Search

Context: Malignant pleural effusion causes disabling dyspnea in patients with a short life expectancy. Palliation is achieved by fluid drainage, but the most effective first-line method has not been determined.Objective: To determine whether indwelling pleural catheters (IPCs) are more effective than chest tube and talc slurry pleurodesis (talc) at relieving dyspnea.Design: Unblinded randomized controlled trial (Second Therapeutic Intervention in Malignant Effusion Trial [TIME2]) comparing IPC and talc (1:1) for which 106 patients with malignant pleural effusion who had not previously undergone pleurodesis were recruited from 143 patients who were treated at 7 UK hospitals. Patients were screened from April 2007-February 2011 and were followed up for a year.Intervention: Indwelling pleural catheters were inserted on an outpatient basis, followed by initial large volume drainage, education, and subsequent home drainage. The talc group were admitted for chest tube insertion and talc for slurry pleurodesis.Main Outcome Measure: Patients completed daily 100-mm line visual analog scale (VAS) of dyspnea over 42 days after undergoing the intervention (0 mm represents no dyspnea and 100 mm represents maximum dyspnea; 10 mm represents minimum clinically significant difference). Mean difference was analyzed using a mixed-effects linear regression model adjusted for minimization variables.Results: Dyspnea improved in both groups, with no significant difference in the first 42 days with a mean VAS dyspnea score of 24.7 in the IPC group (95% CI, 19.3-30.1 mm) and 24.4 mm (95% CI, 19.4-29.4 mm) in the talc group, with a difference of 0.16 mm (95% CI, −6.82 to 7.15; P = .96). There was a statistically significant improvement in dyspnea in the IPC group at 6 months, with a mean difference in VAS score between the IPC group and the talc group of −14.0 mm (95% CI, −25.2 to −2.8 mm; P = .01). Length of initial hospitalization was significantly shorter in the IPC group with a median of 0 days (interquartile range [IQR], 0-1 day) and 4 days (IQR, 2-6 days) for the talc group, with a difference of −3.5 days (95% CI, −4.8 to −1.5 days; P < .001). There was no significant difference in quality of life. Twelve patients (22%) in the talc group required further pleural procedures compared with 3 (6%) in the IPC group (odds ratio [OR], 0.21; 95% CI, 0.04-0.86; P = .03). Twenty-one of the 52 patients in the catheter group experienced adverse events vs 7 of 54 in the talc group (OR, 4.70; 95% CI, 1.75-12.60; P = .002).Conclusion: Among patients with malignant pleural effusion and no previous pleurodesis, there was no significant difference between IPCs and talc pleurodesis at relieving patient-reported dyspnea.Trial Registration: isrctn.org Identifier: ISRCTN87514420. [ABSTRACT FROM AUTHOR]

A common concern in non-inferiority (NI) trials is that non-adherence due, for example, to poor study conduct can make treatment arms artificially similar. Because intention-to-treat analyses can be anti-conservative in this situation, per-protocol analyses are sometimes recommended. However, such advice does not consider the estimands framework, nor the risk of bias from per-protocol analyses. We therefore sought to update the above guidance using the estimands framework, and compare estimators to improve on the performance of per-protocol analyses. We argue the main threat to validity of NI trials is the occurrence of "trial-specific" intercurrent events (IEs), that is, IEs which occur in a trial setting, but would not occur in practice. To guard against erroneous conclusions of non-inferiority, we suggest an estimand using a hypothetical strategy for trial-specific IEs should be employed, with handling of other non-trial-specific IEs chosen based on clinical considerations. We provide an overview of estimators that could be used to estimate a hypothetical estimand, including inverse probability weighting (IPW), and two instrumental variable approaches (one using an informative Bayesian prior on the effect of standard treatment, and one using a treatment-by-covariate interaction as an instrument). We compare them, using simulation in the setting of all-or-nothing compliance in two active treatment arms, and conclude both IPW and the instrumental variable method using a Bayesian prior are potentially useful approaches, with the choice between them depending on which assumptions are most plausible for a given trial., (© 2025 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: Extensions to Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) and Consolidated Standards of Reporting Trials (CONSORT) reporting recommendations specifically for factorial trials have been developed by the Reporting Factorial Trials (RAFT) study group. This article describes the processes and methods used to develop the extensions., Objective: To develop SPIRIT and CONSORT extensions for factorial trials., Design and Participants: A four-phase, consensus-based approach was used: phase 1: scoping review, phase 2: Delphi survey (n=104 respondents in round 1), phase 3: consensus meeting (n=15 members) and phase 4: checklist finalisation., Results: In phase 1, the scoping review identified 31 reporting recommendations, which formed a long list of 50 concepts (19 applied to the SPIRIT extension and 31 applied to the CONSORT extension) to include in the guideline development. In phase 2, a three-round Delphi survey resulted in two new concepts being added and ended with 49 concepts (19 applied to SPIRIT and 30 applied to CONSORT) reaching consensus to remain, with only three concepts meeting the exclusion criteria. In phase 3, the concepts were further refined and translated into specific extension item wording, through an extensive review process conducted by the core RAFT team and leading trial experts, who attended a 2-day hybrid meeting. The resulting 9 SPIRIT items and 17 CONSORT items were further evaluated and developed through an iterative process in phase 4, to promote user acceptance and uptake., Conclusion: Uptake of the CONSORT and SPIRIT extensions will improve the conduct of factorial trials, as well as understanding and interpretation of such trials. By reporting on how these extensions were developed, we promote transparency of this process and share learning experiences to develop best practice when developing reporting guidelines., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY. Published by BMJ Group.)

Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: support from the MRC for the submitted work. SH and A-WC are members of the SPIRIT-CONSORT executive group and leading the current update of the SPIRIT 2013 and CONSORT 2010 reporting guidelines funded by the UK MRC National Institute for Health Research (NIHR) Better Methods, Better Research (MR/W020483/1). NI is paid to work as a research editor for The BMJ; during the course of the project, NI received funding from the UK Office for National Statistics (ONS) and UK NIHR unrelated to the submitted work. JF is paid to work as an associate editor for The BMJ. No other disclosures were reported. JF and NI worked for The BMJ during guideline development, but were not involved in any of the decisions regarding review of the manuscript or its acceptance. This article reflects the views of the authors, the Delphi panellists, and the consensus meeting panellists and may not represent the views of the broader stakeholder groups, the authors’ institutions, or other affiliations.

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).)

Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support for the consensus meeting and for some authors from research funding organisations in the funding statement above; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Estimands can help clarify the interpretation of treatment effects and ensure that estimators are aligned with the study's objectives. Cluster-randomised trials require additional attributes to be defined within the estimand compared to individually randomised trials, including whether treatment effects are marginal or cluster-specific , and whether they are participant- or cluster-average . In this paper, we provide formal definitions of estimands encompassing both these attributes using potential outcomes notation and describe differences between them. We then provide an overview of estimators for each estimand, describe their assumptions, and show consistency (i.e. asymptotically unbiased estimation) for a series of analyses based on cluster-level summaries. Then, through a re-analysis of a published cluster-randomised trial, we demonstrate that the choice of both estimand and estimator can affect interpretation. For instance, the estimated odds ratio ranged from 1.38 ( p  = 0.17) to 1.83 ( p  = 0.03) depending on the target estimand, and for some estimands, the choice of estimator affected the conclusions by leading to smaller treatment effect estimates. We conclude that careful specification of the estimand, along with an appropriate choice of estimator, is essential to ensuring that cluster-randomised trials address the right question., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: After an initial recommendation from the World Health Organisation, trials of patients hospitalised with COVID-19 often include an ordinal clinical status outcome, which comprises a series of ordered categorical variables, typically ranging from 'Alive and discharged from hospital' to 'Dead'. These ordinal outcomes are often analysed using a proportional odds model, which provides a common odds ratio as an overall measure of effect, which is generally interpreted as the odds ratio for being in a higher category. The common odds ratio relies on the assumption of proportional odds, which implies an identical odds ratio across all ordinal categories; however, there is generally no statistical or biological basis for which this assumption should hold; and when violated, the common odds ratio may be a biased representation of the odds ratios for particular categories within the ordinal outcome. In this study, we aimed to evaluate to what extent the common odds ratio in published COVID-19 trials differed to simple binary odds ratios for clinically important outcomes., Methods: We conducted a systematic review of randomised trials evaluating interventions for patients hospitalised with COVID-19, which used a proportional odds model to analyse an ordinal clinical status outcome, published between January 2020 and May 2021. We assessed agreement between the common odds ratio and the odds ratio from a standard logistic regression model for three clinically important binary outcomes: 'Alive', 'Alive without mechanical ventilation', and 'Alive and discharged from hospital'., Results: Sixteen randomised clinical trials, comprising 38 individual comparisons, were included in this study; of these, only 6 trials (38%) formally assessed the proportional odds assumption. The common odds ratio differed by more than 25% compared to the binary odds ratios in 55% of comparisons for the outcome 'Alive', 37% for 'Alive without mechanical ventilation', and 24% for 'Alive and discharged from hospital'. In addition, the common odds ratio systematically underestimated the odds ratio for the outcome 'Alive' by -16.8% (95% confidence interval: -28.7% to -2.9%, p  = 0.02), though differences for the other outcomes were smaller and not statistically significant (-8.4% for 'Alive without mechanical ventilation' and 3.6% for 'Alive and discharged from hospital'). The common odds ratio was statistically significant for 18% of comparisons, while the binary odds ratio was significant in 5%, 16%, and 3% of comparisons for the outcomes 'Alive', 'Alive without mechanical ventilation', and 'Alive and discharged from hospital', respectively., Conclusion: The common odds ratio from proportional odds models often differs substantially to odds ratios from clinically important binary outcomes, and similar to composite outcomes, a beneficial common OR from a proportional odds model does not necessarily indicate a beneficial effect on the most important categories within the ordinal outcome., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

