The cost-effectiveness of procalcitonin for guiding antibiotic prescribing in individuals hospitalized with COVID-19: part of the PEACH study.

BACKGROUND: Many hospitals introduced procalcitonin (PCT) testing to help diagnose bacterial coinfection in individuals with COVID-19, and guide antibiotic decision-making during the COVID-19 pandemic in the UK. OBJECTIVES: Evaluating cost-effectiveness of using PCT to guide antibiotic decisions in individuals hospitalized with COVID-19, as part of a wider research programme. METHODS: Retrospective individual-level data on patients hospitalized with COVID-19 were collected from 11 NHS acute hospital Trusts and Health Boards from England and Wales, which varied in their use of baseline PCT testing during the first COVID-19 pandemic wave. A matched analysis (part of a wider analysis reported elsewhere) created groups of patients whose PCT was/was not tested at baseline. A model was created with combined decision tree/Markov phases, parameterized with quality-of-life/unit cost estimates from the literature, and used to estimate costs and quality-adjusted life years (QALYs). Cost-effectiveness was judged at a £20 000/QALY threshold. Uncertainty was characterized using bootstrapping. RESULTS: People who had baseline PCT testing had shorter general ward/ICU stays and spent less time on antibiotics, though with overlap between the groups' 95% CIs. Those with baseline PCT testing accrued more QALYs (8.76 versus 8.62) and lower costs (£9830 versus £10 700). The point estimate was baseline PCT testing being dominant over no baseline testing, though with uncertainty: the probability of cost-effectiveness was 0.579 with a 1 year horizon and 0.872 with a lifetime horizon. CONCLUSIONS: Using PCT to guide antibiotic therapy in individuals hospitalized with COVID-19 is more likely to be cost-effective than not, albeit with uncertainty.

A retrospective propensity-score-matched cohort study of the impact of procalcitonin testing on antibiotic use in hospitalized patients during the first wave of COVID-19.

BACKGROUND: Procalcitonin (PCT) is a blood marker used to help diagnose bacterial infections and guide antibiotic treatment. PCT testing was widely used/adopted during the COVID-19 pandemic in the UK. OBJECTIVES: Primary: to measure the difference in length of early (during first 7 days) antibiotic prescribing between patients with COVID-19 who did/did not have baseline PCT testing during the first wave of the pandemic. Secondary: to measure differences in length of hospital/ICU stay, mortality, total days of antibiotic prescribing and resistant bacterial infections between these groups. METHODS: Multi-centre, retrospective, observational, cohort study using patient-level clinical data from acute hospital Trusts/Health Boards in England/Wales. Inclusion: patients ≥16 years, admitted to participating Trusts/Health Boards and with a confirmed positive COVID-19 test between 1 February 2020 and 30 June 2020. RESULTS: Data from 5960 patients were analysed: 1548 (26.0%) had a baseline PCT test and 4412 (74.0%) did not. Using propensity-score matching, baseline PCT testing was associated with an average reduction in early antibiotic prescribing of 0.43 days [95% confidence interval (CI): 0.22-0.64 days, P < 0.001) and of 0.72 days (95% CI: 0.06-1.38 days, P = 0.03] in total antibiotic prescribing. Baseline PCT testing was not associated with increased mortality or hospital/ICU length of stay or with the rate of antimicrobial-resistant secondary bacterial infections. CONCLUSIONS: Baseline PCT testing appears to have been an effective antimicrobial stewardship tool early in the pandemic: it reduced antibiotic prescribing without evidence of harm. Our study highlights the need for embedded, rapid evaluations of infection diagnostics in the National Health Service so that even in challenging circumstances, introduction into clinical practice is supported by evidence for clinical utility. STUDY REGISTRATION NUMBER: ISRCTN66682918.

Development and preliminary validation of a diagnostic prediction model to optimise outpatient management of patients with urolithiasis using urinary stones and intervention quality of life (USIQoL) measure.

