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      Improving the Transparency of Prognosis Research: The Role of Reporting, Data Sharing, Registration, and Protocols

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          George Peat and colleagues review and discuss current approaches to transparency and published debates and concerns about efforts to standardize prognosis research practice, and make five recommendations.

          Please see later in the article for the Editors' Summary

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          Most cited references45

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          Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research

          In this article, the third in the PROGRESS series on prognostic factor research, Sara Schroter and colleagues review how prognostic models are developed and validated, and then address how prognostic models are assessed for their impact on practice and patient outcomes, illustrating these ideas with examples.
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            Comparison of stratified primary care management for low back pain with current best practice (STarT Back): a randomised controlled trial

            Summary Background Back pain remains a challenge for primary care internationally. One model that has not been tested is stratification of the management according to the patient's prognosis (low, medium, or high risk). We compared the clinical effectiveness and cost-effectiveness of stratified primary care (intervention) with non-stratified current best practice (control). Methods 1573 adults (aged ≥18 years) with back pain (with or without radiculopathy) consultations at ten general practices in England responded to invitations to attend an assessment clinic. Eligible participants were randomly assigned by use of computer-generated stratified blocks with a 2:1 ratio to intervention or control group. Primary outcome was the effect of treatment on the Roland Morris Disability Questionnaire (RMDQ) score at 12 months. In the economic evaluation, we focused on estimating incremental quality-adjusted life years (QALYs) and health-care costs related to back pain. Analysis was by intention to treat. This study is registered, number ISRCTN37113406. Findings 851 patients were assigned to the intervention (n=568) and control groups (n=283). Overall, adjusted mean changes in RMDQ scores were significantly higher in the intervention group than in the control group at 4 months (4·7 [SD 5·9] vs 3·0 [5·9], between-group difference 1·81 [95% CI 1·06–2·57]) and at 12 months (4·3 [6·4] vs 3·3 [6·2], 1·06 [0·25–1·86]), equating to effect sizes of 0·32 (0·19–0·45) and 0·19 (0·04–0·33), respectively. At 12 months, stratified care was associated with a mean increase in generic health benefit (0·039 additional QALYs) and cost savings (£240·01 vs £274·40) compared with the control group. Interpretation The results show that a stratified approach, by use of prognostic screening with matched pathways, will have important implications for the future management of back pain in primary care. Funding Arthritis Research UK.
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              Cumulative meta-analysis of therapeutic trials for myocardial infarction.

              The large volume of published randomized, controlled trials has led to a need for meta-analyses to track therapeutic advances. Performing a new meta-analysis whenever the results of a new trial of a particular therapy are published permits the study of trends in efficacy and makes it possible to determine when a new treatment appears to be significantly effective or deleterious. We describe the use of such a procedure, cumulative meta-analysis, to assess therapeutic trials among patients with myocardial infarction. We performed cumulative meta-analyses of clinical trials that evaluated 15 treatments and preventive measures for acute myocardial infarction. An example of this method is its application to the use of intravenous streptokinase as thrombolytic therapy for acute infarction. Thirty-three trials evaluating this therapy were performed between 1959 and 1988. We found that a consistent, statistically significant reduction in total mortality (odds ratios, 0.74; 95 percent confidence interval, 0.59 to 0.92) was achieved in 1973, after only eight trials involving 2432 patients had been completed. The results of the 25 subsequent trials, which enrolled an additional 34,542 patients through 1988, had little or no effect on the odds ratio establishing efficacy, but simply narrowed the 95 percent confidence interval. In particular, two very large trials, the Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico trial in 1986 (11,712 patients) and the Second International Study of Infarct Survival trial in 1988 (17,187 patients) did not modify the already established evidence of efficacy. We used a similar approach to study the accumulating evidence of efficacy (or lack of efficacy) of 14 other therapies and preventive measures for myocardial infarction. Cumulative meta-analysis of therapeutic trials facilitates the determination of clinical efficacy and harm and may be helpful in tracking trials, planning future trials, and making clinical recommendations for therapy.
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                Author and article information

