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Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal websites."To maximise the benefit to society, you need to not just do research but do it well" Douglas G Altman. [ABSTRACT FROM AUTHOR]

Background: Selective reporting of outcomes in clinical trials is a serious problem. We aimed to investigate the influence of the peer review process within biomedical journals on reporting of primary outcome(s) and statistical analyses within reports of randomised trials.Methods: Each month, PubMed (May 2014 to April 2015) was searched to identify primary reports of randomised trials published in six high-impact general and 12 high-impact specialty journals. The corresponding author of each trial was invited to complete an online survey asking authors about changes made to their manuscript as part of the peer review process. Our main outcomes were to assess: (1) the nature and extent of changes as part of the peer review process, in relation to reporting of the primary outcome(s) and/or primary statistical analysis; (2) how often authors followed these requests; and (3) whether this was related to specific journal or trial characteristics.Results: Of 893 corresponding authors who were invited to take part in the online survey 258 (29%) responded. The majority of trials were multicentre (n = 191; 74%); median sample size 325 (IQR 138 to 1010). The primary outcome was clearly defined in 92% (n = 238), of which the direction of treatment effect was statistically significant in 49%. The majority responded (1-10 Likert scale) they were satisfied with the overall handling (mean 8.6, SD 1.5) and quality of peer review (mean 8.5, SD 1.5) of their manuscript. Only 3% (n = 8) said that the editor or peer reviewers had asked them to change or clarify the trial's primary outcome. However, 27% (n = 69) reported they were asked to change or clarify the statistical analysis of the primary outcome; most had fulfilled the request, the main motivation being to improve the statistical methods (n = 38; 55%) or avoid rejection (n = 30; 44%). Overall, there was little association between authors being asked to make this change and the type of journal, intervention, significance of the primary outcome, or funding source. Thirty-six percent (n = 94) of authors had been asked to include additional analyses that had not been included in the original manuscript; in 77% (n = 72) these were not pre-specified in the protocol. Twenty-three percent (n = 60) had been asked to modify their overall conclusion, usually (n = 53; 88%) to provide a more cautious conclusion.Conclusion: Overall, most changes, as a result of the peer review process, resulted in improvements to the published manuscript; there was little evidence of a negative impact in terms of post hoc changes of the primary outcome. However, some suggested changes might be considered inappropriate, such as unplanned additional analyses, and should be discouraged. [ABSTRACT FROM AUTHOR]

Background: Guidelines for assessing methodological and reporting quality of systematic reviews (SRs) were developed to contribute to implementing evidence-based health care and the reduction of research waste. As SRs assessing a cohort of SRs is becoming more prevalent in the literature and with the increased uptake of SR evidence for decision-making, methodological quality and standard of reporting of SRs is of interest. The objective of this study is to evaluate SR adherence to the Quality of Reporting of Meta-analyses (QUOROM) and PRISMA reporting guidelines and the A Measurement Tool to Assess Systematic Reviews (AMSTAR) and Overview Quality Assessment Questionnaire (OQAQ) quality assessment tools as evaluated in methodological overviews. Methods: The Cochrane Library, MEDLINE®, and EMBASE® databases were searched from January 1990 to October 2014. Title and abstract screening and full-text screening were conducted independently by two reviewers. Reports assessing the quality or reporting of a cohort of SRs of interventions using PRISMA, QUOROM, OQAQ, or AMSTAR were included. All results are reported as frequencies and percentages of reports and SRs respectively. Results: Of the 20,765 independent records retrieved from electronic searching, 1189 reports were reviewed for eligibility at full text, of which 56 reports (5371 SRs in total) evaluating the PRISMA, QUOROM, AMSTAR, and/or OQAQ tools were included. Notable items include the following: of the SRs using PRISMA, over 85% (1532/1741) provided a rationale for the review and less than 6% (102/1741) provided protocol information. For reports using QUOROM, only 9% (40/449) of SRs provided a trial flow diagram. However, 90% (402/449) described the explicit clinical problem and review rationale in the introduction section. Of reports using AMSTAR, 30% (534/1794) used duplicate study selection and data extraction. Conversely, 80% (1439/1794) of SRs provided study characteristics of included studies. In terms of OQAQ, 37% (499/1367) of the SRs assessed risk of bias (validity) in the included studies, while 80% (1112/1387) reported the criteria for study selection. Conclusions: Although reporting guidelines and quality assessment tools exist, reporting and methodological quality of SRs are inconsistent. Mechanisms to improve adherence to established reporting guidelines and methodological assessment tools are needed to improve the quality of SRs. [ABSTRACT FROM AUTHOR]

Background: The methodological quality and completeness of reporting of the systematic reviews (SRs) is fundamental to optimal implementation of evidence-based health care and the reduction of research waste. Methods exist to appraise SRs yet little is known about how they are used in SRs or where there are potential gaps in research best-practice guidance materials. The aims of this study are to identify reports assessing the methodological quality (MQ) and/or reporting quality (RQ) of a cohort of SRs and to assess their number, general characteristics, and approaches to 'quality' assessment over time. Methods: The Cochrane Library, MEDLINE®, and EMBASE® were searched from January 1990 to October 16, 2014, for reports assessing MQ and/or RQ of SRs. Title, abstract, and full-text screening of all reports were conducted independently by two reviewers. Reports assessing the MQ and/or RQ of a cohort of ten or more SRs of interventions were included. All results are reported as frequencies and percentages of reports. Results: Of 20,765 unique records retrieved, 1189 of them were reviewed for full-text review, of which 76 reports were included. Eight previously published approaches to assessing MQ or reporting guidelines used as proxy to assess RQ were used in 80% (61/76) of identified reports. These included two reporting guidelines (PRISMA and QUOROM) and five quality assessment tools (AMSTAR, R-AMSTAR, OQAQ, Mulrow, Sacks) and GRADE criteria. The remaining 24% (18/76) of reports developed their own criteria. PRISMA, OQAQ, and AMSTAR were the most commonly used published tools to assess MQ or RQ. In conjunction with other approaches, published tools were used in 29% (22/76) of reports, with 36% (8/22) assessing adherence to both PRISMA and AMSTAR criteria and 26% (6/22) using QUOROM and OQAQ. Conclusions: The methods used to assess quality of SRs are diverse, and none has become universally accepted. The most commonly used quality assessment tools are AMSTAR, OQAQ, and PRISMA. As new tools and guidelines are developed to improve both the MQ and RQ of SRs, authors of methodological studies are encouraged to put thoughtful consideration into the use of appropriate tools to assess quality and reporting. [ABSTRACT FROM AUTHOR]

