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1.  Strengthening the Reporting of Genetic Risk Prediction Studies (GRIPS): Explanation and Elaboration 
European journal of epidemiology  2011;26(4):313-337.
The rapid and continuing progress in gene discovery for complex diseases is fuelling interest in the potential application of genetic risk models for clinical and public health practice.The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality.Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction.A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines.These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.
doi:10.1007/s10654-011-9551-z
PMCID: PMC3088812  PMID: 21424820
2.  Strengthening the reporting of genetic risk prediction studies: the GRIPS statement 
Genome Medicine  2011;3(3):16.
The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of genetic risk prediction studies (the GRIPS statement), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published at http://www.plosmedicine.org.
doi:10.1186/gm230
PMCID: PMC3092101  PMID: 21410995
3.  Strengthening the reporting of genetic risk prediction studies: the GRIPS statement 
European Journal of Epidemiology  2011;26(4):255-259.
The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published.
doi:10.1007/s10654-011-9552-y
PMCID: PMC3088799  PMID: 21431409
Genetic; Risk prediction; Methodology; Guidelines; Reporting
4.  Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration 
The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.
doi:10.1038/ejhg.2011.27
PMCID: PMC3083630  PMID: 21407270
5.  Strengthening the reporting of genetic risk prediction studies: the GRIPS statement 
The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies, building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published on the EJHG website.
doi:10.1038/ejhg.2011.25
PMCID: PMC3172920  PMID: 21407265
6.  Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement 
BMC Medicine  2015;13:1.
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
Editors’ note: In order to encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, British Medical Journal, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. An accompanying Explanation and Elaboration article is freely available only on www.annals.org; Annals of Internal Medicine holds copyright for that article.
doi:10.1186/s12916-014-0241-z
PMCID: PMC4284921  PMID: 25563062
Prediction models; Prognostic; Diagnostic; Model development; Validation; Transparency; Reporting
7.  Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) 
Circulation  2015;131(2):211-219.
Background—
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed.
Methods—
The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors.
Results—
The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document.
Conclusions—
To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
doi:10.1161/CIRCULATIONAHA.114.014508
PMCID: PMC4297220  PMID: 25561516
diagnosis; epidemiology; prognosis; research design; risk; statistics
8.  Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement 
British Journal of Cancer  2015;112(2):251-259.
Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
doi:10.1038/bjc.2014.639
PMCID: PMC4454817  PMID: 25562432
prediction models; diagnostic; prognostic; model development; model validation; transparent reporting
9.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies 
PLoS Medicine  2007;4(10):e296.
Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the Web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.
This paper describes the recommendations of The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative on what should be included in an accurate and complete report of an observational study.
doi:10.1371/journal.pmed.0040296
PMCID: PMC2020495  PMID: 17941714
10.  Reporting and Methods in Clinical Prediction Research: A Systematic Review 
PLoS Medicine  2012;9(5):e1001221.
Walter Bouwmeester and colleagues investigated the reporting and methods of prediction studies in 2008, in six high-impact general medical journals, and found that the majority of prediction studies do not follow current methodological recommendations.
Background
We investigated the reporting and methods of prediction studies, focusing on aims, designs, participant selection, outcomes, predictors, statistical power, statistical methods, and predictive performance measures.
Methods and Findings
We used a full hand search to identify all prediction studies published in 2008 in six high impact general medical journals. We developed a comprehensive item list to systematically score conduct and reporting of the studies, based on recent recommendations for prediction research. Two reviewers independently scored the studies. We retrieved 71 papers for full text review: 51 were predictor finding studies, 14 were prediction model development studies, three addressed an external validation of a previously developed model, and three reported on a model's impact on participant outcome. Study design was unclear in 15% of studies, and a prospective cohort was used in most studies (60%). Descriptions of the participants and definitions of predictor and outcome were generally good. Despite many recommendations against doing so, continuous predictors were often dichotomized (32% of studies). The number of events per predictor as a measure of statistical power could not be determined in 67% of the studies; of the remainder, 53% had fewer than the commonly recommended value of ten events per predictor. Methods for a priori selection of candidate predictors were described in most studies (68%). A substantial number of studies relied on a p-value cut-off of p<0.05 to select predictors in the multivariable analyses (29%). Predictive model performance measures, i.e., calibration and discrimination, were reported in 12% and 27% of studies, respectively.