PURPOSE: Patients with urinary calculi undergo resource-intensive follow-up. Application of a PROM, Urinary Stones and Intervention Quality of Life (USIQoL), can potentially optimise current practices if it matches the outcomes of traditional follow-up. Our objective was to develop, and conduct, a preliminary validation of the USIQoL based prediction model to aid triage. METHODS: We performed a two phase prospective cohort study. The 1st phase included development of the USIQoL-based decision model using multicentre data. The 2nd phase involved prospective single-blind external validation for the outpatient application. The aim was to evaluate correlations between the USIQoL scores and key predictors; clinical outcomes and global health ratings (EuroQoL EQ-5D). We used statistical analysis to validate USIQoL cut-off scores to aid triage and the decision to intervene. RESULTS: Of 503 patients invited, 91% (n = 455, Development [305] and Validation [150]; M = 308, F = 147) participated. The relationship between USIQoL domain scores and clinical outcomes was consistently significant (estimated odds: PPH 1.24, p < 0.001, 95% CI 1.13-1.36; PSH 1.22, p < 0.001, 95% CI 1.12-1.33). The ROC values for the model were ≥ 0.75. The optimum domain cut-off scores were derived with rising scores implying increased need to intervene. The model demonstrated satisfactory sensitivity (0.81-0.89) and specificity (0.36-0.47). CONCLUSIONS: The study demonstrates satisfactory correlation between the USIQoL and clinical outcomes making this model a valid aid for triage and optimising outpatient management with the cut-off scores able to identify high risk patients who need active treatment.

Point estimation for adaptive trial designs I: A methodological review.

Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.

Point estimation for adaptive trial designs II: Practical considerations and guidance.

In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred.

Practical guidance for planning resources required to support publicly-funded adaptive clinical trials.

Adaptive designs are a class of methods for improving efficiency and patient benefit of clinical trials. Although their use has increased in recent years, research suggests they are not used in many situations where they have potential to bring benefit. One barrier to their more widespread use is a lack of understanding about how the choice to use an adaptive design, rather than a traditional design, affects resources (staff and non-staff) required to set-up, conduct and report a trial. The Costing Adaptive Trials project investigated this issue using quantitative and qualitative research amongst UK Clinical Trials Units. Here, we present guidance that is informed by our research, on considering the appropriate resourcing of adaptive trials. We outline a five-step process to estimate the resources required and provide an accompanying costing tool. The process involves understanding the tasks required to undertake a trial, and how the adaptive design affects them. We identify barriers in the publicly funded landscape and provide recommendations to trial funders that would address them. Although our guidance and recommendations are most relevant to UK non-commercial trials, many aspects are relevant more widely.

Impact of introducing procalcitonin testing on antibiotic usage in acute NHS hospitals during the first wave of COVID-19 in the UK: a controlled interrupted time series analysis of organization-level data.

BACKGROUND: Blood biomarkers have the potential to help identify COVID-19 patients with bacterial coinfection in whom antibiotics are indicated. During the COVID-19 pandemic, procalcitonin testing was widely introduced at hospitals in the UK to guide antibiotic prescribing. We have determined the impact of this on hospital-level antibiotic consumption. METHODS: We conducted a retrospective, controlled interrupted time series analysis of organization-level data describing antibiotic dispensing, hospital activity and procalcitonin testing for acute hospitals/hospital trusts in England and Wales during the first wave of COVID-19 (24 February to 5 July 2020). RESULTS: In the main analysis of 105 hospitals in England, introduction of procalcitonin testing in emergency departments/acute medical admission units was associated with a statistically significant decrease in total antibiotic use of -1.08 (95% CI: -1.81 to -0.36) DDDs of antibiotic per admission per week per trust. This effect was then lost at a rate of 0.05 (95% CI: 0.02-0.08) DDDs per admission per week. Similar results were found specifically for first-line antibiotics for community-acquired pneumonia and for COVID-19 admissions rather than all admissions. Introduction of procalcitonin in the ICU setting was not associated with any significant change in antibiotic use. CONCLUSIONS: At hospitals where procalcitonin testing was introduced in emergency departments/acute medical units this was associated with an initial, but unsustained, reduction in antibiotic use. Further research should establish the patient-level impact of procalcitonin testing in this population and understand its potential for clinical effectiveness.

Short-term risk prediction after major lower limb amputation: PERCEIVE study.