Trials has 10 years of experience in providing open access publication of protocols for randomised controlled trials. In this editorial, the senior editors and editors-in-chief of Trials discuss editorial issues regarding managing trial protocol submissions, including the content and format of the protocol, timing of submission, approaches to tracking protocol amendments, and the purpose of peer reviewing a protocol submission. With the clarification and guidance provided, we hope we can make the process of publishing trial protocols more efficient and useful to trial investigators and readers. [ABSTRACT FROM AUTHOR]

Background: The CONSORT Statement is an evidence-informed guideline for reporting randomised controlled trials. A number of extensions have been developed that specify additional information to report for more complex trials. The aim of this study was to evaluate the impact of using a simple web-based tool (WebCONSORT, which incorporates a number of different CONSORT extensions) on the completeness of reporting of randomised trials published in biomedical publications. Methods: We conducted a parallel group randomised trial. Journals which endorsed the CONSORT Statement (i.e. referred to it in the Instruction to Authors) but do not actively implement it (i.e. require authors to submit a completed CONSORT checklist) were invited to participate. Authors of randomised trials were requested by the editor to use the web-based tool at the manuscript revision stage. Authors registering to use the tool were randomised (centralised computer generated) to WebCONSORT or control. In the WebCONSORT group, they had access to a tool allowing them to combine the different CONSORT extensions relevant to their trial and generate a customised checklist and flow diagram that they must submit to the editor. In the control group, authors had only access to a CONSORT flow diagram generator. Authors, journal editors, and outcome assessors were blinded to the allocation. The primary outcome was the proportion of CONSORT items (main and extensions) reported in each article post revision. Results: A total of 46 journals actively recruited authors into the trial (25 March 2013 to 22 September 2015); 324 author manuscripts were randomised (WebCONSORT n = 166; control n = 158), of which 197 were reports of randomised trials (n=94; n = 103). Over a third (39%; n = 127) of registered manuscripts were excluded from the analysis, mainly because the reported study was not a randomised trial. Of those included in the analysis, the most common CONSORT extensions selected were non-pharmacologic (n = 43; n = 50), pragmatic (n = 20; n = 16) and cluster (n=10; n=9). In a quarter of manuscripts, authors either wrongly selected an extension or failed to select the right extension when registering their manuscript on the WebCONSORT study site. Overall, there was no important difference in the overall mean score between WebCONSORT (mean score 0.51) and control (0.47) in the proportion of CONSORT and CONSORT extension items reported pertaining to a given study (mean difference, 0.04; 95% CI -0.02 to 0.10). Conclusions: This study failed to show a beneficial effect of a customised web-based CONSORT checklist to help authors prepare more complete trial reports. However, the exclusion of a large number of inappropriately registered manuscripts meant we had less precision than anticipated to detect a difference. Better education is needed, earlier in the publication process, for both authors and journal editorial staff on when and how to implement CONSORT and, in particular, CONSORT-related extensions. [ABSTRACT FROM AUTHOR]

Background: Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. Methods: The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. Results: The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. Conclusions: The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis. [ABSTRACT FROM AUTHOR]

Background: The CONsolidated Standards Of Reporting Trials (CONSORT) Statement provides a minimum standard set of items to be reported in published clinical trials; it has received widespread recognition within the biomedical publishing community. This research aims to provide an update on the endorsement of CONSORT by high impact medical journals.Methods: We performed a cross-sectional examination of the online "Instructions to Authors" of 168 high impact factor (2012) biomedical journals between July and December 2014. We assessed whether the text of the "Instructions to Authors" mentioned the CONSORT Statement and any CONSORT extensions, and we quantified the extent and nature of the journals' endorsements of these. These data were described by frequencies. We also determined whether journals mentioned trial registration and the International Committee of Medical Journal Editors (ICMJE; other than in regards to trial registration) and whether either of these was associated with CONSORT endorsement (relative risk and 95 % confidence interval). We compared our findings to the two previous iterations of this survey (in 2003 and 2007). We also identified the publishers of the included journals.Results: Sixty-three percent (106/168) of the included journals mentioned CONSORT in their "Instructions to Authors." Forty-four endorsers (42 %) explicitly stated that authors "must" use CONSORT to prepare their trial manuscript, 38 % required an accompanying completed CONSORT checklist as a condition of submission, and 39 % explicitly requested the inclusion of a flow diagram with the submission. CONSORT extensions were endorsed by very few journals. One hundred and thirty journals (77 %) mentioned ICMJE, and 106 (63 %) mentioned trial registration.Conclusions: The endorsement of CONSORT by high impact journals has increased over time; however, specific instructions on how CONSORT should be used by authors are inconsistent across journals and publishers. Publishers and journals should encourage authors to use CONSORT and set clear expectations for authors about compliance with CONSORT. [ABSTRACT FROM AUTHOR]

