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[...]for a binary outcome, a risk ratio or risk difference; for a count outcome, a rate ratio; or for time to event outcome, a hazard ratio may be appropriate; for each of the aforementioned outcome types, other summary measure options exist [7, 8–9], and the most appropriate should be chosen based on the trial objective. The average difference (summary measure) in tiredness score (outcome) between eating a mince pie and a bunch of five carrots (treatment conditions) for flying reindeers (population) regardless of whether all the carrots or mince pies are eaten or cheeky reindeers switching allocations or sneaky reindeers eating other food on route or bold reindeers indulging in Santa’s sherry (intercurrent events—treatment policy strategy). Like optimising performance for flying reindeer, in trials of healthcare interventions multiple different questions can be investigated and the answers to these questions may lead to different conclusions on treatment benefit [5]. In such trials, participants may similarly experience different types of intercurrent events which when handled in different ways can result in different impressions of treatment benefit.

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.

Frequentist trials in Rare disease/small population trials often require unfeasibly large sample size to detect minimum clinically important differences. A targeted review was performed investigating what design and analysis methods these trials use when facing restricted recruitment. Targeted Review searching EMBASE and MEDLINE for Phase II-IV RCTs reporting ‘rare’ disease or ‘small population’ within title or abstract, since 2009. A total of 6,128 articles were screened with 64 trials eligible (four Bayesian, 60 frequentist trials). Frequentists trials had planned power ranging 72–90% (median: 80%) but reported recruiting a mean of 6.6% below the planned sample size (n = 38) [median 0%, IQR (-5%, 5%)], most used standard type I error (52 used 5% and one used 1%), and the average standardized effect was high (0.7) with 50% missing their assumed level. Of the four Bayesian trials, three used informed priors, two and one trials performed sensitivity analysis for the impact of priors on design and analysis respectively. Historical data, expert consensus, or both were used to construct informative priors. Bayesian trials required 30–2400% less participants than using frequentist frameworks. Bayesian trials required lower sample size through use of informative priors. Most frequentists didn't achieve their target sample size. Bayesian methods offer promising solutions for such trials but are underutilized.

Background It is important to estimate the treatment effect of interest accurately and precisely within the analysis of randomised controlled trials. One way to increase precision in the estimate and thus improve the power for randomised trials with continuous outcomes is through adjustment for pre-specified prognostic baseline covariates. Typically covariate adjustment is conducted using regression analysis, however recently, Inverse Probability of Treatment Weighting (IPTW) using the propensity score has been proposed as an alternative method. For a continuous outcome it has been shown that the IPTW estimator has the same large sample statistical properties as that obtained via analysis of covariance. However the performance of IPTW has not been explored for smaller population trials (< 100 participants), where precise estimation of the treatment effect has potential for greater impact than in larger samples. Methods In this paper we explore the performance of the baseline adjusted treatment effect estimated using IPTW in smaller population trial settings. To do so we present a simulation study including a number of different trial scenarios with sample sizes ranging from 40 to 200 and adjustment for up to 6 covariates. We also re-analyse a paediatric eczema trial that includes 60 children. Results In the simulation study the performance of the IPTW variance estimator was sub-optimal with smaller sample sizes. The coverage of 95% CI’s was marginally below 95% for sample sizes < 150 and ≥ 100. For sample sizes < 100 the coverage of 95% CI’s was always significantly below 95% for all covariate settings. The minimum coverage obtained with IPTW was 89% with n  = 40. In comparison, regression adjustment always resulted in 95% coverage. The analysis of the eczema trial confirmed discrepancies between the IPTW and regression estimators in a real life small population setting. Conclusions The IPTW variance estimator does not perform so well with small samples. Thus we caution against the use of IPTW in small sample settings when the sample size is less than 150 and particularly when sample size < 100.

