We describe a framework for defining pilot and feasibility studies focusing on studies conducted in preparation for a randomised controlled trial. To develop the framework, we undertook a Delphi survey; ran an open meeting at a trial methodology conference; conducted a review of definitions outside the health research context; consulted experts at an international consensus meeting; and reviewed 27 empirical pilot or feasibility studies. We initially adopted mutually exclusive definitions of pilot and feasibility studies. However, some Delphi survey respondents and the majority of open meeting attendees disagreed with the idea of mutually exclusive definitions. Their viewpoint was supported by definitions outside the health research context, the use of the terms ‘pilot’ and ‘feasibility’ in the literature, and participants at the international consensus meeting. In our framework, pilot studies are a subset of feasibility studies, rather than the two being mutually exclusive. A feasibility study asks whether something can be done, should we proceed with it, and if so, how. A pilot study asks the same questions but also has a specific design feature: in a pilot study a future study, or part of a future study, is conducted on a smaller scale. We suggest that to facilitate their identification, these studies should be clearly identified using the terms ‘feasibility’ or ‘pilot’ as appropriate. This should include feasibility studies that are largely qualitative; we found these difficult to identify in electronic searches because researchers rarely used the term ‘feasibility’ in the title or abstract of such studies. Investigators should also report appropriate objectives and methods related to feasibility; and give clear confirmation that their study is in preparation for a future randomised controlled trial designed to assess the effect of an intervention.

BackgroundIndividualised risk prediction is crucial if targeted pre-operative risk reduction strategies are to be deployed effectively. Radiologically determined sarcopenia has been shown to predict outcomes across a range of intra-abdominal pathologies. Access to pre-operative cross-sectional imaging has resulted in a number of studies investigating the predictive value of radiologically assessed sarcopenia over recent years. This systematic review and meta-analysis aimed to determine whether radiologically determined sarcopenia predicts post-operative morbidity and mortality following abdominal surgery.MethodCENTRAL, EMBASE and MEDLINE databases were searched using terms to capture the concept of radiologically assessed sarcopenia used to predict post-operative complications in abdominal surgery. Outcomes included 30 day post-operative morbidity and mortality, 1-, 3- and 5-year overall and disease-free survival and length of stay. Data were extracted and meta-analysed using either random or fixed effects model (Revman® 5.3).ResultsA total of 24 studies involving 5267 patients were included in the review. The presence of sarcopenia was associated with a significant increase in major post-operative complications (RR 1.61 95% CI 1.24–4.15 p = <0.00001) and 30-day mortality (RR 2.06 95% CI 1.02–4.17 p = 0.04). In addition, sarcopenia predicted 1-, 3- and 5-year survival (RR 1.61 95% CI 1.36–1.91 p = <0.0001, RR 1.45 95% CI 1.33–1.58 p = <0.0001, RR 1.25 95% CI 1.11–1.42 p = 0.0003, respectively) and 1- and 3-year disease-free survival (RR 1.30 95% CI 1.12–1.52 p = 0.0008).ConclusionPeri-operative cross-sectional imaging may be utilised in order to predict those at risk of complications following abdominal surgery. These findings should be interpreted in the context of retrospectively collected data and no universal sarcopenic threshold. Targeted prehabilitation strategies aiming to reverse sarcopenia may benefit patients undergoing abdominal surgery.Electronic supplementary materialThe online version of this article (doi:10.1007/s00268-017-3999-2) contains supplementary material, which is available to authorized users.