Conclusions
The majority of prediction studies in high impact journals do not follow current methodological recommendations, limiting their reliability and applicability.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
There are often times in our lives when we would like to be able to predict the future. Is the stock market going to go up, for example, or will it rain tomorrow? Being able predict future health is also important, both to patients and to physicians, and there is an increasing body of published clinical “prediction research.” Diagnostic prediction research investigates the ability of variables or test results to predict the presence or absence of a specific diagnosis. So, for example, one recent study compared the ability of two imaging techniques to diagnose pulmonary embolism (a blood clot in the lungs). Prognostic prediction research investigates the ability of various markers to predict future outcomes such as the risk of a heart attack. Both types of prediction research can investigate the predictive properties of patient characteristics, single variables, tests, or markers, or combinations of variables, tests, or markers (multivariable studies). Both types of prediction research can include also studies that build multivariable prediction models to guide patient management (model development), or that test the performance of models (validation), or that quantify the effect of using a prediction model on patient and physician behaviors and outcomes (impact assessment).
Why Was This Study Done?
With the increase in prediction research, there is an increased interest in the methodology of this type of research because poorly done or poorly reported prediction research is likely to have limited reliability and applicability and will, therefore, be of little use in patient management. In this systematic review, the researchers investigate the reporting and methods of prediction studies by examining the aims, design, participant selection, definition and measurement of outcomes and candidate predictors, statistical power and analyses, and performance measures included in multivariable prediction research articles published in 2008 in several general medical journals. In a systematic review, researchers identify all the studies undertaken on a given topic using a predefined set of criteria and systematically analyze the reported methods and results of these studies.
What Did the Researchers Do and Find?
The researchers identified all the multivariable prediction studies meeting their predefined criteria that were published in 2008 in six high impact general medical journals by browsing through all the issues of the journals (a hand search). They then scored the methods and reporting of each study using a comprehensive item list based on recent recommendations for the conduct of prediction research (for example, the reporting recommendations for tumor marker prognostic studies—the REMARK guidelines). Of 71 retrieved studies, 51 were predictor finding studies, 14 were prediction model development studies, three externally validated an existing model, and three reported on a model's impact on participant outcome. Study design, participant selection, definitions of outcomes and predictors, and predictor selection were generally well reported, but other methodological and reporting aspects of the studies were suboptimal. For example, despite many recommendations, continuous predictors were often dichotomized. That is, rather than using the measured value of a variable in a prediction model (for example, blood pressure in a cardiovascular disease prediction model), measurements were frequently assigned to two broad categories. Similarly, many of the studies failed to adequately estimate the sample size needed to minimize bias in predictor effects, and few of the model development papers quantified and validated the proposed model's predictive performance.
What Do These Findings Mean?
These findings indicate that, in 2008, most of the prediction research published in high impact general medical journals failed to follow current guidelines for the conduct and reporting of clinical prediction studies. Because the studies examined here were published in high impact medical journals, they are likely to be representative of the higher quality studies published in 2008. However, reporting standards may have improved since 2008, and the conduct of prediction research may actually be better than this analysis suggests because the length restrictions that are often applied to journal articles may account for some of reporting omissions. Nevertheless, despite some encouraging findings, the researchers conclude that the poor reporting and poor methods they found in many published prediction studies is a cause for concern and is likely to limit the reliability and applicability of this type of clinical research.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001221.
The EQUATOR Network is an international initiative that seeks to improve the reliability and value of medical research literature by promoting transparent and accurate reporting of research studies; its website includes information on a wide range of reporting guidelines including the REMARK recommendations (in English and Spanish)
A video of a presentation by Doug Altman, one of the researchers of this study, on improving the reporting standards of the medical evidence base, is available
The Cochrane Prognosis Methods Group provides additional information on the methodology of prognostic research
doi:10.1371/journal.pmed.1001221
PMCID: PMC3358324  PMID: 22629234
11.  Differences in Reporting of Analyses in Internal Company Documents Versus Published Trial Reports: Comparisons in Industry-Sponsored Trials in Off-Label Uses of Gabapentin 
PLoS Medicine  2013;10(1):e1001378.
Using documents obtained through litigation, S. Swaroop Vedula and colleagues compared internal company documents regarding industry-sponsored trials of off-label uses of gabapentin with the published trial reports and find discrepancies in reporting of analyses.
