differing outcome definitions led to markedly different conclusions; one definition led to very little evidence of a treatment effect (OR = 0.94, 95% confidence interval [CI] = 0.37-2.40, P = 0.89), while another led to very strong evidence of a treatment effect (OR = 0.23, 95% CI = 0.11-0.50, P < 0.001)., Conclusions: Outcomes should be pre-specified in sufficient detail to avoid differing definitions being analysed and only the most favourable result being reported., Trial Registration: Clinical Trials.gov, NCT02105532 . Registered on 7 April 2014.

Background: Incorrect classification of outcomes in clinical trials can lead to biased estimates of treatment effect and reduced power. Ensuring appropriate adjudication methods to minimize outcome misclassification is therefore essential. While there are many reported adjudication approaches, there is little consensus over which approach is best., Methods: Under the assumption of non-differential assessment (i.e. that misclassification rates are the same in each treatment arm, as would typically be the case when outcome assessors are blinded), we use simulation and theoretical results to address four different questions about outcome adjudication: (a) How many assessors should be used? (b) When is it better to use onsite or central assessment? (c) Should central assessors adjudicate all outcomes, or only suspected events? (d) Should central assessment with multiple assessors be done independently or through group consensus?, Results: No one adjudication approach performs optimally in all settings. The optimal approach depends on the misclassification rates of site and central assessors, and the correlation between assessors. We found: (a) there will generally be little incremental benefit to using more than three assessors and, for outcomes with very high correlation between assessors, using one assessor is sufficient; (b) when choosing between site and central assessors, the assessor with the smallest misclassification rate should be chosen; when these rates are unknown, a combination of one site assessor and two central assessors will provide good results across a range of scenarios; (c) having central assessors adjudicate only suspected events will typically increase bias, and should be avoided, unless the threshold for sending outcomes for central assessment is extremely low; (d) central assessors can adjudicate either independently or in a group, and the preferred option should be dictated by whichever is expected to have the lowest misclassification rate., Conclusions: Outcome adjudication is of critical importance to ensure validity of trial results, although no one approach is optimal across all settings. Investigators should choose the best strategy based on the specific characteristics of their trial. Regardless of the adjudication strategy chosen, assessors should be qualified and receive appropriate training.

Background: Patient recruitment in clinical trials is often challenging, and as a result, many trials are stopped early due to insufficient recruitment. The re-randomization design allows patients to be re-enrolled and re-randomized for each new treatment episode that they experience. Because it allows multiple enrollments for each patient, this design has been proposed as a way to increase the recruitment rate in clinical trials. However, it is unknown to what extent recruitment could be increased in practice., Methods: We modelled the expected recruitment rate for parallel-group and re-randomization trials in different settings based on estimates from real trials and datasets. We considered three clinical areas: in vitro fertilization, severe asthma exacerbations, and acute sickle cell pain crises. We compared the two designs in terms of the expected time to complete recruitment, and the sample size recruited over a fixed recruitment period., Results: Across the different scenarios we considered, we estimated that re-randomization could reduce the expected time to complete recruitment by between 4 and 22 months (relative reductions of 19% and 45%), or increase the sample size recruited over a fixed recruitment period by between 29% and 171%. Re-randomization can increase recruitment most for trials with a short follow-up period, a long trial recruitment duration, and patients with high rates of treatment episodes., Conclusions: Re-randomization has the potential to increase the recruitment rate in certain settings, and could lead to quicker and more efficient trials in these scenarios.

Background: The cluster randomised crossover (CRXO) design is gaining popularity in trial settings where individual randomisation or parallel group cluster randomisation is not feasible or practical. Our aim is to stimulate discussion on the content of a reporting guideline for CRXO trials and to assess the reporting quality of published CRXO trials., Methods: We undertook a systematic review of CRXO trials. Searches of MEDLINE, EMBASE, and CINAHL Plus as well as citation searches of CRXO methodological articles were conducted to December 2014. Reporting quality was assessed against both modified items from 2010 CONSORT and 2012 cluster trials extension and other proposed quality measures., Results: Of the 3425 records identified through database searching, 83 trials met the inclusion criteria. Trials were infrequently identified as "cluster randomis(z)ed crossover" in title (n = 7, 8%) or abstract (n = 21, 25%), and a rationale for the design was infrequently provided (n = 20, 24%). Design parameters such as the number of clusters and number of periods were well reported. Discussion of carryover took place in only 17 trials (20%). Sample size methods were only reported in 58% (n = 48) of trials. A range of approaches were used to report baseline characteristics. The analysis method was not adequately reported in 23% (n = 19) of trials. The observed within-cluster within-period intracluster correlation and within-cluster between-period intracluster correlation for the primary outcome data were not reported in any trial. The potential for selection, performance, and detection bias could be evaluated in 30%, 81%, and 70% of trials, respectively., Conclusions: There is a clear need to improve the quality of reporting in CRXO trials. Given the unique features of a CRXO trial, it is important to develop a CONSORT extension. Consensus amongst trialists on the content of such a guideline is essential.