Contributing Reviewers: A peer-reviewed journal would not survive without the generous time and insightful comments of the reviewers, whose efforts often go unrecognized. Although final decisions are always editorial, they are greatly facilitated by the deeper technical knowledge, scientific insights, understanding of social consequences, and passion that reviewers bring to our deliberations. For these reasons, the Editors-in-Chief and staff of Trials warmly thank the 854 reviewers whose comments helped to shape the journal, for their invaluable assistance with review of manuscripts in Volume 16 (2015). [ABSTRACT FROM AUTHOR]

Background: The independent oversight of clinical trials, which is recommended by the Medical Research Council (MRC) Guidelines for Good Clinical Practice, is typically provided by an independent advisory Data Monitoring Committee (DMC) and an independent executive committee, to whom the DMC makes recommendations. The detailed roles and function of this executive committee, known as the Trial Steering Committee (TSC), have not previously been studied or reviewed since those originally proposed by the MRC in 1998.Methods: An expert panel (n = 7) was convened comprising statisticians, clinicians and trial methodologists with prior TSC experience. Twelve questions about the role and responsibilities of the TSC were discussed by the panel at two full-day meetings. Each meeting was transcribed in full and the discussions were summarised.Results: The expert panel reached agreement on the role of the TSC, to which it was accountable, the membership, the definition of independence, and the experience and training needed. The management of ethical issues, difficult/complex situations and issues the TSC should not ask the DMC to make recommendations on were more difficult to discuss without specific examples, but support existed for further work to help share issues and to provide appropriate training for TSC members. Additional topics discussed, which had not been identified by previous work relating to the DMCs but were pertinent to the role of the TSC, included the following: review of data sharing requests, indemnity, lifespan of the TSC, general TSC administration, and the roles of both the Funder and the Sponsor.Conclusions: This paper presents recommendations that will contribute to the revision and update of the MRC TSC terms of reference. Uncertainty remains in some areas due to the absence of real-life examples; future guidance on these issues would benefit from a repository of case studies. Notably, the role of a patient and public involvement (PPI) contributor was not discussed, and further work is warranted to explore the role of a PPI contributor in independent trial oversight. [ABSTRACT FROM AUTHOR]

Background: Incomplete reporting is a frequent waste in research. Our aim was to evaluate the impact of a writing aid tool (WAT) based on the CONSORT statement and its extension for non-pharmacologic treatments on the completeness of reporting of randomized controlled trials (RCTs).Methods: We performed a 'split-manuscript' RCT with blinded outcome assessment. Participants were masters and doctoral students in public health. They were asked to write, over a 4-hour period, the methods section of a manuscript based on a real RCT protocol, with a different protocol provided to each participant. Methods sections were divided into six different domains: 'trial design', 'randomization', 'blinding', 'participants', 'interventions', and 'outcomes'. Participants had to draft all six domains with access to the WAT for a random three of six domains. The random sequence was computer-generated and concealed. For each domain, the WAT comprised reminders of the corresponding CONSORT item(s), bullet points detailing all the key elements to be reported, and examples of good reporting. The control intervention consisted of no reminders. The primary outcome was the mean global score for completeness of reporting (scale 0-10) for all domains written with or without the WAT.Results: Forty-one participants wrote 41 different manuscripts of RCT methods sections, corresponding to 246 domains (six for each of the 41 protocols). All domains were analyzed. For the primary outcome, the mean (SD) global score for completeness of reporting was higher with than without use of the WAT: 7.1 (1.2) versus 5.0 (1.6), with a mean (95 % CI) difference 2.1 (1.5-2.7; P <0.01). Completeness of reporting was significantly higher with the WAT for all domains except for blinding and outcomes.Conclusion: Use of the WAT could improve the completeness of manuscripts reporting the results of RCTs.Trial Registration: Clinicaltrials.gov ( http://clinicaltrials.gov NCT02127567 , registration date first received April 29, 2014). [ABSTRACT FROM AUTHOR]

Background: Core outcome sets can increase the efficiency and value of research and, as a result, there are an increasing number of studies looking to develop core outcome sets (COS). However, the credibility of a COS depends on both the use of sound methodology in its development and clear and transparent reporting of the processes adopted. To date there is no reporting guideline for reporting COS studies. The aim of this programme of research is to develop a reporting guideline for studies developing COS and to highlight some of the important methodological considerations in the process. Methods/Design: The study will include a reporting guideline item generation stage which will then be used in a Delphi study. The Delphi study is anticipated to include two rounds. The first round will ask stakeholders to score the items listed and to add any new items they think are relevant. In the second round of the process, participants will be shown the distribution of scores for all stakeholder groups separately and asked to re-score. A final consensus meeting will be held with an expert panel and stakeholder representatives to review the guideline item list. Following the consensus meeting, a reporting guideline will be drafted and review and testing will be undertaken until the guideline is finalised. The final outcome will be the COS-STAR (Core Outcome Set-STAndards for Reporting) guideline for studies developing COS and a supporting explanatory document. Discussion: To assess the credibility and usefulness of a COS, readers of a COS development report need complete, clear and transparent information on its methodology and proposed core set of outcomes. The COS-STAR guideline will potentially benefit all stakeholders in COS development: COS developers, COS users, e.g. trialists and systematic reviewers, journal editors, policy-makers and patient groups. Discussion: To assess the credibility and usefulness of a COS, readers of a COS development report need complete, clear and transparent information on its methodology and proposed core set of outcomes. The COS-STAR guideline will potentially benefit all stakeholders in COS development: COS developers, COS users, e.g. trialists and systematic reviewers, journal editors, policy-makers and patient groups. [ABSTRACT FROM AUTHOR]