                Journal
                PLoS Med
                PLoS Med
                PLoS
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, USA )
                1549-1277
                1549-1676
                July 2014
                8 July 2014
                : 11
                : 7
                : e1001671
                Affiliations
                [1 ]Arthritis Research UK Primary Care Research Centre, Research Institute for Primary Care & Health Sciences, Keele University, Keele, Staffordshire, United Kingdom
                [2 ]School of Health and Population Sciences, University of Birmingham, United Kingdom
                [3 ]Arthritis Research UK Primary Care Research Centre, Research Institute for Primary Care & Health Sciences, Keele University, Keele, Staffordshire, United Kingdom
                [4 ]Department of Epidemiology and Public Health, University College London, London, United Kingdom
                [5 ]Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, Victoria, Australia
                [6 ]Department of Oral and Maxillofacial Surgery, North Manchester General Hospital, Pennine Acute NHS Trust, Manchester, United Kingdom
                [7 ]Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, Netherlands
                [8 ]London School of Hygiene & Tropical Medicine, London, United Kingdom
                [9 ]Department of Public Health, Erasmus MC, Rotterdam, Netherlands
                [10 ]BMJ, London, United Kingdom
                [11 ]Centre for Statistics in Medicine, University of Oxford, Wolfson College Annexe, Oxford, United Kingdom
                [12 ]Department of Epidemiology and Public Health and Director of the Farr Institute of Health Informatics Research at UCL Partners, London, United Kingdom
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: GP RDR PC KIM PAK KGMM PP EWS SS DGA HH. Performed the experiments: GP RDR PC KIM PAK KGMM PP EWS SS DGA HH. Wrote the first draft of the manuscript: GP RDR PC KIM KGMM SS. Contributed to the writing of the manuscript: GP RDR PC KIM PAK KGMM PP EWS SS DGA HH. ICMJE criteria for authorship read and met: GP RDR PC KIM PAK KGMM PP EWS SS DGA HH. Agree with manuscript results and conclusions: GP RDR PC KIM PAK KGMM PP EWS SS DGA HH.

                ¶ These authors contributed equally and are joint first authors.

                ‡ Membership of the PROGRESS Group is listed in the Acknowledgments.

                Article
                PMEDICINE-D-13-03912
                10.1371/journal.pmed.1001671
                4086727
                25003600
                3a06bb53-0749-4d40-a070-93446c26d213
                Copyright @ 2014

                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.

                History
                Page count
                Pages: 8
                Funding
                This paper was developed after the participation of all authors in a workshop on the topic, organised by the MRC PROGnosis RESearch Strategy (PROGRESS) Partnership ( www.progress-partnership.org), which is supported by a Medical Research Council Partnership Grant (G0902393) involving University College London (HH, Aroon Hingorani, KIM), Oxford University (DGA), Birmingham University (RDR), London School of Hygiene & Tropical Medicine (Ian Roberts, PP), Keele University (PC, Daniëlle van der Windt), and Queen Mary University London (Adam D Timmis). DGA is supported by a programme grant from Cancer Research UK (C5529). HH is supported by grants from the UK National Institute for Health Research (RP-PG-0407-10314; http://www.nihr.ac.uk/), and the Wellcome Trust (086091/Z/08/Z; http://www.wellcome.ac.uk/) and by awards establishing the Farr Institute of Health Informatics Research at UCLP Partners from the MRC, in partnership with Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Assembly Government), the Chief Scientist Office (Scottish Government Health Directorates) and the Wellcome Trust (MR/K006584/1). EWS was partly funded by the NIH (NS-042691). KGMM is supported by The Netherlands Organization for Scientific Research (ZON-MW 918.10.615 and 91208004). GP, PC, and Danielle van der Windt are supported by the Arthritis Research UK Centre of Excellence in Primary Care. PC is a UK NIHR Senior Investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Guidelines and Guidance
                Medicine and Health Sciences
                Epidemiology
                Biomarker Epidemiology
                Clinical Epidemiology
                Epidemiological Methods and Statistics
                Research and Analysis Methods
                Research Assessment
                Publication Practices
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