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Evidence shows that the quality of reporting of randomised controlled trials (RCTs) is not optimal. The lack of transparent reporting impedes readers from judging the reliability and validity of trial findings and researchers from extracting information for systematic reviews and results in research waste. The Consolidated Standards of Reporting Trials (CONSORT) statement was developed to improve the reporting of RCTs. Within person trials are used for conditions that can affect two or more body sites, and are a useful and efficient tool because the comparisons between interventions are within people. Such trials are most commonly conducted in ophthalmology, dentistry, and dermatology. The reporting of within person trials has, however, been variable and incomplete, hindering their use in clinical decision making and by future researchers. This document presents the CONSORT extension to within person trials. It aims to facilitate the reporting of these trials. It extends 16 items of the CONSORT 2010 checklist and introduces a modified flowchart and baseline table to enhance transparency. Examples of good reporting and evidence based rationale for CONSORT within person checklist items are provided.

The Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) were developed to address widespread deficiencies in the reporting of such studies. The REMARK checklist consists of 20 items to report for published tumor marker prognostic studies. A detailed paper was published explaining the rationale behind checklist items, providing positive examples and giving empirical evidence of the quality of reporting. REMARK provides a comprehensive overview to educate on good reporting and provide a valuable reference for the many issues to consider when designing, conducting, and analyzing tumor marker studies and prognostic studies in medicine in general. Despite support for REMARK from major cancer journals, prognostic factor research studies remain poorly reported. To encourage dissemination and uptake of REMARK, we have produced this considerably abridged version of the detailed explanatory manuscript, which may also serve as a brief guide to key issues for investigators planning tumor marker prognostic studies. To summarize the current situation, more recent papers investigating the quality of reporting and related reporting guidelines are cited, but otherwise the literature is not updated. Another important impetus for this paper is that it serves as a basis for literal translations into other languages. Translations will help to bring key information to a larger audience world-wide. Many more details can be found in the original paper.

Aims Recent guidelines recommend that patients with heart failure and left ventricular ejection fraction (LVEF) 40–49% should be managed similar to LVEF ≥ 50%. We investigated the effect of beta-blockers according to LVEF in double-blind, randomized, placebo-controlled trials. Methods and results Individual patient data meta-analysis of 11 trials, stratified by baseline LVEF and heart rhythm (Clinicaltrials.gov: NCT0083244; PROSPERO: CRD42014010012). Primary outcomes were all-cause mortality and cardiovascular death over 1.3 years median follow-up, with an intention-to-treat analysis. For 14 262 patients in sinus rhythm, median LVEF was 27% (interquartile range 21–33%), including 575 patients with LVEF 40–49% and 244 ≥ 50%. Beta-blockers reduced all-cause and cardiovascular mortality compared to placebo in sinus rhythm, an effect that was consistent across LVEF strata, except for those in the small subgroup with LVEF ≥ 50%. For LVEF 40–49%, death occurred in 21/292 [7.2%] randomized to beta-blockers compared to 35/283 [12.4%] with placebo; adjusted hazard ratio (HR) 0.59 [95% confidence interval (CI) 0.34–1.03]. Cardiovascular death occurred in 13/292 [4.5%] with beta-blockers and 26/283 [9.2%] with placebo; adjusted HR 0.48 (95% CI 0.24–0.97). Over a median of 1.0 years following randomization (n = 4601), LVEF increased with beta-blockers in all groups in sinus rhythm except LVEF ≥50%. For patients in atrial fibrillation at baseline (n = 3050), beta-blockers increased LVEF when Conclusion Beta-blockers improve LVEF and prognosis for patients with heart failure in sinus rhythm with a reduced LVEF. The data are most robust for LVEF

Background Case series are an important and common study type. No guideline exists for reporting case series and there is evidence of key data being missed from such reports. The first step in the process of developing a methodologically sound reporting guideline is a systematic review of literature relevant to the reporting deficiencies of case series. Methods A systematic review of methodological and reporting quality in surgical case series was performed. The electronic search strategy was developed by an information specialist and included MEDLINE, Embase, Cochrane Methods Register, Science Citation Index and Conference Proceedings Citation index, from the start of indexing to 5 November 2014. Independent screening, eligibility assessments and data extraction were performed. Included articles were then analysed for five areas of deficiency: failure to use standardized definitions, missing or selective data (including the omission of whole cases or important variables), transparency or incomplete reporting, whether alternative study designs were considered, and other issues. Results Database searching identified 2205 records. Through the process of screening and eligibility assessments, 92 articles met inclusion criteria. Frequencies of methodological and reporting issues identified were: failure to use standardized definitions (57 per cent), missing or selective data (66 per cent), transparency or incomplete reporting (70 per cent), whether alternative study designs were considered (11 per cent) and other issues (52 per cent). Conclusion The methodological and reporting quality of surgical case series needs improvement. The data indicate that evidence-based guidelines for the conduct and reporting of case series may be useful.