To obtain valid inference following stratified randomisation, treatment effects should be estimated with adjustment for stratification variables. Stratification sometimes requires categorisation of a continuous prognostic variable (eg, age), which raises the question: should adjustment be based on randomisation categories or underlying continuous values? In practice, adjustment for randomisation categories is more common. We reviewed trials published in general medical journals and found none of the 32 trials that stratified randomisation based on a continuous variable adjusted for continuous values in the primary analysis. Using data simulation, this article evaluates the performance of different adjustment strategies for continuous and binary outcomes where the covariate-outcome relationship (via the link function) was either linear or non-linear. Given the utility of covariate adjustment for addressing missing data, we also considered settings with complete or missing outcome data. Analysis methods included linear or logistic regression with no adjustment for the stratification variable, adjustment for randomisation categories, or adjustment for continuous values assuming a linear covariate-outcome relationship or allowing for non-linearity using fractional polynomials or restricted cubic splines. Unadjusted analysis performed poorly throughout. Adjustment approaches that misspecified the underlying covariate-outcome relationship were less powerful and, alarmingly, biased in settings where the stratification variable predicted missing outcome data. Adjustment for randomisation categories tends to involve the highest degree of misspecification, and so should be avoided in practice. To guard against misspecification, we recommend use of flexible approaches such as fractional polynomials and restricted cubic splines when adjusting for continuous stratification variables in randomised trials., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: support from the UK Medical Research Council and the NIHR for the submitted work. BCK and SC declare grant funding (payable to employing institutions) from the MRC-NIHR Trials Methodology Research Partnership. BCK and ME declare grant funding (payable to employing institutions) from the NIHR for the FLO-ELA trial. TPM declares consultancy fees from Bayer Healthcare Pharmaceuticals, Alliance Pharmaceuticals, Gilead Sciences, and Kite Pharma; declares conference attendance and travel paid for as an invited speaker at the 2023 European Society for Blood and Marrow Transplantation conference; and is an independent member of the data and safety monitoring board for the FLO-ELA trial. All other authors declare no conflicts of interest.

Importance: Transparent reporting of randomized trials is essential to facilitate critical appraisal and interpretation of results. Factorial trials, in which 2 or more interventions are assessed in the same set of participants, have unique methodological considerations. However, reporting of factorial trials is suboptimal., Objective: To develop a consensus-based extension to the Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement for factorial trials., Design: Using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework, the CONSORT extension for factorial trials was developed by (1) generating a list of reporting recommendations for factorial trials using a scoping review of methodological articles identified using a MEDLINE search (from inception to May 2019) and supplemented with relevant articles from the personal collections of the authors; (2) a 3-round Delphi survey between January and June 2022 to identify additional items and assess the importance of each item, completed by 104 panelists from 14 countries; and (3) a hybrid consensus meeting attended by 15 panelists to finalize the selection and wording of items for the checklist., Findings: This CONSORT extension for factorial trials modifies 16 of the 37 items in the CONSORT 2010 checklist and adds 1 new item. The rationale for the importance of each item is provided. Key recommendations are (1) the reason for using a factorial design should be reported, including whether an interaction is hypothesized, (2) the treatment groups that form the main comparisons should be clearly identified, and (3) for each main comparison, the estimated interaction effect and its precision should be reported., Conclusions and Relevance: This extension of the CONSORT 2010 Statement provides guidance on the reporting of factorial randomized trials and should facilitate greater understanding of and transparency in their reporting.

Background: Recent work has shown that cluster-randomised trials can estimate two distinct estimands: the participant-average and cluster-average treatment effects. These can differ when participant outcomes or the treatment effect depends on the cluster size (termed informative cluster size). In this case, estimators that target one estimand (such as the analysis of unweighted cluster-level summaries, which targets the cluster-average effect) may be biased for the other. Furthermore, commonly used estimators such as mixed-effects models or generalised estimating equations with an exchangeable correlation structure can be biased for both estimands. However, there has been little empirical research into whether informative cluster size is likely to occur in practice., Method: We re-analysed a cluster-randomised trial comparing two different thresholds for red blood cell transfusion in patients with acute upper gastrointestinal bleeding to explore whether estimates for the participant- and cluster-average effects differed, to provide empirical evidence for whether informative cluster size may be present. For each outcome, we first estimated a participant-average effect using independence estimating equations, which are unbiased under informative cluster size. We then compared this to two further methods: (1) a cluster-average effect estimated using either weighted independence estimating equations or unweighted cluster-level summaries, and (2) estimates from a mixed-effects model or generalised estimating equations with an exchangeable correlation structure. We then performed a small simulation study to evaluate whether observed differences between cluster- and participant-average estimates were likely to occur even if no informative cluster size was present., Results: For most outcomes, treatment effect estimates from different methods were similar. However, differences of >10% occurred between participant- and cluster-average estimates for 5 of 17 outcomes (29%). We also observed several notable differences between estimates from mixed-effects models or generalised estimating equations with an exchangeable correlation structure and those based on independence estimating equations. For example, for the EQ-5D VAS score, the independence estimating equation estimate of the participant-average difference was 4.15 (95% confidence interval: -3.37 to 11.66), compared with 2.84 (95% confidence interval: -7.37 to 13.04) for the cluster-average independence estimating equation estimate, and 3.23 (95% confidence interval: -6.70 to 13.16) from a mixed-effects model. Similarly, for thromboembolic/ischaemic events, the independence estimating equation estimate for the participant-average odds ratio was 0.43 (95% confidence interval: 0.07 to 2.48), compared with 0.33 (95% confidence interval: 0.06 to 1.77) from the cluster-average estimator., Conclusion: In this re-analysis, we found that estimates from the various approaches could differ, which may be due to the presence of informative cluster size. Careful consideration of the estimand and the plausibility of assumptions underpinning each estimator can help ensure an appropriate analysis methods are used. Independence estimating equations and the analysis of cluster-level summaries (with appropriate weighting for each to correspond to either the participant-average or cluster-average treatment effect) are a desirable choice when informative cluster size is deemed possible, due to their unbiasedness in this setting., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Importance: Trial protocols outline a trial's objectives as well as the methods (design, conduct, and analysis) that will be used to meet those objectives, and transparent reporting of trial protocols ensures objectives are clear and facilitates appraisal regarding the suitability of study methods. Factorial trials, in which 2 or more interventions are assessed in the same set of participants, have unique methodological considerations. However, no extension of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Statement, which provides guidance on reporting of trial protocols, for factorial trials is available., Objective: To develop a consensus-based extension to the SPIRIT 2013 Statement for factorial trials., Evidence Review: The SPIRIT extension for factorial trials was developed using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework. First, a list of reporting recommendations was generated using a scoping review of methodological articles identified using a MEDLINE search (inception to May 2019), which was supplemented with relevant articles from the personal collections of the authors. Second, a 3-round Delphi survey (January to June 2022, completed by 104 panelists from 14 countries) was conducted to assess the importance of items and identify additional recommendations. Third, a hybrid consensus meeting was held, attended by 15 panelists to finalize selection and wording of the checklist., Findings: This SPIRIT extension for factorial trials modified 9 of the 33 items in the SPIRIT 2013 checklist. Key reporting recommendations were that the rationale for using a factorial design should be provided, including whether an interaction is hypothesized; the treatment groups that will form the main comparisons should be identified; and statistical methods for each main comparison should be provided, including how interactions will be assessed., Conclusions and Relevance: In this consensus statement, 9 factorial-specific items were provided that should be addressed in all protocols of factorial trials to increase the trial's utility and transparency.