Background
Details about the type of analysis (e.g., intent to treat [ITT]) and definitions (i.e., criteria for including participants in the analysis) are necessary for interpreting a clinical trial's findings. Our objective was to compare the description of types of analyses and criteria for including participants in the publication (i.e., what was reported) with descriptions in the corresponding internal company documents (i.e., what was planned and what was done). Trials were for off-label uses of gabapentin sponsored by Pfizer and Parke-Davis, and documents were obtained through litigation.
Methods and Findings
For each trial, we compared internal company documents (protocols, statistical analysis plans, and research reports, all unpublished), with publications. One author extracted data and another verified, with a third person verifying discordant items and a sample of the rest. Extracted data included the number of participants randomized and analyzed for efficacy, and types of analyses for efficacy and safety and their definitions (i.e., criteria for including participants in each type of analysis). We identified 21 trials, 11 of which were published randomized controlled trials, and that provided the documents needed for planned comparisons. For three trials, there was disagreement on the number of randomized participants between the research report and publication. Seven types of efficacy analyses were described in the protocols, statistical analysis plans, and publications, including ITT and six others. The protocol or publication described ITT using six different definitions, resulting in frequent disagreements between the two documents (i.e., different numbers of participants were included in the analyses).
Conclusions
Descriptions of analyses conducted did not agree between internal company documents and what was publicly reported. Internal company documents provide extensive documentation of methods planned and used, and trial findings, and should be publicly accessible. Reporting standards for randomized controlled trials should recommend transparent descriptions and definitions of analyses performed and which study participants are excluded.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
To be credible, published research must present an unbiased, transparent, and accurate description of the study methods and findings so that readers can assess all relevant information to make informed decisions about the impact of any conclusions. Therefore, research publications should conform to universally adopted guidelines and checklists. Studies to establish whether a treatment is effective, termed randomized controlled trials (RCTs), are checked against a comprehensive set of guidelines: The robustness of trial protocols are measured through the Standard Protocol Items for Randomized Trials (SPIRIT), and the Consolidated Standards of Reporting Trials (CONSORT) statement (which was constructed and agreed by a meeting of journal editors in 1996, and has been updated over the years) includes a 25-point checklist that covers all of the key points in reporting RCTs.
Why Was This Study Done?
Although the CONSORT statement has helped improve transparency in the reporting of the methods and findings from RCTs, the statement does not define how certain types of analyses should be conducted and which patients should be included in the analyses, for example, in an intention-to-treat analysis (in which all participants are included in the data analysis of the group to which they were assigned, whether or not they completed the intervention given to the group). So in this study, the researchers used internal company documents released in the course of litigation against the pharmaceutical company Pfizer regarding the drug gabapentin, to compare between the internal and published reports the reporting of the numbers of participants, the description of the types of analyses, and the definitions of each type of analysis. The reports involved studies of gabapentin used for medical reasons not approved for marketing by the US Food and Drug Administration, known as “off-label” uses.
What Did the Researchers Do and Find?
The researchers identified trials sponsored by Pfizer relating to four off-label uses of gabapentin and examined the internal company protocols, statistical analysis plans, research reports, and the main publications related to each trial. The researchers then compared the numbers of participants randomized and analyzed for the main (primary) outcome and the type of analysis for efficacy and safety in both the internal research report and the trial publication. The researchers identified 21 trials, 11 of which were published RCTs that had the associated documents necessary for comparison.
The researchers found that in three out of ten trials there were differences in the internal research report and the main publication regarding the number of randomized participants. Furthermore, in six out of ten trials, the researchers were unable to compare the internal research report with the main publication for the number of participants analyzed for efficacy, because the research report either did not describe the primary outcome or did not describe the type of analysis. Overall, the researchers found that seven different types of efficacy analyses were described in the protocols, statistical analysis plans, and publications, including intention-to-treat analysis. However, the protocol or publication used six different descriptions for the intention-to-treat analysis, resulting in several important differences between the internal and published documents about the number of patients included in the analysis.
What Do These Findings Mean?