BACKGROUND: The accuracy with which healthcare professionals (HCPs) and risk prediction tools predict outcomes after major lower limb amputation (MLLA) is uncertain. The aim of this study was to evaluate the accuracy of predicting short-term (30 days after MLLA) mortality, morbidity, and revisional surgery. METHODS: The PERCEIVE (PrEdiction of Risk and Communication of outcomE following major lower limb amputation: a collaboratIVE) study was launched on 1 October 2020. It was an international multicentre study, including adults undergoing MLLA for complications of peripheral arterial disease and/or diabetes. Preoperative predictions of 30-day mortality, morbidity, and MLLA revision by surgeons and anaesthetists were recorded. Probabilities from relevant risk prediction tools were calculated. Evaluation of accuracy included measures of discrimination, calibration, and overall performance. RESULTS: Some 537 patients were included. HCPs had acceptable discrimination in predicting mortality (931 predictions; C-statistic 0.758) and MLLA revision (565 predictions; C-statistic 0.756), but were poor at predicting morbidity (980 predictions; C-statistic 0.616). They overpredicted the risk of all outcomes. All except three risk prediction tools had worse discrimination than HCPs for predicting mortality (C-statistics 0.789, 0.774, and 0.773); two of these significantly overestimated the risk compared with HCPs. SORT version 2 (the only tool incorporating HCP predictions) demonstrated better calibration and overall performance (Brier score 0.082) than HCPs. Tools predicting morbidity and MLLA revision had poor discrimination (C-statistics 0.520 and 0.679). CONCLUSION: Clinicians predicted mortality and MLLA revision well, but predicted morbidity poorly. They overestimated the risk of mortality, morbidity, and MLLA revision. Most short-term risk prediction tools had poorer discrimination or calibration than HCPs. The best method of predicting mortality was a statistical tool that incorporated HCP estimation.

Mid-Regional Pro-Adrenomedullin in Combination With Pediatric Early Warning Scores for Risk Stratification of Febrile Children Presenting to the Emergency Department: Secondary Analysis of a Nonprespecified United Kingdom Cohort Study.

OBJECTIVES: Current sepsis guidelines do not provide good risk stratification of subgroups in whom prompt IV antibiotics and fluid resuscitation might of benefit. We evaluated the utility of mid-regional pro-adrenomedullin (MR-proADM) in identification of patient subgroups at risk of requiring PICU or high-dependency unit (HDU) admission or fluid resuscitation. DESIGN: Secondary, nonprespecified analysis of prospectively collected dataset. SETTING: Pediatric Emergency Department in a United Kingdom tertiary center. PATIENTS: Children less than 16 years old presenting with fever and clinical indication for venous blood sampling ( n = 1,183). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Primary outcome measures were PICU/HDU admission or administration of fluid resuscitation, with a secondary outcome of definite or probable bacterial infection. Biomarkers were measured on stored plasma samples and children phenotyped into bacterial and viral groups using a previously published algorithm. Of the 1,183 cases, 146 children (12.3%) required fluids, 48 (4.1%) were admitted to the PICU/HDU, and 244 (20.6%) had definite or probable bacterial infection. Area under the receiver operating characteristic (AUC) was used to assess performance. MR-proADM better predicted fluid resuscitation (AUC, 0.73; 95% CI, 0.67-0.78), than both procalcitonin (AUC, 0.65; 95% CI, 0.59-0.71) and Pediatric Early Warning Score (PEWS: AUC, 0.62; 95% CI, 0.56-0.67). PEWS alone showed good accuracy for PICU/HDU admission 0.83 (0.78-0.89). Patient subgroups with high MR-proADM (≥ 0.7 nmol/L) and high procalcitonin (≥ 0.5 ng/mL) had increased association with PICU/HDU admission, fluid resuscitation, and bacterial infection compared with subgroups with low MR-proADM (< 0.7 nmol/L). For children with procalcitonin less than 0.5 ng/mL, high MR-proADM improved stratification for fluid resuscitation only. CONCLUSIONS: High MR-proADM and high procalcitonin were associated with increased likelihood of subsequent disease progression. Incorporating MR-proADM into clinical risk stratification may be useful in clinician decision-making regarding initiation of IV antibiotics, fluid resuscitation, and escalation to PICU/HDU admission.