The quality of reporting clinical and preclinical research is not optimal. Reporting guidelines can help make reports of research more complete and transparent, thus increasing their value and making them more useful to all readers. Getting reporting guidelines into practice is complex and expensive, and involves several stakeholders, including prospective authors, peer reviewers, journal editors, guideline developers, and implementation scientists. Working together will help ensure their maximum uptake and penetration. We are all responsible for helping to ensure that all research is reported so completely that it is of value to everybody. [ABSTRACT FROM AUTHOR]

Background: Randomized controlled trials (RCTs) often have more than one primary outcome and frequently have secondary and harm outcomes. Comparison of outcomes between study arms is the primary focus of RCTs, but there are times when the relation between outcomes is important, such as determining whether an intermediate outcome and a clinical outcome have a strong association. We sought to determine how often reports of RCTs depict the relations among outcomes at the individual patient level and, for those studies that use composite outcomes, how often the relations between component elements are depicted. Methods: We selected 20 general, specialty and subspecialty medical journals with high impact factors that publish original clinical research. We identified every RCT in the 2011 and 2012 issues and randomly selected 10 articles per journal. For each article we recorded the number of outcomes, the number of composite outcomes and how often the relations between outcomes or elements of composite outcomes were portrayed. Results: All but 16 of the 200 RCTs had more than one outcome. Thus, outcomes could have been related in 92% of studies, but such relations were only reported in 2 (1%). A total of 33 (17%) investigations measured a composite outcome, 32 of which showed data for each component. None, however, showed cross-tabulation of the components. Conclusions: Readers are rarely shown the relation between outcomes. Mandatory posting of datasets or requirements for detailed appendices would allow readers to see these cross-tabulations, helping future investigators know which outcomes are redundant, which provide unique information and which are most responsive to changes in the independent variables. While not every relationship between outcomes requires depiction, at present such information is seldom portrayed. [ABSTRACT FROM AUTHOR]

The Core Outcome Measures in Effectiveness Trials (COMET) Initiative database is an international repository of studies relevant to the development of core outcome sets. By the end of 2013, it included a unique collection of 306 studies. The website is increasingly being used, with more than 12,000 visits in 2013 (a 55% increase over 2012), 8,369 unique visitors (a 53% increase) and 6,844 new visitors (a 48% increase). There has been a rise in visits from outside the United Kingdom, with 2,405 such visits in 2013 (30% of all visits). By December 2013, a total of 4,205 searches had been completed, with 2,139 in 2013 alone. [ABSTRACT FROM AUTHOR]

Background Before considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the model (referred to as external validation). We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models. Methods We conducted a systematic review of articles describing some form of external validation of one or more multivariable prediction models indexed in PubMed core clinical journals published in 2010. Study data were extracted in duplicate on design, sample size, handling of missing data, reference to the original study developing the prediction models and predictive performance measures. Results 11,826 articles were identified and 78 were included for full review, which described the evaluation of 120 prediction models. in participant data that were not used to develop the model. Thirty-three articles described both the development of a prediction model and an evaluation of its performance on a separate dataset, and 45 articles described only the evaluation of an existing published prediction model on another dataset. Fifty-seven percent of the prediction models were presented and evaluated as simplified scoring systems. Sixteen percent of articles failed to report the number of outcome events in the validation datasets. Fifty-four percent of studies made no explicit mention of missing data. Sixty-seven percent did not report evaluating model calibration whilst most studies evaluated model discrimination. It was often unclear whether the reported performance measures were for the full regression model or for the simplified models. Conclusions The vast majority of studies describing some form of external validation of a multivariable prediction model were poorly reported with key details frequently not presented. The validation studies were characterised by poor design, inappropriate handling and acknowledgement of missing data and one of the most key performance measures of prediction models i.e. calibration often omitted from the publication. It may therefore not be surprising that an overwhelming majority of developed prediction models are not used in practice, when there is a dearth of well-conducted and clearly reported (external validation) studies describing their performance on independent participant data. [ABSTRACT FROM AUTHOR]

Background: A case report is a narrative that describes, for medical, scientific, or educational purposes, a medical problem experienced by one or more patients. Case reports written without guidance from reporting standards are insufficiently rigorous to guide clinical practice or to inform clinical study design. Primary Objective. Develop, disseminate, and implement systematic reporting guidelines for case reports. Methods: We used a three-phase consensus process consisting of (1) pre-meeting literature review and interviews to generate items for the reporting guidelines, (2) a face-to-face consensus meeting to draft the reporting guidelines, and (3) post-meeting feedback, review, and pilot testing, followed by finalization of the case report guidelines. Results: This consensus process involved 27 participants and resulted in a 13-item checklist-a reporting guideline for case reports. The primary items of the checklist are title, key words, abstract, introduction, patient information, clinical findings, timeline, diagnostic assessment, therapeutic interventions, follow-up and outcomes, discussion, patient perspective, and informed consent. Conclusions: We believe the implementation of the CARE (CAse REport) guidelines by medical journals will improve the completeness and transparency of published case reports and that the systematic aggregation of information from case reports will inform clinical study design, provide early signals of effectiveness and harms, and improve healthcare delivery. [ABSTRACT FROM AUTHOR]