Many trials use stratified randomisation, where participants are randomised within strata defined by one or more baseline covariates. While it is important to adjust for stratification variables in the analysis, the appropriate method of adjustment is unclear when stratification variables are affected by misclassification and hence some participants are randomised in the incorrect stratum. We conducted a simulation study to compare methods of adjusting for stratification variables affected by misclassification in the analysis of continuous outcomes when all or only some stratification errors are discovered, and when the treatment effect or treatment-by-covariate interaction effect is of interest. The data were analysed using linear regression with no adjustment, adjustment for the strata used to perform the randomisation (randomisation strata), adjustment for the strata if all errors are corrected (true strata), and adjustment for the strata after some errors are discovered and corrected (updated strata). The unadjusted model performed poorly in all settings. Adjusting for the true strata was optimal, while the relative performance of adjusting for the randomisation strata or the updated strata varied depending on the setting. As the true strata are unlikely to be known with certainty in practice, we recommend using the updated strata for adjustment and performing subgroup analyses, provided the discovery of errors is unlikely to depend on treatment group, as expected in blinded trials. Greater transparency is needed in the reporting of stratification errors and how they were addressed in the analysis., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

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).)

Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most authors do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is challenging, because many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings in which patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Background: A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. For example, in a double-blind drug trial, some participants may not receive any dose of study medication. Many trials use a 'modified intention-to-treat' approach, whereby participants who do not initiate treatment are excluded from the analysis. However, it is not clear (a) the estimand being targeted by such an approach and (b) the assumptions necessary for such an approach to be unbiased., Methods: Using potential outcome notation, we demonstrate that a modified intention-to-treat analysis which excludes participants who do not begin treatment is estimating a principal stratum estimand (i.e. the treatment effect in the subpopulation of participants who would begin treatment, regardless of which arm they were assigned to). The modified intention-to-treat estimator is unbiased for the principal stratum estimand under the assumption that the intercurrent event is not affected by the assigned treatment arm, that is, participants who initiate treatment in one arm would also do so in the other arm (i.e. if someone began the intervention, they would also have begun the control, and vice versa)., Results: We identify two key criteria in determining whether the modified intention-to-treat estimator is likely to be unbiased: first, we must be able to measure the participants in each treatment arm who experience the intercurrent event, and second, the assumption that treatment allocation will not affect whether the participant begins treatment must be reasonable. Most double-blind trials will satisfy these criteria, as the decision to start treatment cannot be influenced by the allocation, and we provide an example of an open-label trial where these criteria are likely to be satisfied as well, implying that a modified intention-to-treat analysis which excludes participants who do not begin treatment is an unbiased estimator for the principal stratum effect in these settings. We also give two examples where these criteria will not be satisfied (one comparing an active intervention vs usual care, where we cannot identify which usual care participants would have initiated the active intervention, and another comparing two active interventions in an unblinded manner, where knowledge of the assigned treatment arm may affect the participant's choice to begin or not), implying that a modified intention-to-treat estimator will be biased in these settings., Conclusion: A modified intention-to-treat analysis which excludes participants who do not begin treatment can be an unbiased estimator for the principal stratum estimand. Our framework can help identify when the assumptions for unbiasedness are likely to hold, and thus whether modified intention-to-treat is appropriate or not.