BackgroundFeasibility and pilot studies are essential components of planning or preparing for a larger randomized controlled trial (RCT). They are intended to provide useful information about the feasibility of the main RCT—with the goal of reducing uncertainty and thereby increasing the chance of successfully conducting the main RCT. However, research has shown that there are serious inadequacies in the reporting of pilot and feasibility studies. Reasons for this include a lack of explicit publication policies for pilot and feasibility studies in many journals, unclear definitions of what constitutes a pilot or feasibility RCT/study, and a lack of clarity in the objectives and methodological focus. All these suggest that there is an urgent need for new guidelines for reporting pilot and feasibility studies.ObjectivesThe aim of this paper is to describe the methods and processes in our development of an extension to the Consolidated Standards of Reporting Trials (CONSORT) Statement for reporting pilot and feasibility RCTs, that are executed in preparation for a future, more definitive RCT.Methods/designThere were five overlapping parts to the project: (i) the project launch—which involved establishing a working group and conducting a review of the literature; (ii) stakeholder engagement—which entailed consultation with the CONSORT group, journal editors and publishers, the clinical trials community, and funders; (iii) a Delphi process—used to assess the agreement of experts on initial definitions and to generate a reporting checklist for pilot RCTs, based on the 2010 CONSORT statement extension applicable to reporting pilot studies; (iv) a consensus meeting—to discuss, add, remove, or modify checklist items, with input from experts in the field; and (v) write-up and implementation—which included a guideline document which gives an explanation and elaboration (E&E) and which will provide advice for each item, together with examples of good reporting practice. This final part also included a plan for dissemination and publication of the guideline.ConclusionsWe anticipate that implementation of our guideline will improve the reporting completeness, transparency, and quality of pilot RCTs, and hence benefit several constituencies, including authors of journal manuscripts, funding agencies, educators, researchers, and end-users.

Missing data are present in the majority of cluster randomised trials. However, they are poorly reported, and most authors give little consideration to the assumptions under which their analysis will be valid. The majority of the methods currently used are valid under very strong assumptions about the missing data, whose plausibility is rarely discussed in the corresponding reports. This may have important consequences for the validity of inferences in some trials. Methods which result in valid inferences under general Missing-at-Random assumptions are available and should be made more accessible.

A crucial aspect of threshold-based extreme value analyses is the level at which the threshold is set. For a suitably high threshold asymptotic theory suggests that threshold excesses may be modelled by a generalized Pareto distribution. A common threshold diagnostic is a plot of estimates of the generalized Pareto shape parameter over a range of thresholds. The aim is to select the lowest threshold above which the estimates are judged to be approximately constant, taking into account sampling variability summarized by pointwise confidence intervals. This approach doesn't test directly the hypothesis that the underlying shape parameter is constant above a given threshold, but requires the user subjectively to combine information from many dependent estimates and confidence intervals. We develop tests of this hypothesis based on a multiple-threshold penultimate model that generalizes a two-threshold model proposed recently. One variant uses only the model fits from the traditional parameter stability plot. This is particularly beneficial when many datasets are analysed and enables assessment of the properties of the test on simulated data. We assess and illustrate these tests on river flow rate data and 72 series of significant wave heights.

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BackgroundThe clinical assessment of patients with chest pain of recent onset remains difficult. This study presents a critical review of clinical predictive tools for the assessment of patients with chest pain.MethodsSystematic review of observational studies and estimation of probabilities of coronary artery disease (CAD) in patients with chest pain. Searches were conducted in PubMed, Embase, Scopus, and Web of Science to identify studies reporting tools, with at least three variables from clinical history, physical examination or ECG, produced with multivariate analysis, to estimate probabilities of CAD in patients with chest pain of recent onset, published from inception of the database to the 31st July 2015. The references of previous relevant reviews were hand searched. The methodological quality was assessed with standard criteria. Since the incidence of CAD has changed in the past few decades, the date of publication was acknowledged to be relevant in order to use the tool in clinical practice, and more recent papers were considered more relevant. Probabilities of CAD according to the studies of highest quality were estimated and the evidence provided was graded.ResultsTwelve papers were included out of the 19126 references initially identified. The methodological quality of all of them was high. The clinical characteristics of the chest pain, age, past medical history of cardiovascular disease, gender, and abnormalities in the ECG were the predictors of CAD most commonly reported across the studies. The most recent papers, with highest methodological quality, and most practical for use in clinical settings, reported prediction or exclusion of CAD with area under the curve 0.90 in Primary Care, 0.91 in Emergency department, and 0.79 in Cardiology. These papers provide evidence of high level (1B) and the recommendation to use their results in the management of patients with chest pain is strong (A).ConclusionsThe risk of CAD can be estimated on clinical grounds in patients with chest pain in different clinical settings with high accuracy. The estimation of probabilities of CAD presented in these studies could be used for a better management of patients with chest pain and also in the development of future predictive tools.Electronic supplementary materialThe online version of this article (doi:10.1186/s12872-016-0196-4) contains supplementary material, which is available to authorized users.

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