Background: For a randomized trial, the primary publication is usually the one which reports the results of the primary outcome and provides consolidated data from all study centers. Other aspects of a randomized trial's findings (that is, non-primary results) are often reported in subsequent publications. Methods: We carried out a cross-sectional review of the characteristics and type of information reported in non-primary reports (n = 69) of randomized trials (indexed in PubMed core clinical journals in 2009) and whether they report pre-specified or exploratory analyses. We also compared consistency of information in non-primary publications with that reported in the primary publication. Results: The majority (n = 56; 81%) of non-primary publications were large, multicenter trials, published in specialty journals. Most reported subgroup analyses (n = 27; 39%), analyzing a specific subgroup of patients from the randomized trial, or reported on secondary outcomes (n = 29; 42%); 19% (n = 13) reported extended follow-up. Less than half reported details of trial registration (n = 30; 43%) or the trial protocol (n = 27; 39%) and in 41% (n = 28) it was unclear from reading the abstract that the report was not the primary publication for the trial. Non-primary publications often analyzed and reported multiple different outcomes (16% reported >20 outcomes) and in 10% (n = 7) it was unclear how many outcomes had actually been assessed; in 42% (n = 29) it was unclear whether the analyses reported were pre-specified or exploratory. Only 39% (n = 27) of non-primary publications described the primary outcome of the randomized trial, 6% (n = 4) reported its numerical results and 9% (n = 6) details of how participants were randomized. Conclusion: Non-primary publications often lack important information about the randomized trial and the type of analyses conducted and whether these were pre-specified or exploratory to enable readers to accurately identify and assess the validity and reliably of the study findings. We provide recommendations for what information authors should include in non-primary reports of randomized trials. [ABSTRACT FROM AUTHOR]

Background: A prognostic model should not enter clinical practice unless it has been demonstrated that it performs a useful role. External validation denotes evaluation of model performance in a sample independent of that used to develop the model. Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature. Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample. Validating Cox models is not straightforward because event probabilities are estimated relative to an unspecified baseline function. Methods: We describe statistical approaches to external validation of a published Cox model according to the level of published information, specifically (1) the prognostic index only, (2) the prognostic index together with Kaplan-Meier curves for risk groups, and (3) the first two plus the baseline survival curve (the estimated survival function at the mean prognostic index across the sample). The most challenging task, requiring level 3 information, is assessing calibration, for which we suggest a method of approximating the baseline survival function. Results: We apply the methods to two comparable datasets in primary breast cancer, treating one as derivation and the other as validation sample. Results are presented for discrimination and calibration. We demonstrate plots of survival probabilities that can assist model evaluation. Conclusions: Our validation methods are applicable to a wide range of prognostic studies and provide researchers with a toolkit for external validation of a published Cox model. [ABSTRACT FROM AUTHOR]

Background Fetal ultrasound scanning is considered vital for routine antenatal care with first trimester scans recommended for accurate estimation of gestational age (GA). A reliable estimate of gestational age is key information underpinning clinical care and allows estimation of expected date of delivery. Fetal crown-rump length (CRL) is recommended over last menstrual period for estimating GA when measured in early pregnancy i.e. 9+0-13+6 weeks. Methods The INTERGROWTH-21st Project is the largest prospective study to collect data on CRL in geographically diverse populations and with a high level of quality control measures in place. We aim to develop a new gestational age estimation equation based on the crown-rump length (CRL) from women recruited between 9+0-13+6 weeks. The main statistical challenge is modelling data when the outcome variable (GA) is truncated at both ends, i.e. at 9 and 14 weeks. We explored three alternative statistical approaches to overcome the truncation of GA. To evaluate these strategies we generated a data set with no truncation of GA that was similar to the INTERGROWTH-21st Project CRL data, which we used to explore the performance of different methods of analysis of these data when we imposed truncation at 9 and 14 weeks of gestation. These 3 methods were first tested in a simulation based study using a previously published dating equation by Verburg et al. and evaluated how well each of them performed in relation to the model from which the data were generated. After evaluating the 3 approaches using simulated data based on the Verburg equations, the best approach will be applied to the INTERGROWTH-21st Project data to estimate GA from CRL. Results Results of these rather "ad hoc" statistical methods correspond very closely to the "real data" for Verburg, a data set that is similar to the INTERGROWTH-21st project CRL data set. Conclusions We are confident that we can use these approaches to get reliable estimates based on INTERGROWTH-21st Project CRL data. These approaches may be a solution to other truncation problems involving similar data though their application to other settings would need to be evaluated. [ABSTRACT FROM AUTHOR]

The selection of appropriate outcomes or domains is crucial when designing clinical trials in order to compare directly the effects of different interventions in ways that minimize bias. If the findings are to influence policy and practice then the chosen outcomes need to be relevant and important to key stakeholders including patients and the public, health care professionals and others making decisions about health care. There is a growing recognition that insufficient attention has been paid to the outcomes measured in clinical trials. These issues could be addressed through the development and use of an agreed standardized collection of outcomes, known as a core outcome set, which should be measured and reported, as a minimum, in all trials for a specific clinical area. Accumulating work in this area has identified the need for general guidance on the development of core outcome sets. Key issues to consider in the development of a core outcome set include its scope, the stakeholder groups to involve, choice of consensus method and the achievement of a consensus. [ABSTRACT FROM AUTHOR]