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: Cluster-randomized trials (CRTs) involve randomizing groups of individuals (e.g. hospitals, schools or villages) to different interventions. Various approaches exist for analysing CRTs but there has been little discussion around the treatment effects (estimands) targeted by each., Methods: We describe the different estimands that can be addressed through CRTs and demonstrate how choices between different analytic approaches can impact the interpretation of results by fundamentally changing the question being asked, or, equivalently, the target estimand., Results: CRTs can address either the participant-average treatment effect (the average treatment effect across participants) or the cluster-average treatment effect (the average treatment effect across clusters). These two estimands can differ when participant outcomes or the treatment effect depends on the cluster size (referred to as 'informative cluster size'), which can occur for reasons such as differences in staffing levels or types of participants between small and large clusters. Furthermore, common estimators, such as mixed-effects models or generalized estimating equations with an exchangeable working correlation structure, can produce biased estimates for both the participant-average and cluster-average treatment effects when cluster size is informative. We describe alternative estimators (independence estimating equations and cluster-level analyses) that are unbiased for CRTs even when informative cluster size is present., Conclusion: We conclude that careful specification of the estimand at the outset can ensure that the study question being addressed is clear and relevant, and, in turn, that the selected estimator provides an unbiased estimate of the desired quantity., (© The Author(s) 2022. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background/aims: Tuberculosis remains one of the leading causes of death from an infectious disease globally. Both choices of outcome definitions and approaches to handling events happening post-randomisation can change the treatment effect being estimated, but these are often inconsistently described, thus inhibiting clear interpretation and comparison across trials., Methods: Starting from the ICH E9(R1) addendum's definition of an estimand, we use our experience of conducting large Phase III tuberculosis treatment trials and our understanding of the estimand framework to identify the key decisions regarding how different event types are handled in the primary outcome definition, and the important points that should be considered in making such decisions. A key issue is the handling of intercurrent (i.e. post-randomisation) events (ICEs) which affect interpretation of or preclude measurement of the intended final outcome. We consider common ICEs including treatment changes and treatment extension, poor adherence to randomised treatment, re-infection with a new strain of tuberculosis which is different from the original infection, and death. We use two completed tuberculosis trials (REMoxTB and STREAM Stage 1) as illustrative examples. These trials tested non-inferiority of new tuberculosis treatment regimens versus a control regimen. The primary outcome was a binary composite endpoint, 'favourable' or 'unfavourable', which was constructed from several components., Results: We propose the following improvements in handling the above-mentioned ICEs and loss to follow-up (a post-randomisation event that is not in itself an ICE). First, changes to allocated regimens should not necessarily be viewed as an unfavourable outcome; from the patient perspective, the potential harms associated with a change in the regimen should instead be directly quantified. Second, handling poor adherence to randomised treatment using a per-protocol analysis does not necessarily target a clear estimand; instead, it would be desirable to develop ways to estimate the treatment effects more relevant to programmatic settings. Third, re-infection with a new strain of tuberculosis could be handled with different strategies, depending on whether the outcome of interest is the ability to attain culture negativity from infection with any strain of tuberculosis, or specifically the presenting strain of tuberculosis. Fourth, where possible, death could be separated into tuberculosis-related and non-tuberculosis-related and handled using appropriate strategies. Finally, although some losses to follow-up would result in early treatment discontinuation, patients lost to follow-up before the end of the trial should not always be classified as having an unfavourable outcome. Instead, loss to follow-up should be separated from not completing the treatment, which is an ICE and may be considered as an unfavourable outcome., Conclusion: The estimand framework clarifies many issues in tuberculosis trials but also challenges trialists to justify and improve their outcome definitions. Future trialists should consider all the above points in defining their outcomes.

Factorial trials offer an efficient method to evaluate multiple interventions in a single trial, however the use of additional treatments can obscure research objectives, leading to inappropriate analytical methods and interpretation of results. We define a set of estimands for factorial trials, and describe a framework for applying these estimands, with the aim of clarifying trial objectives and ensuring appropriate primary and sensitivity analyses are chosen. This framework is intended for use in factorial trials where the intent is to conduct "two-trials-in-one" (ie, to separately evaluate the effects of treatments A and B), and is comprised of four steps: (i) specifying how additional treatment(s) (eg, treatment B) will be handled in the estimand, and how intercurrent events affecting the additional treatment(s) will be handled; (ii) designating the appropriate factorial estimator as the primary analysis strategy; (iii) evaluating the interaction to assess the plausibility of the assumptions underpinning the factorial estimator; and (iv) performing a sensitivity analysis using an appropriate multiarm estimator to evaluate to what extent departures from the underlying assumption of no interaction may affect results. We show that adjustment for other factors is necessary for noncollapsible effect measures (such as odds ratio), and through a trial re-analysis we find that failure to consider the estimand could lead to inappropriate interpretation of results. We conclude that careful use of the estimands framework clarifies research objectives and reduces the risk of misinterpretation of trial results, and should become a standard part of both the protocol and reporting of factorial trials., (© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Objectives: To evaluate how often the precise research question being addressed about an intervention (the estimand) is stated or can be determined from reported methods, and to identify what types of questions are being investigated in phase 2-4 randomised trials., Design: Systematic review of the clarity of research questions being investigated in randomised trials in 2020 in six leading general medical journals., Data Source: PubMed search in February 2021., Eligibility Criteria for Selecting Studies: Phase 2-4 randomised trials, with no restrictions on medical conditions or interventions. Cluster randomised, crossover, non-inferiority, and equivalence trials were excluded., Main Outcome Measures: Number of trials that stated the precise primary question being addressed about an intervention (ie, the primary estimand), or for which the primary estimand could be determined unambiguously from the reported methods using statistical knowledge. Strategies used to handle post-randomisation events that affect the interpretation or existence of patient outcomes, such as intervention discontinuations or uses of additional drug treatments (known as intercurrent events), and the corresponding types of questions being investigated., Results: 255 eligible randomised trials were identified. No trials clearly stated all the attributes of the estimand. In 117 (46%) of 255 trials, the primary estimand could be determined from the reported methods. Intercurrent events were reported in 242 (95%) of 255 trials; but the handling of these could only be determined in 125 (49%) of 255 trials. Most trials that provided this information considered the occurrence of intercurrent events as irrelevant in the calculation of the treatment effect and assessed the effect of the intervention regardless (96/125, 77%)-that is, they used a treatment policy strategy. Four (4%) of 99 trials with treatment non-adherence owing to adverse events estimated the treatment effect in a hypothetical setting (ie, the effect as if participants continued treatment despite adverse events), and 19 (79%) of 24 trials where some patients died estimated the treatment effect in a hypothetical setting (ie, the effect as if participants did not die)., Conclusions: The precise research question being investigated in most trials is unclear, mainly because of a lack of clarity on the approach to handling intercurrent events. Clear reporting of estimands is necessary in trial reports so that all stakeholders, including clinicians, patients and policy makers, can make fully informed decisions about medical interventions., Systematic Review Registration: PROSPERO CRD42021238053., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: SC had financial support from the NIHR for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.)