These findings from a sample of industry-sponsored trials on the off-label use of gabapentin suggest that when compared to the internal research reports, the trial publications did not always accurately reflect what was actually done in the trial. Therefore, the trial publication could not be considered to be an accurate and transparent record of the numbers of participants randomized and analyzed for efficacy. These findings support the need for further revisions of the CONSORT statement, such as including explicit statements about the criteria used to define each type of analysis and the numbers of participants excluded from each type of analysis. Further guidance is also needed to ensure consistent terminology for types of analysis. Of course, these revisions will improve reporting only if authors and journals adhere to them. These findings also highlight the need for all individual patient data to be made accessible to readers of the published article.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001378.
For more information, see the CONSORT statement website
The EQUATOR Network website is a resource center for the good reporting of health research studies and has more information about the SPIRIT initiative and the CONSORT statement
doi:10.1371/journal.pmed.1001378
PMCID: PMC3558476  PMID: 23382656
12.  STrengthening the REporting of Genetic Association studies (STREGA) – an extension of the STROBE statement 
Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy–Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.
doi:10.1111/j.1365-2362.2009.02125.x
PMCID: PMC2730482  PMID: 19297801
Epidemiology; gene-disease associations; gene-environment interaction; genetics; genome-wide association; meta-analysis; reporting recommendations; systematic review
13.  Strengthening the reporting of genetic association studies (STREGA): an extension of the STROBE statement 
Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy–Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.
doi:10.1007/s10654-008-9302-y
PMCID: PMC2764094  PMID: 19189221
Gene–disease associations; Genetics; Gene–environment interaction; Systematic review; Meta analysis; Reporting recommendations; Epidemiology; Genome-wide association
14.  Reporting Guidelines for Survey Research: An Analysis of Published Guidance and Reporting Practices 
PLoS Medicine  2011;8(8):e1001069.
Carol Bennett and colleagues review the evidence and find that there is limited guidance and no consensus on the optimal reporting of survey research.
Background
Research needs to be reported transparently so readers can critically assess the strengths and weaknesses of the design, conduct, and analysis of studies. Reporting guidelines have been developed to inform reporting for a variety of study designs. The objective of this study was to identify whether there is a need to develop a reporting guideline for survey research.
Methods and Findings
We conducted a three-part project: (1) a systematic review of the literature (including “Instructions to Authors” from the top five journals of 33 medical specialties and top 15 general and internal medicine journals) to identify guidance for reporting survey research; (2) a systematic review of evidence on the quality of reporting of surveys; and (3) a review of reporting of key quality criteria for survey research in 117 recently published reports of self-administered surveys. Fewer than 7% of medical journals (n = 165) provided guidance to authors on survey research despite a majority having published survey-based studies in recent years. We identified four published checklists for conducting or reporting survey research, none of which were validated. We identified eight previous reviews of survey reporting quality, which focused on issues of non-response and accessibility of questionnaires. Our own review of 117 published survey studies revealed that many items were poorly reported: few studies provided the survey or core questions (35%), reported the validity or reliability of the instrument (19%), defined the response rate (25%), discussed the representativeness of the sample (11%), or identified how missing data were handled (11%).
Conclusions
There is limited guidance and no consensus regarding the optimal reporting of survey research. The majority of key reporting criteria are poorly reported in peer-reviewed survey research articles. Our findings highlight the need for clear and consistent reporting guidelines specific to survey research.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Surveys, or questionnaires, are an essential component of many types of research, including health, and usually gather information by asking a sample of people questions on a specific topic and then generalizing the results to a larger population. Surveys are especially important when addressing topics that are difficult to assess using other approaches and usually rely on self reporting, for example self-reported behaviors, such as eating habits, satisfaction, beliefs, knowledge, attitudes, opinions. However, the methods used in conducting survey research can significantly affect the reliability, validity, and generalizability of study results, and without clear reporting of the methods used in surveys, it is difficult or impossible to assess these characteristics and therefore to have confidence in the findings.
Why Was This Study Done?
This uncertainty in other forms of research has given rise to Reporting Guidelines—evidence-based, validated tools that aim to improve the reporting quality of health research. The STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) Statement includes cross-sectional studies, which often involve surveys. But not all surveys are epidemiological, and STROBE does not include methods' and results' reporting characteristics that are unique to surveys. Therefore, the researchers conducted this study to help determine whether there is a need for a reporting guideline for health survey research.
What Did the Researchers Do and Find?