BACKGROUND: Breast cancer at a young age is associated with poor prognosis. The Prospective Study of Outcomes in Sporadic and Hereditary Breast Cancer (POSH) was designed to investigate factors affecting prognosis in this patient group. METHODS: Between 2000 and 2008, 2956 patients aged 40 years or younger were recruited to a UK multicenter prospective observational cohort study (POSH). Details of tumor pathology, disease stage, treatment received, and outcome were recorded. Overall survival (OS) and distant disease-free interval (DDFI) were assessed using Kaplan-Meier curves. All statistical tests were two-sided. RESULTS: Median age of patients was 36 years. Median tumor diameter was 22 mm, and 50% of patients had positive lymph nodes; 59% of tumors were grade 3, 33.7% were estrogen receptor (ER) negative, and 24% were human epidermal growth factor receptor 2 (HER2) positive. Five-year OS was higher for patients with ER-positive than ER-negative tumors (85.0%, 95% confidence interval [CI] = 83.2% to 86.7% vs 75.7%, 95% CI = 72.8% to 78.4%; P < .001), but by eight years, survival was almost equal. The eight-year OS of patients with ER-positive tumors was similar to that of patients with ER-negative tumors in both HER2-positive and HER2-negative subgroups. The flexible parametric survival model for OS shows that the risk of death increases steadily over time for patients with ER-positive tumors in contrast to patients with ER-negative tumors, where risk of death peaked at two years. CONCLUSIONS: These results confirm the increased frequency of ER-negative tumors and early relapse in young patients and also demonstrate the equally poor longer-term outlook of young patients who have ER-positive tumors with HER2-negative or -positive disease.

The importance of transparency in the conduct and reporting of medical research is increasingly recognized. Fueling that interest is growing evidence that the literature is biased by poor reporting practices. Prime concerns are the nonpublication of whole studies and the selective reporting of results within publications, both driven by the nature of the findings. Regardless of whether such behavior is intended to mislead, such post hoc decision-making clearly opposes key principles of research and causes bias. Nonreporting and misleading reporting do not just mislead researchers in the field, they also diminish the evidence base underpinning clinical practice and harm patients; there is a strong case that they represent scientific misconduct (1, 2). Practices that distort the evidence base are unethical. The results of multiple similar studies will vary around what we can consider to be the truth. Some studies will have promising results and others will not. Even when in truth the effect being studied is absent, for 5% of studies the result will be formally statistically significant ( P < 0.05). Preferential publication of the significant studies will make the evidence for any question seem more promising than it should be. A major driver of biased reporting is the use of P values and the unfortunate related practice of labeling studies as “positive” or “negative.” For randomized controlled trials (RCTs), there is extremely strong evidence that what gets published is indeed biased in favor of statistically significant results (3, 4). That bias means that the effectiveness of drugs or other interventions is overestimated. Although the finger is often pointed at industry, academics and noncommercial funders are just as guilty …