Background Randomised controlled trials (RCTs) are the gold standard for evaluating treatment effects, with the results informing policy and clinical practice. To ensure appropriate methods are utilised and to avoid misinterpretation of the results of a clinical trial, it is vital that we understand the research question a trial aims to answer. However, there is often ambiguity in how trialists define their research questions. In 2019, an addendum to the international trial regulatory guidelines (ICH E9 (R1)) introduced the estimand framework to combat this. A review of protocols published in 2020 investigated the early adoption of the estimand framework and found no uptake as well as a lack of clarity on key items such as the handling of intercurrent events. The aim of this review was to identify the current application of the estimand framework specifically to trials with an adaptive design. Methods The search strategy aimed to identify trial protocols and statistical analysis plans that described RCTs published in two journals (BMJ Open and Trials) in 2023. Articles were eligible if they related to phase 2–4 trials with an adaptive design. A pre-piloted data extract form was used to extract data relating to study details, intercurrent events and estimands. Results One thousand five hundred and forty-one articles were identified by the initial search. Following screening, 146 articles were identified as meeting the eligibility criteria. Of the eligible articles, five (3%) stated their primary estimand, and of these, three (2%) stated all five estimand attributes. Ninety-four (64%) articles described one or more intercurrent events; these included a total of two hundred and thirty-two intercurrent events described. Fifty-two (36%) articles did not describe any intercurrent events. No articles specified the estimand for any planned interim analyses or considered the implications of adaptations on the primary estimand. Conclusions This review provides evidence that there is still a lack of uptake of the estimand framework in RCTs. Wider application of the estimand framework would ensure clarity in the reporting and interpretation of clinical trial results. In addition, clear guidance on how to implement the estimand framework to trials with an adaptive design is needed.

In the United Kingdom, it is estimated that 5,000 patients sustain eye injuries or ocular trauma requiring hospital admission annually, of which 250 patients will be permanently blinded. This study explores the cost-effectiveness of Adjunctive Steroid Combination in Ocular Trauma (ASCOT) given during surgery versus standard treatment in vitreoretinal surgery in patients with open globe trauma. This economic evaluation was embedded alongside the ASCOT RCT (ClinicalTrials.gov Identifier: NCT02873026). We conducted a primary cost-effectiveness analysis from a National Health Service perspective using the proportion of patients who achieved a visual acuity of 10 or more letter improvement on the Early Treatment Diabetic Retinopathy Study (ETDRS) scale as the measure of effect, in developing incremental cost-effectiveness ratios (ICERs). Secondary cost-utility analysis using the EuroQol 5 Dimension 5 Level (EQ-5D-5L) to generate a cost per quality-adjusted life-year (QALY), and a cost-effectiveness analysis using vision-specific quality of life (QoL) was conducted. Sensitivity analyses were also applied to investigate parameter uncertainties. The sample size of the ASCOT intervention arm and standard care arm of this study was 130 and 129, respectively. The intervention cost per patient was estimated at £132. The proportion of participants with an ETDRS of 10 or more letter improvement was 0.47 for the ASCOT group with a mean cost of £5,526 per patient, while the standard care group had an effect of 0.43 with a mean cost of £5,099 per patient. The ICER value of the primary outcome was £12,178 per 10 or more letter improvement on the ETDRS score. The secondary result in terms of cost per QALYs gained had a probability of 44% being cost-effective at a willingness-to-pay threshold of £30,000/QALY gained. Though there is no formally accepted cost-effectiveness willingness-to-pay threshold for 10-letter or more improvement, the ASCOT intervention for open globe trauma is a low-cost intervention. The ASCOT intervention is not cost-effective when compared to the standard care in this group and setting. The proportion of patients in the ASCOT intervention arm with 10 or more letter improvement produced some positive results but this is outweighed by the costs.

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.