The researchers identified any previous relevant guidance for survey research, and any evidence on the quality of reporting of survey research, by: reviewing current guidance for reporting survey research in the “Instructions to Authors” of leading medical journals and in published literature; conducting a systematic review of evidence on the quality of reporting of surveys; identifying key quality criteria for the conduct of survey research; and finally, reviewing how these criteria are currently reported by conducting a review of recently published reports of self-administered surveys.
The researchers found that 154 of the 165 journals searched (93.3%) did not provide any guidance on survey reporting, even though the majority (81.8%) have published survey research. Only three of the 11 journals that provided some guidance gave more than one directive or statement. Five papers and one Internet site provided guidance on the reporting of survey research, but none used validated measures or explicit methods for development. The researchers identified eight papers that addressed the quality of reporting of some aspect of survey research: the reporting of response rates; the reporting of non-response analyses in survey research; and the degree to which authors make their survey instrument available to readers. In their review of 117 published survey studies, the researchers found that many items were poorly reported: few studies provided the survey or core questions (35%), reported the validity or reliability of the instrument (19%), discussed the representativeness of the sample (11%), or identified how missing data were handled (11%). Furthermore, (88 [75%]) did not include any information on consent procedures for research participants, and one-third (40 [34%]) of papers did not report whether the study had received research ethics board review.
What Do These Findings Mean?
Overall, these results show that guidance is limited and consensus lacking about the optimal reporting of survey research, and they highlight the need for a well-developed reporting guideline specifically for survey research—possibly an extension of the guideline for observational studies in epidemiology (STROBE)—that will provide the structure to ensure more complete reporting and allow clearer review and interpretation of the results from surveys.
Additional Information
Please access these web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001069.
More than 100 reporting guidelines covering a broad spectrum of research types are indexed on the EQUATOR Networks web site
More information about STROBE is available on the STROBE Statement web site
doi:10.1371/journal.pmed.1001069
PMCID: PMC3149080  PMID: 21829330
15.  Comparative Performance of Private and Public Healthcare Systems in Low- and Middle-Income Countries: A Systematic Review 
PLoS Medicine  2012;9(6):e1001244.
A systematic review conducted by Sanjay Basu and colleagues reevaluates the evidence relating to comparative performance of public versus private sector healthcare delivery in low- and middle-income countries.
Introduction
Private sector healthcare delivery in low- and middle-income countries is sometimes argued to be more efficient, accountable, and sustainable than public sector delivery. Conversely, the public sector is often regarded as providing more equitable and evidence-based care. We performed a systematic review of research studies investigating the performance of private and public sector delivery in low- and middle-income countries.
Methods and Findings
Peer-reviewed studies including case studies, meta-analyses, reviews, and case-control analyses, as well as reports published by non-governmental organizations and international agencies, were systematically collected through large database searches, filtered through methodological inclusion criteria, and organized into six World Health Organization health system themes: accessibility and responsiveness; quality; outcomes; accountability, transparency, and regulation; fairness and equity; and efficiency. Of 1,178 potentially relevant unique citations, data were obtained from 102 articles describing studies conducted in low- and middle-income countries. Comparative cohort and cross-sectional studies suggested that providers in the private sector more frequently violated medical standards of practice and had poorer patient outcomes, but had greater reported timeliness and hospitality to patients. Reported efficiency tended to be lower in the private than in the public sector, resulting in part from perverse incentives for unnecessary testing and treatment. Public sector services experienced more limited availability of equipment, medications, and trained healthcare workers. When the definition of “private sector” included unlicensed and uncertified providers such as drug shop owners, most patients appeared to access care in the private sector; however, when unlicensed healthcare providers were excluded from the analysis, the majority of people accessed public sector care. “Competitive dynamics” for funding appeared between the two sectors, such that public funds and personnel were redirected to private sector development, followed by reductions in public sector service budgets and staff.
Conclusions
Studies evaluated in this systematic review do not support the claim that the private sector is usually more efficient, accountable, or medically effective than the public sector; however, the public sector appears frequently to lack timeliness and hospitality towards patients.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Health care can be provided through public and private providers. Public health care is usually provided by the government through national healthcare systems. Private health care can be provided through “for profit” hospitals and self-employed practitioners, and “not for profit” non-government providers, including faith-based organizations.