Editor's Note: PTJ's Editorial Board has adopted PRISMA to help PTJ better communicate research to physical therapists. For more, read Chris Maher's editorial starting on page 870. Membership of the PRISMA Group is provided in the Acknowledgments. This article has been reprinted with permission from the Annals of Internal Medicine from Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Ann Intern Med. Available at: http://www.annals.org/cgi/content/full/151/4/264. The authors jointly hold copyright of this article. This article has also been published in PLoS Medicine, BMJ, Journal of Clinical Epidemiology, and Open Medicine. Copyright © 2009 Moher et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Background Randomized trials without reported adequate allocation concealment have been shown to overestimate the benefit of experimental interventions. We investigated the robustness of conclusions drawn from meta-analyses to exclusion of such trials. Material Random sample of 38 reviews from The Cochrane Library 2003, issue 2 and 32 other reviews from PubMed accessed in 2002. Eligible reviews presented a binary effect estimate from a meta-analysis of randomized controlled trials as the first statistically significant result that supported a conclusion in favour of one of the interventions. Methods We assessed the methods sections of the trials in each included meta-analysis for adequacy of allocation concealment. We replicated each meta-analysis using the authors' methods but included only trials that had adequate allocation concealment. Conclusions were defined as not supported if our result was not statistically significant. Results Thirty-four of the 70 meta-analyses contained a mixture of trials with unclear or inadequate concealment as well as trials with adequate allocation concealment. Four meta-analyses only contained trials with adequate concealment, and 32, only trials with unclear or inadequate concealment. When only trials with adequate concealment were included, 48 of 70 conclusions (69%; 95% confidence interval: 56-79%) lost support. The loss of support mainly reflected loss of power (the total number of patients was reduced by 49%) but also a shift in the point estimate towards a less beneficial effect. Conclusion Two-thirds of conclusions in favour of one of the interventions were no longer supported if only trials with adequate allocation concealment were included.

For ethical and economic reasons, it is important to design animal experiments well, to analyze the data correctly, and to use the minimum number of animals necessary to achieve the scientific objectives---but not so few as to miss biologically important effects or require unnecessary repetition of experiments. Investigators are urged to consult a statistician at the design stage and are reminded that no experiment should ever be started without a clear idea of how the resulting data are to be analyzed. These guidelines are provided to help biomedical research workers perform their experiments efficiently and analyze their results so that they can extract all useful information from the resulting data. Among the topics discussed are the varying purposes of experiments (e.g., exploratory vs. confirmatory); the experimental unit; the necessity of recording full experimental details (e.g., species, sex, age, microbiological status, strain and source of animals, and husbandry conditions); assigning experimental units to treatments using randomization; other aspects of the experiment (e.g., timing of measurements); using formal experimental designs (e.g., completely randomized and randomized block); estimating the size of the experiment using power and sample size calculations; screening raw data for obvious errors; using the t-test or analysis of variance for parametric analysis; and effective design of graphical data.

The atypical mole syndrome (AMS) phenotype is the strongest known risk factor for cutaneous melanoma but recognition of the phenotype has been claimed to be problematic and to require specialist assessment. This study determined the ability of previously unskilled doctors and nurses in five countries to recognize the phenotype after brief training. The system used was the AMS scoring system. This incorporates melanocytic naevus counts, clinical atypia of naevi and distribution of naevi. The agreement in scoring between the dermatologist and trained personnel was determined in 986 patients; overall agreement in diagnosis was 94.5% (kappa 0.70, P < 0.0001). The kappa scores in different countries ranged from 0.65 to 0.77 for individual naevus characteristics, indicative of good agreement. Accurate diagnosis of the atypical mole syndrome phenotype is possible by non- specialists. This has implications for collaborative studies of naevi, for screening and for both primary and secondary prevention of melanoma.

In medicine, patients and their care providers are confronted with making numerous decisions that are commonly, if not always, made on the basis of a probability—a probability that a specific disease or condition is present (diagnostic setting) or a specific event or outcome will occur in the future (prognostic setting). In the diagnostic setting, the probability that a particular disease is present is used, for example, to inform the referral of patients for further testing, initiate treatment directly, or reassure patients that a serious cause for their complaint is unlikely. In the prognostic setting, predictions are used for planning lifestyle or therapeutic decisions based on the probability of developing a particular outcome or health state within a specific time period. Prognostic probabilities can be estimated from ill or healthy individuals, and simply refer to the …