Background: Maintenance of the blind-to-treatment allocation is one of the most important means of avoiding bias in randomised controlled clinical trials. Commonly used methodologies to determine whether patients have become unblinded to treatment allocation are imperfect. This may be of particular concern in studies where outcomes are patient-reported, and with products which have a characteristic adverse event profile. We report the results of an evidence-based statistical approach to exploring the possible impact of unblinding to a cannabis-based medicine (SativexW) in people with muscle spasticity due to multiple sclerosis. Methods: All 666 patients included in three Phase III placebo-controlled studies were included in this analysis. The relationship between factors that might permit patients to identify their treatment allocation and the effect of treatment on the self-reported primary outcome measure was investigated using a general linear model where the dependent variable was the change from baseline in patient self-reported spasticity severity, and the various possible explanatory factors were regarded as fixed factors in the model. Results: There was no significant relationship between the effect of SativexW on spasticity and the prior use of cannabis or the incidence of 'typical' adverse events. Nor was there any significant relationship between the prior use of cannabis and the incidence of 'typical' adverse events, nor between prior use of cannabis and dose of SativexW. Conclusions: There is no evidence to suggest that there was widespread unblinding to treatment allocation in these three studies. If any patients did become unblinded, then there is no evidence that this led to bias in the assessment of the treatment difference between SativexW and Placebo for efficacy, adverse events or study drug dosing. This methodology may be suitable for assessment of the integrity of the blind in other randomized clinical trials [ABSTRACT FROM AUTHOR]

The CONSORT statement is used worldwide to improve the reporting of randomised controlled trials. Kenneth Schulz and colleagues describe the latest version, CONSORT 2010, which updates the reporting guideline based on new methodological evidence and accumulating experience. To encourage dissemination of the CONSORT 2010 Statement, this article is freely accessible on bmj.com and will also be published in the Lancet, Obstetrics and Gynecology, PLoS Medicine, Annals of Internal Medicine, Open Medicine, Journal of Clinical Epidemiology, BMC Medicine, and Trials. [ABSTRACT FROM AUTHOR]

Background: The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model. Methods: Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500 replications. Five levels of missingness (ranging from 5% to 75%) were imposed on three covariates using a missing at random (MAR) mechanism. Five missing data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) multiple imputation using regression switching imputation, d) multiple imputation using regression switching with predictive mean matching (MICE-PMM) and e) multiple imputation using flexible additive imputation models. A Cox proportional hazards model was fitted to each dataset and estimates for the regression coefficients and model performance measures obtained. Results: CC produced biased regression coefficient estimates and inflated standard errors (SEs) with 25% or more missingness. The underestimated SE after SI resulted in poor coverage with 25% or more missingness. Of the MI approaches investigated, MI using MICE-PMM produced the least biased estimates and better model performance measures. However, this MI approach still produced biased regression coefficient estimates with 75% missingness. Conclusions: Very few differences were seen between the results from all missing data approaches with 5% missingness. However, performing MI using MICE-PMM may be the preferred missing data approach for handling between 10% and 50% MAR missingness. [ABSTRACT FROM AUTHOR]

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the use and reporting of adjusted analysis in randomised controlled trials (RCTs) and compare the quality of reporting before and after the revision of the CONSORT Statement in 2001. Design: Comparison of two cross sectional samples of published articles. Data Sources: Journal articles indexed on PubMed in December 2000 and December 2006. Study Selection: Parallel group RCTs with a full publication carried out in humans and published in English Main outcome measures: Proportion of articles reported adjusted analysis; use of adjusted analysis; the reason for adjustment; the method of adjustment and the reporting of adjusted analysis results in the main text and abstract. Results: In both cohorts, 25% of studies reported adjusted analysis (84/355 in 2000 vs 113/422 in 2006). Compared with articles reporting only unadjusted analyses, articles that reported adjusted analyses were more likely to specify primary outcomes, involve multiple centers, perform stratified randomization, be published in general medical journals, and recruit larger sample sizes. In both years a minority of articles explained why and how covariates were selected for adjustment (20% to 30%). Almost all articles specified the statistical methods used for adjustment (99% in 2000 vs 100% in 2006) but only 5% and 10%, respectively, reported both adjusted and unadjusted results as recommended in the CONSORT guidelines. Conclusion: There was no evidence of change in the reporting of adjusted analysis results five years after the revision of the CONSORT Statement and only a few articles adhered fully to the CONSORT recommendations. [ABSTRACT FROM AUTHOR]

In recognition of the benefits of transparent reporting, many peer-reviewed journals require that their authors be prepared to share their raw, unprocessed data with other scientists and/or state the availability of raw data in published articles. But little information on how data should be prepared for publication - or sharing - has emerged. In clinical research patient privacy and consent for use of personal health information are key considerations, but agreed-upon definitions of what constitutes anonymised patient information do not appear to have been established. We aim to address this issue by providing practical guidance for those involved in the publication process, by proposing a minimum standard for de-identifying datasets for the purposes of publication in a peer-reviewed biomedical journal, or sharing with other researchers. Basic advice on file preparation is provided along with procedural guidance on prospective and retrospective publication of raw data, with an emphasis on randomised controlled trials. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal web sites. [ABSTRACT FROM AUTHOR]

Background: Appropriate choice and use of prognostic models in clinical practice require the use of good methods for both model development, and for developing prognostic indices and risk groups from the models. In order to assess reliability and generalizability for use, models need to have been validated and measures of model performance reported. We reviewed published articles to assess the methods and reporting used to develop and evaluate performance of prognostic indices and risk groups from prognostic models. Methods: We developed a systematic search string and identified articles from PubMed. Forty-seven articles were included that satisfied the following inclusion criteria: published in 2005; aiming to predict patient outcome; presenting new prognostic models in cancer with outcome time to an event and including a combination of at least two separate variables; and analysing data using multivariable analysis suitable for time to event data. Results: In 47 studies, Cox models were used in 94% (44), but the coefficients or hazard ratios for the variables in the final model were reported in only 72% (34). The reproducibility of the derived model was assessed in only 11% (5) of the articles. A prognostic index was developed from the model in 81% (38) of the articles, but researchers derived the prognostic index from the final prognostic model in only 34% (13) of the studies; different coefficients or variables from those in the final model were used in 50% (19) of models and the methods used were unclear in 16% (6) of the articles. Methods used to derive prognostic groups were also poor, with researchers not reporting the methods used in 39% (14 of 36) of the studies and data derived methods likely to bias estimates of differences between risk groups being used in 28% (10) of the studies. Validation of their models was reported in only 34% (16) of the studies. In 15 studies validation used data from the same population and in five studies from a different population. Including reports of validation with external data from publications up to four years following model development, external validation was attempted for only 21% (10) of models. Insufficient information was provided on the performance of models in terms of discrimination and calibration. Conclusions: Many published prognostic models have been developed using poor methods and many with poor reporting, both of which compromise the reliability and clinical relevance of models, prognostic indices and risk groups derived from them. [ABSTRACT FROM AUTHOR]

