Douglas G. Altman’s research while affiliated with University of Oxford and other places

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org. This article is the translation in to Russian by Dr. Ruslan Saygitov (ORCID: https://orcid.org/0000-0002-8915-6153) from the original published in [Ann Intern Med. 2015;162:W1-W73. doi: https://doi.org/10.7326/M14-0698 ].

Background . Well-written and transparent case reports (1) reveal early signals of potential benefits, harms, and information on the use of resources; (2) provide information for clinical research and clinical practice guidelines, and (3) inform medical education. High-quality case reports are more likely when authors follow reporting guidelines. During 2011–2012, a group of clinicians, researchers, and journal editors developed recommendations for the accurate reporting of information in case reports that resulted in the CARE (CAse REport) Statement and Checklist. They were presented at the 2013 International Congress on Peer Review and Biomedical Publication, have been endorsed by multiple medical journals, and translated into nine languages. Objectives . This explanation and elaboration document has the objective to increase the use and dissemination of the CARE Checklist in writing and publishing case reports. Article design and setting . Each item from the CARE Checklist is explained and accompanied by published examples. The explanations and examples in this document are designed to support the writing of high-quality case reports by authors and their critical appraisal by editors, peer reviewers, and readers. Results and conclusion. This article and the 2013 CARE Statement and Checklist, available from the CARE website [www.care-statement.org] and the EQUATOR Network [www.equator-network.org], are resources for improving the completeness and transparency of case reports. Source . This article is a translation of the original paper «CARE guidelines for case reports: explanation and elaboration document» in the Journal of Clinical Epidemiology (doi: 10.1016/j.jclinepi.2017.04.026), prepared under the permission of the copyright holder (Elsevier Inc.), with supervision from the Scientific Editor by Professor E.G. Starostina, MD, PhD (translator) (Moscow, Russia). Present translation was first published in Digital Diagnostics. doi: 10.17816/DD105291. It is published with minor changes related to the literary editing of the translation itself. Keywords: case report; case study; EQUATOR network; health research reporting guidelines; CARE guideline; timelines; N-of-1 For citation: Riley David S., Barber Melissa S., Kienle Gunver S., Aronson Jeffrey K., von Schoen-Angerer Tido, Tugwell Peter, Kiene Helmut, Helfand Mark, Altman Douglas G., Sox Harold, Werthmann Paul G., Moher David, Rison Richard A., Shamseer Larissa, Koch Christian A., Sun Gordon H., Hanaway Patrick, Sudak Nancy L., Kaszkin-Bettag Marietta, Carpenter James E., Gagnier Joel J. CARE Guidelines for Case Reports: Explanation and Elaboration Document. Translation into Russian. Voprosy sovremennoi pediatrii — Current Pediatrics. 2023;22(2):88–108. (In Russ). doi: https://doi.org/10.15690/vsp.v22i2.2540

Background To assess the quality of reporting of RCT protocols approved by UK research ethics committees before and after the publication of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guideline. Methods We had access to RCT study protocols that received ethical approval in the UK in 2012 (n=103) and 2016 (n=108). From those, we assessed the adherence to the 33 SPIRIT items (i.e. a total of 64 components of the 33 SPIRIT items). We descriptively analysed the adherence to SPIRIT guidelines as proportion of adequately reported items (median and interquartile range [IQR]) and stratified the results by year of approval and sponsor. Results The proportion of reported SPIRIT items increased from a median of 64.9% (IQR, 57.6–69.2%) in 2012 to a median of 72.5% (IQR, 65.3–78.3%) in 2016. Industry-sponsored RCTs reported more SPIRIT items in 2012 (median 67.4%; IQR, 64.1–69.4%) compared to non-industry-sponsored trials (median 59.8%; IQR, 46.5–67.7%). This gap between industry- and non-industry-sponsored trials increased in 2016 (industry-sponsored: median 75.6%; IQR, 71.2–79.0% vs non-industry-sponsored: median 65.3%; IQR, 51.6–76.3%). Conclusions The adherence to SPIRIT guidelines has improved in the UK from 2012 to 2016 but remains on a modest level, especially for non-industry-sponsored RCTs.

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalisability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated Web site (http://www. strobe-statement.org/) should be helpful resources to improve reporting of observational research. Present article is Russian-language translation of the original manuscript edited by Doctor of Medicine R.T. Saygitov. Present translation was first published in Digital Diagnostics. doi: 10.17816/DD70821. It is published with minor changes related to the literary editing of the translation itself.