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: Factorial designs and multi-arm multi-stage (MAMS) platform designs have many advantages, but the practical advantages and disadvantages of combining the two designs have not been explored., Methods: We propose practical methods for a combined design within the platform trial paradigm where some interventions are not expected to interact and could be given together., Results: We describe the combined design and suggest diagrams that can be used to represent it. Many properties are common both to standard factorial designs, including the need to consider interactions between interventions and the impact of intervention efficacy on power of other comparisons, and to standard multi-arm multi-stage designs, including the need to pre-specify procedures for starting and stopping intervention comparisons. We also identify some specific features of the factorial-MAMS design: timing of interim and final analyses should be determined by calendar time or total observed events; some non-factorial modifications may be useful; eligibility criteria should be broad enough to include any patient eligible for any part of the randomisation; stratified randomisation may conveniently be performed sequentially; and analysis requires special care to use only concurrent controls., Conclusion: A combined factorial-MAMS design can combine the efficiencies of factorial trials and multi-arm multi-stage platform trials. It allows us to address multiple research questions under one protocol and to test multiple new treatment options, which is particularly important when facing a new emergent infection such as COVID-19.

Often patients may require treatment on multiple occasions. The re-randomisation design can be used in such multi-episode settings, as it allows patients to be re-enrolled and re-randomised for each new treatment episode they experience. We propose a set of estimands that can be used in multi-episode settings, focusing on issues unique to multi-episode settings, namely how each episode should be weighted, how the patient's treatment history in previous episodes should be handled, and whether episode-specific effects or average effects across all episodes should be used. We then propose independence estimators for each estimand, and show the manner in which many re-randomisation trials have been analysed in the past (a simple comparison between all intervention episodes vs. all control episodes) corresponds to a per-episode added-benefit estimand, that is, the average effect of the intervention across all episodes, over and above any benefit conferred from the intervention in previous episodes. We show this estimator is generally unbiased, and describe when other estimators will be unbiased. We conclude that (i) consideration of these estimands can help guide the choice of which analysis method is most appropriate; and (ii) the re-randomisation design with an independence estimator can be a useful approach in multi-episode settings.

Precise specification of the research question and associated treatment effect of interest is essential in clinical research, yet recent work shows that they are often incompletely specified. The ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials introduces a framework that supports researchers in precisely and transparently specifying the treatment effect they aim to estimate in their clinical trial. In this paper, we present practical examples to demonstrate to all researchers involved in clinical trials how estimands can help them to specify the research question, lead to a better understanding of the treatment effect to be estimated and hence increase the probability of success of the trial., Competing Interests: Competing interests: We have read and understood the BMJ Group policy on declaration of interests and declare the following interests: TPC and AP are employed by the clinical research organisation ICON., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Composite endpoints are commonly used to define primary outcomes in randomized controlled trials. A participant may be classified as meeting the endpoint if they experience an event in one or several components (eg, a favorable outcome based on a composite of being alive and attaining negative culture results in trials assessing tuberculosis treatments). Partially observed components that are not missing simultaneously complicate the analysis of the composite endpoint. An intuitive strategy frequently used in practice for handling missing values in the components is to derive the values of the composite endpoint from observed components when possible, and exclude from analysis participants whose composite endpoint cannot be derived. Alternatively, complete record analysis (CRA) (excluding participants with any missing components) or multiple imputation (MI) can be used. We compare a set of methods for analyzing a composite endpoint with partially observed components mathematically and by simulation, and apply these methods in a reanalysis of a published trial (TOPPS). We show that the derived composite endpoint can be missing not at random even when the components are missing completely at random. Consequently, the treatment effect estimated from the derived endpoint is biased while CRA results without the derived endpoint are valid. Missing at random mechanisms require MI of the components. We conclude that, although superficially attractive, deriving the composite endpoint from observed components should generally be avoided. Despite the potential risk of imputation model mis-specification, MI of missing components is the preferred approach in this study setting., (© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: Re-randomisation trials involve re-enrolling and re-randomising patients for each new treatment episode they experience. They are often used when interest lies in the average effect of an intervention across all the episodes for which it would be used in practice. Re-randomisation trials are often analysed using independence estimators, where a working independence correlation structure is used. However, research into independence estimators in the context of re-randomisation has been limited., Methods: We performed a simulation study to evaluate the use of independence estimators in re-randomisation trials. We focussed on a continuous outcome, and the setting where treatment allocation does not affect occurrence of subsequent episodes. We evaluated different treatment effect mechanisms (e.g. by allowing the treatment effect to vary across episodes, or to become less effective on re-use, etc), and different non-enrolment mechanisms (e.g. where patients who experience a poor outcome are less likely to re-enrol for their second episode). We evaluated four different independence estimators, each corresponding to a different estimand (per-episode and per-patient approaches, and added-benefit and policy-benefit approaches)., Results: We found that independence estimators were unbiased for the per-episode added-benefit estimand in all scenarios we considered. We found independence estimators targeting other estimands (per-patient or policy-benefit) were unbiased, except when there was differential non-enrolment between treatment groups (i.e. when different types of patients from each treatment group decide to re-enrol for subsequent episodes). We found the use of robust standard errors provided close to nominal coverage in all settings where the estimator was unbiased., Conclusions: Careful choice of estimand can ensure re-randomisation trials are addressing clinically relevant questions. Independence estimators are a useful approach, and should be considered as the default estimator until the statistical properties of alternative estimators are thoroughly evaluated., (© 2021. 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).)