Background: Development of prognostic models enables identification of variables that are influential in predicting patient outcome and the use of these multiple risk factors in a systematic, reproducible way according to evidence based methods. The reliability of models depends on informed use of statistical methods, in combination with prior knowledge of disease. We reviewed published articles to assess reporting and methods used to develop new prognostic models in cancer. Methods: We developed a systematic search string and identified articles from PubMed. Forty-seven articles were included that satisfied the following inclusion criteria: published in 2005; aiming to predict patient outcome; presenting new prognostic models in cancer with outcome time to an event and including a combination of at least two separate variables; and analysing data using multivariable analysis suitable for time to event data. Results: In 47 studies, prospective cohort or randomised controlled trial data were used for model development in only 33% (15) of studies. In 30% (14) of the studies insufficient data were available, having fewer than 10 events per variable (EPV) used in model development. EPV could not be calculated in a further 40% (19) of the studies. The coding of candidate variables was only reported in 68% (32) of the studies. Although use of continuous variables was reported in all studies, only one article reported using recommended methods of retaining all these variables as continuous without categorisation. Statistical methods for selection of variables in the multivariate modelling were often flawed. A method that is not recommended, namely, using statistical significance in univariate analysis as a pre-screening test to select variables for inclusion in the multivariate model, was applied in 48% (21) of the studies. Conclusions: We found that published prognostic models are often characterised by both use of inappropriate methods for development of multivariable models and poor reporting. In addition, models are limited by the lack of studies based on prospective data of sufficient sample size to avoid overfitting. The use of poor methods compromises the reliability of prognostic models developed to provide objective probability estimates to complement clinical intuition of the physician and guidelines. [ABSTRACT FROM AUTHOR]

Although current electronic methods of scientific publishing offer increased opportunities for publishing all research studies and describing them in sufficient detail, health research literature still suffers from many shortcomings. These shortcomings seriously undermine the value and utility of the literature and waste scarce resources invested in the research. In recent years there have been several positive steps aimed at improving this situation, such as a strengthening of journals' policies on research publication and the wide requirement to register clinical trials. The EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network is an international initiative set up to advance high quality reporting of health research studies; it promotes good reporting practices including the wider implementation of reporting guidelines. EQUATOR provides free online resources http://www.equatornetwork. org supported by education and training activities and assists in the development of robust reporting guidelines. This paper outlines EQUATOR's goals and activities and offers suggestions for organizations and individuals involved in health research on how to strengthen research reporting. [ABSTRACT FROM AUTHOR]

Many research-funding agencies now require open access to the results of research they have funded, and some also require that researchers make available the raw data generated from that research. Similarly, the journal Trials aims to address inadequate reporting in randomised controlled trials, and in order to fulfil this objective, the journal is working with the scientific and publishing communities to try to establish best practice for publishing raw data from clinical trials in peerreviewed biomedical journals. Common issues encountered when considering raw data for publication include patient privacy - unless explicit consent for publication is obtained - and ownership, but agreed-upon policies for tackling these concerns do not appear to be addressed in the guidance or mandates currently established. Potential next steps for journal editors and publishers, ethics committees, research-funding agencies, and researchers are proposed, and alternatives to journal publication, such as restricted access repositories, are outlined. [ABSTRACT FROM AUTHOR]

Objective: Although crossover trials enjoy wide use, standards for analysis and reporting have not been established. We reviewed methodological aspects and quality of reporting in a representative sample of published crossover trials. Methods: We searched MEDLINE for December 2000 and identified all randomized crossover trials. We abstracted data independently, in duplicate, on 14 design criteria, 13 analysis criteria, and 14 criteria assessing the data presentation. Results: We identified 526 randomized controlled trials, of which 116 were crossover trials. Trials were drug efficacy (48%), pharmacokinetic (28%), and nonpharmacologic (30%). The median sample size was 15 (interquartile range 8-38). Most (72%) trials used 2 treatments and had 2 periods (64%). Few trials reported allocation concealment (17%) or sequence generation (7%). Only 20% of trials reported a sample size calculation and only 31% of these considered pairing of data in the calculation. Carry-over issues were addressed in 29% of trial's methods. Most trials reported and defended a washout period (70%). Almost all trials (93%) tested for treatment effects using paired data and also presented details on by-group results (95%). Only 29% presented CIs or SE so that data could be entered into a meta-analysis. Conclusion: Reports of crossover trials frequently omit important methodological issues in design, analysis, and presentation. Guidelines for the conduct and reporting of crossover trials might improve the conduct and reporting of studies using this important trial design. [ABSTRACT FROM AUTHOR]