Costs and staffing resource requirements for adaptive clinical trials: quantitative and qualitative results from the Costing Adaptive Trials project.

BACKGROUND: Adaptive designs offer great promise in improving the efficiency and patient-benefit of clinical trials. An important barrier to further increased use is a lack of understanding about which additional resources are required to conduct a high-quality adaptive clinical trial, compared to a traditional fixed design. The Costing Adaptive Trials (CAT) project investigated which additional resources may be required to support adaptive trials. METHODS: We conducted a mock costing exercise amongst seven Clinical Trials Units (CTUs) in the UK. Five scenarios were developed, derived from funded clinical trials, where a non-adaptive version and an adaptive version were described. Each scenario represented a different type of adaptive design. CTU staff were asked to provide the costs and staff time they estimated would be needed to support the trial, categorised into specified areas (e.g. statistics, data management, trial management). This was calculated separately for the non-adaptive and adaptive version of the trial, allowing paired comparisons. Interviews with 10 CTU staff who had completed the costing exercise were conducted by qualitative researchers to explore reasons for similarities and differences. RESULTS: Estimated resources associated with conducting an adaptive trial were always (moderately) higher than for the non-adaptive equivalent. The median increase was between 2 and 4% for all scenarios, except for sample size re-estimation which was 26.5% (as the adaptive design could lead to a lengthened study period). The highest increase was for statistical staff, with lower increases for data management and trial management staff. The percentage increase in resources varied across different CTUs. The interviews identified possible explanations for differences, including (1) experience in adaptive trials, (2) the complexity of the non-adaptive and adaptive design, and (3) the extent of non-trial specific core infrastructure funding the CTU had. CONCLUSIONS: This work sheds light on additional resources required to adequately support a high-quality adaptive trial. The percentage increase in costs for supporting an adaptive trial was generally modest and should not be a barrier to adaptive designs being cost-effective to use in practice. Informed by the results of this research, guidance for investigators and funders will be developed on appropriately resourcing adaptive trials.

A Systematic Review and Narrative Synthesis of Risk Prediction Tools Used to Estimate Mortality, Morbidity, and Other Outcomes Following Major Lower Limb Amputation.

OBJECTIVE: The decision to undertake a major lower limb amputation can be complex. This review evaluates the performance of risk prediction tools in estimating mortality, morbidity, and other outcomes following amputation. METHODS: A systematic review was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The MEDLINE, Embase, and Cochrane databases were searched to identify studies reporting on risk prediction tools that predict outcomes following amputation. Outcome measures included the accuracy of the risk tool in predicting a range of post-operative complications, including mortality (both short and long term), peri-operative morbidity, need for re-amputation, and ambulation success. A narrative synthesis was performed in accordance with the Guidance on the Conduct of Narrative Synthesis In Systematic Reviews. RESULTS: The search identified 518 database records. Twelve observational studies, evaluating 13 risk prediction tools in a total cohort of 61 099 amputations, were included. One study performed external validation of an existing risk prediction tool, while all other studies developed novel tools or modified pre-existing generic calculators. Two studies conducted external validation of the novel/modified tools. Nine tools provided risk estimations for mortality, two tools provided predictions for post-operative morbidity, two for likelihood of ambulation, and one for re-amputation to the same or higher level. Most mortality prediction tools demonstrated acceptable discrimination performance with C statistic values ranging from 0.65 to 0.81. Tools estimating the risk of post-operative complications (0.65 - 0.74) and necessity for re-amputation (0.72) also performed acceptably. The Blatchford Allman Russell tool demonstrated outstanding discrimination for predicting functional mobility outcomes post-amputation (0.94). Overall, most studies were at high risk of bias with poor external validity. CONCLUSION: This review identified several risk prediction tools that demonstrate acceptable to outstanding discrimination for objectively predicting an array of important post-operative outcomes. However, the methodological quality of some studies was poor, external validation studies are generally lacking, and there are no tools predicting other important outcomes, especially quality of life.

Reduction in massive postpartum haemorrhage and red blood cell transfusion during a national quality improvement project, Obstetric Bleeding Strategy for Wales, OBS Cymru: an observational study.