Background: Cluster randomised trials (CRTs) are commonly analysed using mixed-effects models or generalised estimating equations (GEEs). However, these analyses do not always perform well with the small number of clusters typical of most CRTs. They can lead to increased risk of a type I error (finding a statistically significant treatment effect when it does not exist) if appropriate corrections are not used., Methods: We conducted a small simulation study to evaluate the impact of using small-sample corrections for mixed-effects models or GEEs in CRTs with a small number of clusters. We then reanalysed data from TRIGGER, a CRT with six clusters, to determine the effect of using an inappropriate analysis method in practice. Finally, we reviewed 100 CRTs previously identified by a search on PubMed in order to assess whether trials were using appropriate methods of analysis. Trials were classified as at risk of an increased type I error rate if they did not report using an analysis method which accounted for clustering, or if they had fewer than 40 clusters and performed an individual-level analysis without reporting the use of an appropriate small-sample correction., Results: Our simulation study found that using mixed-effects models or GEEs without an appropriate correction led to inflated type I error rates, even for as many as 70 clusters. Conversely, using small-sample corrections provided correct type I error rates across all scenarios. Reanalysis of the TRIGGER trial found that inappropriate methods of analysis gave much smaller P values (P ≤ 0.01) than appropriate methods (P = 0.04-0.15). In our review, of the 99 trials that reported the number of clusters, 64 (65 %) were at risk of an increased type I error rate; 14 trials did not report using an analysis method which accounted for clustering, and 50 trials with fewer than 40 clusters performed an individual-level analysis without reporting the use of an appropriate correction., Conclusions: CRTs with a small or medium number of clusters are at risk of an inflated type I error rate unless appropriate analysis methods are used. Investigators should consider using small-sample corrections with mixed-effects models or GEEs to ensure valid results.

Background: Malignant pleural effusions (MPEs) remain a common problem, with 40,000 new cases in the United Kingdom each year and up to 250,000 in the United States. Traditional management of MPE usually involves an inpatient stay with placement of a chest drain, followed by the instillation of a pleural sclerosing agent such as talc, which aims to minimise further fluid build-up. Despite a good success rate in studies, this approach can be expensive, time-consuming and inconvenient for patients. More recently, an alternative method has become available in the form of indwelling pleural catheters (IPCs), which can be inserted and managed in an outpatient setting. It is currently unknown whether combining talc pleurodesis with IPCs will provide improved pleural symphysis rates over those of IPCs alone., Methods/design: IPC-PLUS is a patient-blind, multicentre randomised controlled trial (RCT) comparing the combination of talc with an IPC to the use of an IPC alone for inducing pleurodesis in MPEs. The primary outcome is successful pleurodesis at five weeks post-randomisation. This study will recruit 154 patients, with an interim analysis for efficacy after 100 patients, and aims to help to define the future gold standard for outpatient management of patients with symptomatic MPEs., Discussion: IPC-PLUS is the first RCT to examine the practicality and utility of talc administered via an IPC. The study remains in active recruitment and has the potential to significantly alter how patients requiring pleurodesis for MPE are approached in the future., Trial Registration: This trial was registered with Current Controlled Trials (identifier: ISRCTN73255764 ) on 23 August 2012.

Background: Adjustment for prognostic covariates can lead to increased power in the analysis of randomized trials. However, adjusted analyses are not often performed in practice., Methods: We used simulation to examine the impact of covariate adjustment on 12 outcomes from 8 studies across a range of therapeutic areas. We assessed (1) how large an increase in power can be expected in practice; and (2) the impact of adjustment for covariates that are not prognostic., Results: Adjustment for known prognostic covariates led to large increases in power for most outcomes. When power was set to 80% based on an unadjusted analysis, covariate adjustment led to a median increase in power to 92.6% across the 12 outcomes (range 80.6 to 99.4%). Power was increased to over 85% for 8 of 12 outcomes, and to over 95% for 5 of 12 outcomes. Conversely, the largest decrease in power from adjustment for covariates that were not prognostic was from 80% to 78.5%., Conclusions: Adjustment for known prognostic covariates can lead to substantial increases in power, and should be routinely incorporated into the analysis of randomized trials. The potential benefits of adjusting for a small number of possibly prognostic covariates in trials with moderate or large sample sizes far outweigh the risks of doing so, and so should also be considered.

Background: The Coping with Persistent Pain, Effectiveness Research into Self-management (COPERS) trial assessed whether a group-based self-management course is effective in reducing pain-related disability in participants with chronic musculoskeletal pain. This article describes the statistical analysis plan for the COPERS trial., Methods and Design: COPERS was a pragmatic, multicentre, unmasked, parallel group, randomised controlled trial. This article describes (a) the overall analysis principles (including which participants will be included in each analysis, how results will be presented, which covariates will be adjusted for, and how we will account for clustering in the intervention group); (b) the primary and secondary outcomes, and how each outcome will be analysed; (c) sensitivity analyses; (d) subgroup analyses; and (e) adherence-adjusted analyses., Trial Registration: ISRCTN24426731.

Background: Previous research has suggested an association between more liberal red blood cell (RBC) transfusion and greater risk of further bleeding and mortality following acute upper gastrointestinal bleeding (AUGIB)., Methods and Design: The Transfusion in Gastrointestinal Bleeding (TRIGGER) trial is a pragmatic cluster-randomised feasibility trial which aims to evaluate the feasibility of implementing a restrictive vs. liberal RBC transfusion policy for adult patients admitted to hospital with AUGIB in the UK. This trial will help to inform the design and methodology of a phase III trial. The protocol for TRIGGER has been published in Transfusion Medicine Reviews. Recruitment began in September 2012 and was completed in March 2013. This update presents the statistical analysis plan, detailing how analysis of the TRIGGER trial will be performed. It is hoped that prospective publication of the full statistical analysis plan will increase transparency and give readers a clear overview of how TRIGGER will be analysed., Trial Registration: ISRCTN85757829.