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 Classical approaches to subgroup analysis in randomised controlled trials (RCTs) to identify heterogeneous treatment effects (HTEs) involve testing the interaction between each pre-specified possible treatment effect modifier and the treatment effect. However, individual significant interactions may not always yield clinically actionable subgroups, particularly for continuous covariates. Non-parametric causal machine learning approaches are flexible alternatives for estimating HTEs across many possible treatment effect modifiers in a single analysis. Methods We conducted a secondary analysis of the VANISH RCT, which compared the early use of vasopressin with norepinephrine on renal failure-free survival for patients with septic shock at 28 days. We used classical (separate tests for interaction with Bonferroni correction), data-adaptive (hierarchical lasso regression), and non-parametric causal machine learning (causal forest) methods to analyse HTEs for the primary outcome of being alive at 28 days. Causal forests comprise honest causal trees, which use sample splitting to determine tree splits and estimate treatment effects separately. The modal initial (root) splits of the causal forest were extracted, and the mean value was used as a threshold to partition the population into subgroups with different treatment effects. Results All three models found evidence of HTE with serum potassium levels. Univariable logistic regression OR 0.435 (95%CI [0.270, 0.683]. p  = 0.0004), hierarchical lasso logistic regression standardised OR: 0.604 (95% CI 0.259, 0.701), lambda = 0.0049. Hierarchical lasso kept the interaction between the treatment and serum potassium, sodium level, minimum temperature, platelet count and presence of ischemic heart disease. The causal forest approach found some evidence of HTE ( p  = 0.124). When extracting root splits, the modal split was on serum potassium (mean applied threshold of 4.68 mmol/L). When dividing the patient population into subgroups based on the mean initial root threshold, risk differences in being alive at 28 days were 0.069 (95%CI [-0.032, 0.169]) and − 0.257 (95%CI [-0.368, -0.146]) with serum potassium ≤ 4.68 and > 4.68 respectively. Conclusions The causal forest agreed with the data-adaptive and classical method of subgroup analysis in identifying HTE by serum potassium. Whilst classical and data-adaptive methods may identify sources of HTE, they do not immediately suggest subgroup splits which are clinically actionable. The extraction of root splits in causal forests is a novel approach to obtaining data-derived subgroups, to be further investigated.

Introduction Underrepresentation of disabled groups in clinical trials results in an inadequate evidence base for their clinical care, which drives health inequalities. This study aims to review and map the potential barriers and facilitators to the recruitment of disabled people in clinical trials to identify knowledge gaps and areas for further extensive research. The review addresses the question: ‘What are the barriers and facilitators to recruitment of disabled people to clinical trials?’. Methods The Joanna Briggs Institute (JBI) Scoping review guidelines were followed to complete the current scoping review. MEDLINE and EMBASE databases were searched via Ovid. The literature search was guided by a combination of four key concepts from the research question: (1) disabled populations, (2) patient recruitment, (3) barriers and facilitators, and (4) clinical trials. Papers discussing barriers and facilitators of all types were included. Papers that did not have at least one disabled group as their population were excluded. Data on study characteristics and identified barriers and facilitators were extracted. Identified barriers and facilitators were then synthesised according to common themes. Results The review included 56 eligible papers. The evidence on barriers and facilitators was largely sourced from Short Communications from Researcher Perspectives ( N  = 22) and Primary Quantitative Research ( N  = 17). Carer perspectives were rarely represented in articles. The most common disability types for the population of interest in the literature were neurological and psychiatric disabilities. A total of five emergent themes were determined across the barriers and facilitators. These were as follows: risk vs benefit assessment, design and management of recruitment protocol, balancing internal and external validity considerations, consent and ethics, and systemic factors. Conclusions Both barriers and facilitators were often highly specific to disability type and context. Assumptions should be minimised, and study design should prioritise principles of co-design and be informed by a data-driven assessment of needs for the study population. Person-centred approaches to consent that empower disabled people to exercise their right to choose should be adopted in inclusive practice. Implementing these recommendations stands to improve inclusive practices in clinical trial research, serving to produce a well-rounded and comprehensive evidence base.