Competing Interests: Competing interests: GCR receives funding from the NHS National Institute of Health Research (NIHR) School for Primary Care Research (SPCR), the Naji Foundation and the Rotary Foundation to study for a Doctor of Philosophy (DPhil) at the University of Oxford. GCR is a member of the EBMLive Steering Committee. SHB, ABD, CBH, BCK, TR, CW and LZJW were Doug Altman Scholars, presented at EBMLive 2019 and had their expenses reimbursed to travel to and attend the conference. CW is funded through the National Institute of Health grant number F30 CA243651-01. BCK has received funding from the NIHR Doctoral Research Fellowship program. PJG has received grant funding from the Canadian Paediatric Society, and the Canadian Institute of Health Research (CIHR) in the past 5 years. He is on the CMAJ Editorial Advisory Board and on the Institute Advisory Board for the CIHR Institute of Human Development, Child and Youth Health (IHDCYH) where he has expenses reimbursed to attend meetings. He is on the editorial board of BMJ Evidence Based Medicine. He is a member of the EBMLive Steering Committee, and he has expenses reimbursed to attend the conference.

Background and Objective: Prespecification of statistical methods in clinical trial protocols and statistical analysis plans can help to deter bias from p-hacking but is only effective if the prespecified approach is made available., Study Design and Setting: For 100 randomized trials published in 2018 and indexed in PubMed, we evaluated how often a prespecified statistical analysis approach for the trial's primary outcome was publicly available. For each trial with an available prespecified analysis, we compared this with the trial publication to identify whether there were unexplained discrepancies., Results: Only 12 of 100 trials (12%) had a publicly available prespecified analysis approach for their primary outcome; this document was dated before recruitment began for only two trials. Of the 12 trials with an available prespecified analysis approach, 11 (92%) had one or more unexplained discrepancies. Only 4 of 100 trials (4%) stated that the statistician was blinded until the SAP was signed off, and only 10 of 100 (10%) stated the statistician was blinded until the database was locked., Conclusion: For most published trials, there is insufficient information available to determine whether the results may be subject to p-hacking. Where information was available, there were often unexplained discrepancies between the prespecified and final analysis methods., (Copyright © 2020 Elsevier Inc. All rights reserved.)

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.

When designing a clinical trial, explicitly defining the treatment estimands of interest (that which is to be estimated) can help to clarify trial objectives and ensure the questions being addressed by the trial are clinically meaningful. There are several challenges when defining estimands. Here, we discuss a number of these in the context of trials of treatments for patients hospitalised with COVID-19 and make suggestions for how estimands should be defined for key outcomes. We suggest that treatment effects should usually be measured as differences in proportions (or risk or odds ratios) for outcomes such as death and requirement for ventilation, and differences in means for outcomes such as the number of days ventilated. We further recommend that truncation due to death should be handled differently depending on whether a patient- or resource-focused perspective is taken; for the former, a composite approach should be used, while for the latter, a while-alive approach is preferred. Finally, we suggest that discontinuation of randomised treatment should be handled from a treatment policy perspective, where non-adherence is ignored in the analysis (i.e. intention to treat).

Results from clinical trials can be susceptible to bias if investigators choose their analysis approach after seeing trial data, as this can allow them to perform multiple analyses and then choose the method that provides the most favourable result (commonly referred to as 'p-hacking'). Pre-specification of the planned analysis approach is essential to help reduce such bias, as it ensures analytical methods are chosen in advance of seeing the trial data. For this reason, guidelines such as SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and ICH-E9 (International Conference for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) require the statistical methods for a trial's primary outcome be pre-specified in the trial protocol. However, pre-specification is only effective if done in a way that does not allow p-hacking. For example, investigators may pre-specify a certain statistical method such as multiple imputation, but give little detail on how it will be implemented. Because there are many different ways to perform multiple imputation, this approach to pre-specification is ineffective, as it still allows investigators to analyse the data in different ways before deciding on a final approach. In this article, we describe a five-point framework (the Pre-SPEC framework) for designing a pre-specified analysis approach that does not allow p-hacking. This framework was designed based on the principles in the SPIRIT and ICH-E9 guidelines and is intended to be used in conjunction with these guidelines to help investigators design the statistical analysis strategy for the trial's primary outcome in the trial protocol.