BACKGROUND: Postpartum haemorrhage (PPH) is a major cause of maternal morbidity and mortality and its incidence is increasing in many countries despite management guidelines. A national quality improvement programme called the Obstetric Bleeding Strategy for Wales (OBS Cymru) was introduced in all obstetric units in Wales. The aim was to reduce moderate PPH (1000 mL) progressing to massive PPH (> 2500 mL) and the need for red cell transfusion. METHODS: A PPH care bundle was introduced into all 12 obstetric units in Wales included all women giving birth in 2017 and 2018 (n = 61,094). The care bundle prompted: universal risk assessment, quantitative measurement of blood loss after all deliveries (as opposed to visual estimation), structured escalation to senior clinicians and point-of-care viscoelastometric-guided early fibrinogen replacement. Data were submitted by each obstetric unit to a national database. Outcome measures were incidence of massive PPH (> 2500 mL) and red cell transfusion. Analysis was performed using linear regression of the all Wales monthly data. RESULTS: Uptake of the intervention was good: quantitative blood loss measurement and risk assessment increased to 98.1 and 64.5% of all PPH > 1000 mL, whilst ROTEM use for PPH > 1500 mL increased to 68.2%. Massive PPH decreased by 1.10 (95% CI 0.28 to 1.92) per 1000 maternities per year (P = 0.011). Fewer women progressed from moderate to massive PPH in the last 6 months, 74/1490 (5.0%), than in the first 6 months, 97/1386 (7.0%), (P = 0.021). Units of red cells transfused decreased by 7.4 (95% CI 1.6 to 13.2) per 1000 maternities per year (P = 0.015). Red cells were transfused to 350/15204 (2.3%) and 268/15150 (1.8%) (P = 0.001) in the first and last 6 months, respectively. There was no increase in the number of women with lowest haemoglobin below 80 g/L during this time period. Infusions of fresh frozen plasma fell and there was no increase in the number of women with haemostatic impairment. CONCLUSIONS: The OBS Cymru care bundle was feasible to implement and associated with progressive, clinically significant improvements in outcomes for PPH across Wales. It is applicable across obstetric units of widely varying size, complexity and staff mixes.

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs.

Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies.

Designing and evaluating dose-escalation studies made easy: The MoDEsT web app.

BACKGROUND/AIMS: Dose-escalation studies are essential in the early stages of developing novel treatments, when the aim is to find a safe dose for administration in humans. Despite their great importance, many dose-escalation studies use study designs based on heuristic algorithms with well-documented drawbacks. Bayesian decision procedures provide a design alternative that is conceptually simple and methodologically sound, but very rarely used in practice, at least in part due to their perceived statistical complexity. There are currently very few easily accessible software implementations that would facilitate their application. METHODS: We have created MoDEsT, a free and easy-to-use web application for designing and conducting single-agent dose-escalation studies with a binary toxicity endpoint, where the objective is to estimate the maximum tolerated dose. MoDEsT uses a well-established Bayesian decision procedure based on logistic regression. The software has a user-friendly point-and-click interface, makes changes visible in real time, and automatically generates a range of graphs, tables, and reports. It is aimed at clinicians as well as statisticians with limited expertise in model-based dose-escalation designs, and does not require any statistical programming skills to evaluate the operating characteristics of, or implement, the Bayesian dose-escalation design. RESULTS: MoDEsT comes in two parts: a 'Design' module to explore design options and simulate their operating characteristics, and a 'Conduct' module to guide the dose-finding process throughout the study. We illustrate the practical use of both modules with data from a real phase I study in terminal cancer. CONCLUSION: Enabling both methodologists and clinicians to understand and apply model-based study designs with ease is a key factor towards their routine use in early-phase studies. We hope that MoDEsT will enable incorporation of Bayesian decision procedures for dose escalation at the earliest stage of clinical trial design, thus increasing their use in early-phase trials.

Adaptive designs in clinical trials: why use them, and how to run and report them.

Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.

Development process of a consensus-driven CONSORT extension for randomised trials using an adaptive design.