Background: The CONSORT Statement provides recommendations for reporting randomized controlled trials. We assessed the extent to which leading medical journals that publish reports of randomized trials incorporate the CONSORT recommendations into their journal and editorial processes. Methods: This article reports on two observational studies. Study 1: We examined the online version of 'Instructions to Authors' for 165 high impact factor medical journals and extracted all text mentioning the CONSORT Statement or CONSORT extension papers. Any mention of the International Committee of Medical Journal Editors (ICMJE) or clinical trial registration were also sought and extracted. Study 2: We surveyed the editor-in-chief, or editorial office, for each of the 165 journals about their journal's endorsement of CONSORT recommendations and its incorporation into their editorial and peer-review processes. Results: Study 1: Thirty-eight percent (62/165) of journals mentioned the CONSORT Statement in their online 'Instructions to Authors'; of these 37% (23/62) stated this was a requirement, 63% (39/62) were less clear in their recommendations. Very few journals mentioned the CONSORT extension papers. Journals that referred to CONSORT were more likely to refer to ICMJE guidelines (RR 2.16; 95% CI 1.51 to 3.08) and clinical trial registration (RR 3.67; 95% CI 2.36 to 5.71) than those journals which did not. Study 2: Thirty-nine percent (64/165) of journals responded to the on-line survey, the majority were journal editors. Eighty-eight percent (50/57) of journals recommended authors comply with the CONSORT Statement; 62% (35/56) said they would require this. Forty-one percent (22/53) reported incorporating CONSORT into their peer-review process and 47% (25/53) into their editorial process. Eighty-one percent (47/58) reported including CONSORT in their 'Instructions to Authors' although there was some inconsistency when cross checking information on the journal's website. Sixty-nine percent (31/45) of journals recommended authors comply with the CONSORT extension for cluster trials, 60% (27/45) for harms and 42% (19/45) for non-inferiority and equivalence trials. Few journals mentioned these extensions in their 'Instructions to Authors'. Conclusion: Journals should be more explicit in their recommendations and expectations of authors regarding the CONSORT Statement and related CONSORT extensions papers. [ABSTRACT FROM AUTHOR]

This editorial introduces the new online, open access, peer-reviewed journal Trials. The journal considers manuscripts on any aspect of the design, performance, and findings of randomised controlled trials in any discipline related to health care, and also encourages the publication of protocols. Trialists will be able to provide the necessary detail for a true and complete scientific record. They will be able to communicate not only all outcome measures, as well as varying analyses and interpretations, but also in-depth descriptions of what they did and honest reflections about what they learnt. Trials also encourages articles covering generic issues related to trials, for example focussing on the design, conduct, analysis, interpretation, or reporting. [ABSTRACT FROM AUTHOR]

Background: Standard coronary artery bypass graft surgery uses a single internal mammary artery and supplemental vein or radial artery grafts. Several observational studies have suggested a survival benefit with two internal mammary artery grafts compared to a single internal mammary artery graft, but this has not been tested in a randomised trial. The Arterial Revascularisation Trial is a Medical Research Council and British Heart Foundation funded, multi-centre international trial comparing single internal mammary artery grafting versus bilateral internal mammary artery grafting. Methods/Design: Twenty centres in the UK, Australia, Poland and Brazil are planning to randomise 3000 coronary artery bypass graft surgery patients to single or bilateral internal mammary artery grafting. Supplemental grafts may be either saphenous vein or radial artery. Coronary artery bypass grafting can be performed as an on-pump or off-pump procedure. The primary outcome is survival at 10 years and secondary end-points include clinical events, quality of life and cost effectiveness. The effect of age, left ventricular function, diabetes, number of grafts, vein grafts and off-pump surgery are pre-specified subgroups. Discussion: The Arterial Revascularisation Trial is one of the first randomised trials to evaluate the effects on survival and other clinical outcomes of single internal mammary artery grafting versus bilateral internal mammary artery grafting, and will help to establish the best approach for patients requiring coronary artery bypass graft surgery [ABSTRACT FROM AUTHOR]

Background: Systematic reviews or randomised-controlled trials usually help to establish the effectiveness of drugs and other health technologies, but are rarely sufficient by themselves to ensure actual clinical use of the technology. The process from innovation to routine clinical use is complex. Numerous computerised decision support systems (DSS) have been developed, but many fail to be taken up into actual use. Some developers construct technologically advanced systems with little relevance to the real world. Others did not determine whether a clinical need exists. With NHS investing £5 billion in computer systems, also occurring in other countries, there is an urgent need to shift from a technology-driven approach to one that identifies and employs the most cost-effective method to manage knowledge, regardless of the technology. The generic term, 'decision tool' (DT), is therefore suggested to demonstrate that these aids, which seem different technically, are conceptually the same from a clinical viewpoint. Discussion: Many computerised DSSs failed for various reasons, for example, they were not based on best available knowledge; there was insufficient emphasis on their need for high quality clinical data; their development was technology-led; or evaluation methods were misapplied. We argue that DSSs and other computer-based, paper-based and even mechanical decision aids are members of a wider family of decision tools. A DT is an active knowledge resource that uses patient data to generate case specific advice, which supports decision making about individual patients by health professionals, the patients themselves or others concerned about them. The identification of DTs as a consistent and important category of health technology should encourage the sharing of lessons between DT developers and users and reduce the frequency of decision tool projects focusing only on technology. The focus of evaluation should become more clinical, with the impact of computer-based DTs being evaluated against other computer, paper- or mechanical tools, to identify the most cost effective tool for each clinical problem. Summary: We suggested the generic term 'decision tool' to demonstrate that decision-making aids, such as computerised DSSs, paper algorithms, and reminders are conceptually the same, so the methods to evaluate them should be the same. [ABSTRACT FROM AUTHOR]

The author reflects methods of publishing research articles in the journals. Topics discussed include challenges for readers in finding details of study methods and the results of the study; developing text mining software to locate the relevant information and including information about the methodology to allow others to replicate the study, and adoption of the IMRAD format (Introduction, Methods, Results and Discussion).