Background: The coronavirus pandemic (Covid-19) presents a variety of challenges for ongoing clinical trials, including an inevitably higher rate of missing outcome data, with new and non-standard reasons for missingness. International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking., Methods: We present a four-step strategy for handling missing outcome data in the analysis of randomised trials that are ongoing during a pandemic. We consider handling missing data arising due to (i) participant infection, (ii) treatment disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a 'pandemic-free world' and 'world including a pandemic' are of interest., Results: In any trial, investigators should; (1) Clarify the treatment estimand of interest with respect to the occurrence of the pandemic; (2) Establish what data are missing for the chosen estimand; (3) Perform primary analysis under the most plausible missing data assumptions followed by; (4) Sensitivity analysis under alternative plausible assumptions. To obtain an estimate of the treatment effect in a 'pandemic-free world', participant data that are clinically affected by the pandemic (directly due to infection or indirectly via treatment disruptions) are not relevant and can be set to missing. For primary analysis, a missing-at-random assumption that conditions on all observed data that are expected to be associated with both the outcome and missingness may be most plausible. For the treatment effect in the 'world including a pandemic', all participant data is relevant and should be included in the analysis. For primary analysis, a missing-at-random assumption - potentially incorporating a pandemic time-period indicator and participant infection status - or a missing-not-at-random assumption with a poorer response may be most relevant, depending on the setting. In all scenarios, sensitivity analysis under credible missing-not-at-random assumptions should be used to evaluate the robustness of results. We highlight controlled multiple imputation as an accessible tool for conducting sensitivity analyses., Conclusions: Missing data problems will be exacerbated for trials active during the Covid-19 pandemic. This four-step strategy will facilitate clear thinking about the appropriate analysis for relevant questions of interest.

Background: There are around 40,000 new cases of malignant pleural effusion in the UK each year. Insertion of talc slurry via a chest tube is the current standard treatment in the UK. However, some centres prefer local anaesthetic thoracoscopy and talc poudrage. There is no consensus as to which approach is most effective., Objective: This trial tested the hypothesis that thoracoscopy and talc poudrage increases the proportion of patients with successful pleurodesis at 3 months post procedure, compared with chest drain insertion and talc slurry., Design: This was a multicentre, open-label, randomised controlled trial with embedded economic evaluation. Follow-up took place at 1, 3 and 6 months., Setting: This trial was set in 17 NHS hospitals in the UK., Participants: A total of 330 adults with a confirmed diagnosis of malignant pleural effusion needing pleurodesis and fit to undergo thoracoscopy under local anaesthetic were included. Those adults needing a tissue diagnosis or with evidence of lung entrapment were excluded., Interventions: Allocation took place following minimisation with a random component, performed by a web-based, centralised computer system. Participants in the control arm were treated with a bedside chest drain insertion and 4 g of talc slurry. In the intervention arm, participants underwent local anaesthetic thoracoscopy with 4 g of talc poudrage., Main Outcome Measures: The primary outcome measure was pleurodesis failure at 90 days post randomisation. Secondary outcome measures included mortality and patient-reported symptoms. A cost-utility analysis was also performed., Results: A total of 166 and 164 patients were allocated to poudrage and slurry, respectively. Participants were well matched at baseline. For the primary outcome, no significant difference in pleurodesis failure was observed between the treatment groups at 90 days, with rates of 36 out of 161 (22%) and 38 out of 159 (24%) noted in the poudrage and slurry groups, respectively (odds ratio 0.91, 95% confidence interval 0.54 to 1.55; p  = 0.74). No differences (or trends towards difference) were noted in adverse events or any of the secondary outcomes at any time point, including pleurodesis failure at 180 days [poudrage 46/161 (29%), slurry 44/159 (28%), odds ratio 1.05, 95% confidence interval 0.63 to 1.73; p  = 0.86], mean number of nights in hospital over 90 days [poudrage 12 nights (standard deviation 13 nights), slurry 11 nights (standard deviation 10 nights); p  = 0.35] and all-cause mortality at 180 days [poudrage 66/166 (40%), slurry 68/164 (42%); p  = 0.70]. At £20,000 per quality-adjusted life-year gained, poudrage would have a 0.36 probability of being cost-effective compared with slurry., Limitations: Entry criteria specified that patients must be sufficiently fit to undergo thoracoscopy, which may make the results less applicable to those patients presenting with a greater degree of frailty. Furthermore, the trial was conducted on an open-label basis, which may have influenced the results of patient-reported measures., Conclusions: The TAPPS (evaluating the efficacy of Thoracoscopy And talc Poudrage versus Pleurodesis using talc Slurry) trial has robustly demonstrated that there is no additional clinical effectiveness or cost-effectiveness benefit in performing talc poudrage at thoracoscopy over bedside chest drain and talc slurry for the management of malignant pleural effusion., Trial Registration: Current Controlled Trials ISRCTN47845793., Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment ; Vol. 24, No. 26. See the NIHR Journals Library website for further project information., Competing Interests: Ramon Luengo-Fernandez reports grants from the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme outside the submitted work. Najib M Rahman reports personal fees from Rocket Medical plc (Watford, UK) outside the submitted work. Nick A Maskell is a member of the NIHR HTA Commissioning Board.