BACKGROUND: Adequate reporting of adaptive designs (ADs) maximises their potential benefits in the conduct of clinical trials. Transparent reporting can help address some obstacles and concerns relating to the use of ADs. Currently, there are deficiencies in the reporting of AD trials. To overcome this, we have developed a consensus-driven extension to the CONSORT statement for randomised trials using an AD. This paper describes the processes and methods used to develop this extension rather than detailed explanation of the guideline. METHODS: We developed the guideline in seven overlapping stages: 1) Building on prior research to inform the need for a guideline; 2) A scoping literature review to inform future stages; 3) Drafting the first checklist version involving an External Expert Panel; 4) A two-round Delphi process involving international, multidisciplinary, and cross-sector key stakeholders; 5) A consensus meeting to advise which reporting items to retain through voting, and to discuss the structure of what to include in the supporting explanation and elaboration (E&E) document; 6) Refining and finalising the checklist; and 7) Writing-up and dissemination of the E&E document. The CONSORT Executive Group oversaw the entire development process. RESULTS: Delphi survey response rates were 94/143 (66%), 114/156 (73%), and 79/143 (55%) in rounds 1, 2, and across both rounds, respectively. Twenty-seven delegates from Europe, the USA, and Asia attended the consensus meeting. The main checklist has seven new and nine modified items and six unchanged items with expanded E&E text to clarify further considerations for ADs. The abstract checklist has one new and one modified item together with an unchanged item with expanded E&E text. The E&E document will describe the scope of the guideline, the definition of an AD, and some types of ADs and trial adaptations and explain each reporting item in detail including case studies. CONCLUSIONS: We hope that making the development processes, methods, and all supporting information that aided decision-making transparent will enhance the acceptability and quick uptake of the guideline. This will also help other groups when developing similar CONSORT extensions. The guideline is applicable to all randomised trials with an AD and contains minimum reporting requirements.

Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models.

Simultaneous inference in longitudinal, repeated-measures, and multi-endpoint designs can be onerous, especially when trying to find a reasonable joint model from which the interesting effects and covariances are estimated. A novel statistical approach known as multiple marginal models greatly simplifies the modelling process: the core idea is to "marginalise" the problem and fit multiple small models to different portions of the data, and then estimate the overall covariance matrix in a subsequent, separate step. Using these estimates guarantees strong control of the family-wise error rate, however only asymptotically. In this paper, we show how to make the approach also applicable to small-sample data problems. Specifically, we discuss the computation of adjusted P values and simultaneous confidence bounds for comparisons of randomised treatment groups as well as for levels of a nonrandomised factor such as multiple endpoints, repeated measures, or a series of points in time or space. We illustrate the practical use of the method with a data example.

Simultaneous confidence regions for multivariate bioequivalence.

Demonstrating bioequivalence of several pharmacokinetic (PK) parameters, such as AUC and Cmax , that are calculated from the same biological sample measurements is in fact a multivariate problem, even though this is neglected by most practitioners and regulatory bodies, who typically settle for separate univariate analyses. We believe, however, that a truly multivariate evaluation of all PK measures simultaneously is clearly more adequate. In this paper, we review methods to construct joint confidence regions around multivariate normal means and investigate their usefulness in simultaneous bioequivalence problems via simulation. Some of them work well for idealised scenarios but break down when faced with real-data challenges such as unknown variance and correlation among the PK parameters. We study the shapes of the confidence regions resulting from different methods, discuss how marginal simultaneous confidence intervals for the individual PK measures can be derived, and illustrate the application to data from a trial on ticlopidine hydrochloride. An R package is available.

Boxplots for grouped and clustered data in toxicology.

The vast majority of toxicological papers summarize experimental data as bar charts of means with error bars. While these graphics are easy to generate, they often obscure essential features of the data, such as outliers or subgroups of individuals reacting differently to a treatment. In particular, raw values are of prime importance in toxicology; therefore, we argue they should not be hidden in messy supplementary tables but rather unveiled in neat graphics in the results section. We propose jittered boxplots as a very compact yet comprehensive and intuitively accessible way of visualizing grouped and clustered data from toxicological studies together with individual raw values and indications of statistical significance. A web application to create these plots is available online.