Background Risk of the outcome is a mathematical determinant of the absolute treatment benefit of an intervention, yet this can vary substantially within a trial population, complicating the interpretation of trial results. Methods We developed risk models using Cox or logistic regression on a set of large publicly available randomized controlled trials (RCTs). We evaluated risk heterogeneity using the extreme quartile risk ratio (EQRR, the ratio of outcome rates in the lowest risk quartile to that in the highest) and skewness using the median to mean risk ratio (MMRR, the ratio of risk in the median risk patient to the average). We also examined heterogeneity of treatment effects (HTE) across risk strata. Results We describe 39 analyses using data from 32 large trials, with event rates across studies ranging from 3% to 63% (median = 15%, 25th-75th percentile = 9-29%). C-statistics of risk models ranged from 0.59 to 0.89 (median = 0.70, 25th-75th percentile = 0.65-0.71). The EQRR ranged from 1.8 to 50.7 (median = 4.3, 25th-75th percentile = 3.0-6.1). The MMRR ranged from 0.4 to 1.0 (median = 0.86, 25th-75th percentile = 0.80-0.92). EQRRs were predictably higher and MMRRs predictably lower as the c-statistic increased or the overall outcome incidence decreased. Among 18 comparisons with a significant overall treatment effect, there was a significant interaction between treatment and baseline risk on the proportional scale in only one. The difference in the absolute risk reduction between extreme risk quartiles ranged from -3.2 to 28.3% (median = 5.1%; 25th-75th percentile = 0.3-10.9). Conclusions There is typically substantial variation in outcome risk in clinical trials, commonly leading to clinically significant differences in absolute treatment effects Most patients have outcome risks lower than the trial average reflected in the summary result. Risk-stratified trial analyses are feasible and may be clinically informative, particularly when the outcome is predictable and uncommon.

Despite the fundamental importance of measurement in medicine, studies which compare methods of clinical measurement are often analysed inappropriately and may thus come to incorrect conclusions. We explain the problems with the main methods used, notably correlation and regression, and describe the limits of agreement approach which we have proposed. We consider why incorrect methods are so widely used and describe our own experience in bringing this issue to the attention of medical researchers.

We read with great interest the recent development and internal validation of EuroSCORE II [1] in the anticipation of greater methodological rigour and reporting than for the original study describing the development of EuroSCORE I [2]. In general, we feel that there has been an improvement in reporting, however, like others we find the description of the internal validation of EuroSCORE II somewhat confusing, and subsequent responses have highlighted flaws in evaluating the calibration of EuroSCORE II [3, 4]. In particular, it is unclear, given that the authors randomly split the cohort into development and internal validation data sets, why they included an undefined portion of the development data set in calculating the Hosmer–Lemeshow test statistic, and did not just use the validation data set. Furthermore, it is disappointing that a Hosmer–Lemeshow test giving a P-value that only just exceeded the Holy Grail of 0.05 was taken to indicate good calibration; it is far from convincing evidence that EuroSCORE II is well-calibrated. Disappointingly, recent correspondence bringing this to the attention of interested readers committed the cardinal sin of associating statistical significance (P = 0.05) with clinical significance [3]. We urge authors to be clear when describing the results of a statistical test, particularly those without an associated effect measure, and refrain from inferring clinical importance on the basis of the magnitude of a single P-value. Similarly, a second correspondent who highlighted flaws of the Hosmer–Lemeshow analysis made another disappointing statement, namely that ‘statistical results are either significant or non-significant, black or white, there is no grey in statistics’ [4]. Such a statement is misinformed and discouraging; statistics is ’entirely’ about this grey zone. The foundations of statistics lie in probability theory where one is solely concerned with capturing uncertainty. Where variability in measurements exists, we can never be entirely confident that our results are not merely an artefact of the data. Furthermore, describing statistical results as significant or non-significant is highly uninformative and should be avoided in preference to measures of effect including accounting for uncertainty by reporting appropriate confidence intervals. The Hosmer–Lemeshow test has limited usefulness and provides no information on any overor under-prediction of a risk prediction model [5]. It would be more useful for authors to present calibration plots, and essential that they restrict this analysis to the validation data set only. Such plots provide a highly informative graphical display of calibration, which can be accompanied by estimates of the calibration slope with a confidence interval (a slope of 1 with intercept 0 indicates perfect discrimination) [6]. In the light of the unimpressive calibration results of EuroSCORE II, calibration plots (including examining calibration by key prognostic factors such as age) would provide an insight into where the model is miscalibrated.