Diagnostic accuracy studies are, like other clinical studies, at risk of bias due to shortcomings in design and conduct, and the results of a diagnostic accuracy study may not apply to other patient groups and settings. Readers of study reports need to be informed about study design and conduct, in sufficient detail to judge the trustworthiness and applicability of the study findings. The STARD statement (Standards for Reporting of Diagnostic Accuracy Studies) was developed to improve the completeness and transparency of reports of diagnostic accuracy studies. STARD contains a list of essential items that can be used as a checklist, by authors, reviewers and other readers, to ensure that a report of a diagnostic accuracy study contains the necessary information. STARD was recently updated. All updated STARD materials, including the checklist, are available at http://www.equator-network.org/reporting-guidelines/stard. Here, we present the STARD 2015 explanation and elaboration document. Through commented examples of appropriate reporting, we clarify the rationale for each of the 30 items on the STARD 2015 checklist, and describe what is expected from authors in developing sufficiently informative study reports. Present article is Russian-language translation of the original manuscript edited by Doctor of Medicine R.T. Saygitov. Present translation was first published in Digital Diagnostics. doi: 10.17816/DD71031. It is published with minor changes related to the literary editing of the translation itself.

Prediction models combine values of multiple predictors to estimate an individual's risk of having a certain outcome or disease (diagnostic models) or developing a future outcome (prognostic models). Systematic reviews are needed to identify existing prediction models for a certain target population or outcome and to summarize their predictive performance and heterogeneity in their performance. Appraising the quality and reporting of a prediction model study is essential. Studies describing the development or validation of a prediction model often do not conform to prevailing methodological standards and key details are often not reported. Meta‐analysis of the predictive performance of a specific prediction model from multiple external validation studies of that model is possible, focusing on calibration and discrimination. In this chapter, we describe the types of systematic reviews that can be conducted on prediction model studies and discuss the challenges faced in identifying, appraising, and qualitatively and quantitatively synthesizing these studies.

Primary studies to identify prognostic factors are abundant, but often have conflicting findings and variable quality. This motivates systematic reviews to identify, evaluate, and summarize prognostic factor studies. Broad search strings are required to identify relevant studies, and the CHARMS‐PF checklist guides subsequent data extraction. The QUIPS tool examines each study's risk of bias; unfortunately, many studies will have high risk of bias due to poor design and analysis. Meta‐analysis can be used to combine and summarize prognostic effect estimates (such as hazard ratios or odds ratios) across studies, but may not always be sensible. Between‐study heterogeneity is expected. Ideally, separate meta‐analyses are performed; for example, for each method of prognostic factor measurement, for each cut point (for categorized continuous prognostic factors), and for unadjusted and adjusted prognostic factor estimates. The adjusted prognostic factor estimate is usually more relevant, because prognosis in clinical practice is commonly based on multiple prognostic factors, and so the prognostic information from a particular factor needs to add value over others. Publication bias is also a major threat in reviews of prognosis studies. Availability of individual participant data alleviates many, but not all, of the challenges.

The principles and steps of systematic reviews are similar to any other research undertaking: formulation of the problem to be addressed, collection and analysis of the data, and interpretation of the results. A study protocol should be written, which states objectives and eligibility criteria, describes how studies will be identified and selected, explains how any assessment of methodological quality or risk of bias will be undertaken, and provides details of any planned synthesis methods. The results from eligible studies are expressed in a standardized format and are often displayed graphically in forest plots with confidence intervals. Heterogeneity between study results and possible biases may be explored graphically in a forest, funnel, and other plots, and in statistical analyses. If a meta‐analysis is deemed appropriate, a typical effect is estimated by combining the data. Most meta‐analysis methods follow either fixed‐effect(s) or random‐effects approaches, which differ in the way they treat between‐study heterogeneity. Meta‐analyses generally use relative effect measures, while absolute measures are used when applying the findings to clinical or public health situations.

Studies at high risk of bias may distort the results of systematic reviews and meta‐analyses. Based on empirical evidence and theoretical considerations, the following sources of bias should be assessed when including randomized trials in a review: bias arising from the randomization process, bias due to deviations from the intended interventions, bias due to missing outcome data, bias in measurement of the outcome, and bias due to selective reporting. The use of summary scores from quality scales is problematic. Results depend on the choice of scale, and the interpretation of the results is difficult. Therefore, judging risk of bias within separate specified bias domains and recording the information on which each judgment is based – the domain‐based approach – are preferred. Assessments of risk of bias of included studies should routinely be incorporated in systematic reviews and meta‐analyses. Currently, this is best done using sensitivity analyses.