Our interest in the analysis of method comparison studies stemmed from discussions about consulting problems we were independently working on in the late 1970s. Examination of published papers showed that, at that time, most authors were using the Pearson correlation coefficient. It was obvious to us that this method did not assess agreement, but association, and that a high correlation was no guarantee of good agreement. We felt that a comparison of two methods of measurement, such as different assays, should attempt to quantify the differences and that P values were largely irrelevant. The question is not whether the two methods agree, but how closely they agree. Our statistical approach was based on investigation of the distribution of the between-method differences. We suggested summarizing the data by the mean and 95% range of the differences, which we called the 95% limits of agreement. The graph, which many think is the whole of our method, was …

s of the 4th Congress of ECCO the European Crohn’s and Colitis Organisation S71 procedure to 95 minutes. A dedicated infusion unit for IBD patients can achieve high standards of care. P151 How variable is the Mayo score between observers and might this affect trial recruitment or outcome? A.J. Walsh1 *, A.O.S. Brain1, S. Keshav1, O. Buchel1, B. Merrin1, M. Rolinski1, S. Thomas1, W. Lydia1, D. Altman2, S. Travis1. 1The John Radcliffe Hospital, Oxford, United Kingdom, 2University of Oxford, Oxford, United Kingdom Background: The Mayo score for ulcerative colitis (UC) activity has 4 components: stool frequency (SF), rectal bleeding (RB), flexible sigmoidoscopy (FS) and physician’s global assessment (PGA). Each category scores 0 3. How interobserver variation (IOV) affects the Mayo score and its impact on criteria for inclusion or outcome of clinical trials are unknown. Methods: 100 patients with UC were seen independently, each on the same day, in random order, by 4 gastroenterologists. Both patient and clinicians completed proformas. A separate clinician performed video-recorded FS on the same day. The video was scored by the 4 gastroenterologists, blinded to other results. Comparison was made with inclusion criteria for ACT 1&2 trials (Mayo 6 12, endoscopy subscore 2), remission outcome (Mayo 2, no subscore >1) and partial Mayo score (endoscopy excluded). For clinical relevance (CR), scores were categorised as remission ( 2), mild (3 5), moderate (6 8), or severe (9 12) and an experienced, blinded clinician independently assigned an appropriate clinical category (ACC) to each patient by assessing symptoms, examination, blood results, FS and histology. Quadratic weighted u statistics assessed agreement within the Mayo score, where disagreements are weighted in relation to their magnitude. Results: Of 100 patients, there was complete agreement between 4 clinicians in total Mayo score in 6/96 (4 had no FS), varying by 2 points in 84/96, which changed clinical category in 23/84. Agreement for CR and ACC were good (u=0.88, u=0.81). Between patients and clinicians there was 70% agreement for SF and RB, but patients disagreed with individual clinicians in 30% for SF and 17% for RB. Patients scored a higher category than clinician in 34% for SF and 60% for RB. Between clinicians there was complete agreement in 65% for SF, 74% for RB, but only 21% for FS and 45% for PGA. Most disagreement was by one category (median 81%, range 74 93). For inclusion criteria, at least 1 clinician would have included 41/96, but all agreed in only 17/41 (41%). At least 2 clinicians would have included 22 and excluded 67, so there was at least 25% disagreement in 26/96 (27%) and 50% disagreement in 11/96 (11%). 11% had FS score 2 but total score 5; 9.1% had a total score 6 but a FS score 1. For remission, at least 1 clinician scored 2 in 43/96, but all agreed in only 20/43 (47%) and 2 agreed in 35/43 (81%). Agreement was not significantly improved by a total score 1. Conclusion: There is high variability in Mayo scoring between observers, despite good agreement on clinical category. Complete agreement between observers for recruitment to clinical trials or outcome occurs in

A method for combining matched and unmatched data is described and was applied to the results of randomized, controlled trials of photocoagulation in the treatment of diabetic retinopathy. A pooled estimate from the matched and unmatched studies was obtained by adaptation of the Mantel-Haenszel method, where the strata were unmatched studies and matched pairs within studies. A test of significance was based on the Mantel-Haenszel chi-square statistic, the latter also being used to calculate test-based confidence intervals. A test of homogeneity was performed by combining Mantel-Haenszel chi-square statistics from the matched and unmatched studies. By these methods, the combined estimate of the risk of deterioration of visual acuity for those receiving photocoagulation (relative to a risk of unity for those not receiving photocoagulation) was 0.37 (95 per cent confidence interval 0.29-0.46). The chi-square statistic (1 df) for an effect of treatment was 87.75 (p less than 0.0001). The chi-square statistic for homogeneity of relative risk among studies was 11.20 (4 df, p less than 0.05). However, this result was influenced disproportionately by one small matched study.

A partir d'un modele de Cox pour des donnees de survie censurees, il est possible d'estimer la probabilite de survie a l'annee t pour un individu de covariables Z 0