There is considerable ideological debate around whether low- and middle-income countries should strengthen public versus private healthcare services, but in reality, most low- and middle-income countries use both types of healthcare provision. Recently, as the global economic recession has put major constraints on government budgets—the major funding source for healthcare expenditures in most countries—disputes between the proponents of private and public systems have escalated, further fuelled by the recommendation of International Monetary Fund (an international finance institution) that countries increase the scope of private sector provision in health care as part of loan conditions to reduce government debt. However, critics of the private health sector believe that public healthcare provision is of most benefit to poor people and is the only way to achieve universal and equitable access to health care.
Why Was This Study Done?
Both sides of the public versus private healthcare debate draw on selected case reports to defend their viewpoints, but there is a widely held view that the private health system is more efficient than the public health system. Therefore, in order to inform policy, there is an urgent need for robust evidence to evaluate the quality and effectiveness of the health care provided through both systems. In this study, the authors reviewed all of the evidence in a systematic way to evaluate available data on public and private sector performance.
What Did the Researchers Do and Find?
The researchers used eight databases and a comprehensive key word search to identify and review appropriate published data and studies of private and public sector performance in low- and middle-income countries. They assessed selected studies against the World Health Organization's six essential themes of health systems—accessibility and responsiveness; quality; outcomes; accountability, transparency, and regulation; fairness and equity; and efficiency—and conducted a narrative review of each theme.
Out of the 102 relevant studies included in their comparative analysis, 59 studies were research studies and 13 involved meta-analysis, with the rest involving case reports or reviews. The researchers found that study findings varied considerably across countries studied (one-third of studies were conducted in Africa and a third in Southeast Asia) and by the methods used.
Financial barriers to care (such as user fees) were reported for both public and private systems. Although studies report that patients in the private sector experience better timeliness and hospitality, studies suggest that providers in the private sector more frequently violate accepted medical standards and have lower reported efficiency.
What Do These Findings Mean?
This systematic review did not support previous views that private sector delivery of health care in low- and middle-income settings is more efficient, accountable, or effective than public sector delivery. Each system has its strengths and weaknesses, but importantly, in both sectors, there were financial barriers to care, and each had poor accountability and transparency. This systematic review highlights a limited and poor-quality evidence base regarding the comparative performance of the two systems.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001244.
A previous PLoS Medicine study examined the outpatient care provided by the public and private sector in low-income countries
The WHO website provides more information on healthcare systems
The World Bank website provides information on health system financing
Oxfam provides an argument against increased private health care in poor countries
doi:10.1371/journal.pmed.1001244
PMCID: PMC3378609  PMID: 22723748
16.  SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials 
High quality protocols facilitate proper conduct, reporting, and external review of clinical trials. However, the completeness of trial protocols is often inadequate. To help improve the content and quality of protocols, an international group of stakeholders developed the SPIRIT 2013 Statement (Standard Protocol Items: Recommendations for Interventional Trials). The SPIRIT Statement provides guidance in the form of a checklist of recommended items to include in a clinical trial protocol.
This SPIRIT 2013 Explanation and Elaboration paper provides important information to promote full understanding of the checklist recommendations. For each checklist item, we provide a rationale and detailed description; a model example from an actual protocol; and relevant references supporting its importance. We strongly recommend that this explanatory paper be used in conjunction with the SPIRIT Statement. A website of resources is also available (www.spirit-statement.org).
The SPIRIT 2013 Explanation and Elaboration paper, together with the Statement, should help with the drafting of trial protocols. Complete documentation of key trial elements can facilitate transparency and protocol review for the benefit of all stakeholders.
doi:10.1136/bmj.e7586
PMCID: PMC3541470  PMID: 23303884
17.  Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration 
BMC Medicine  2012;10:51.
Background
The Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) checklist consists of 20 items to report for published tumor marker prognostic studies. It was developed to address widespread deficiencies in the reporting of such studies. In this paper we expand on the REMARK checklist to enhance its use and effectiveness through better understanding of the intent of each item and why the information is important to report.
Methods
REMARK recommends including a transparent and full description of research goals and hypotheses, subject selection, specimen and assay considerations, marker measurement methods, statistical design and analysis, and study results. Each checklist item is explained and accompanied by published examples of good reporting, and relevant empirical evidence of the quality of reporting. We give prominence to discussion of the 'REMARK profile', a suggested tabular format for summarizing key study details.
Summary
The paper 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.
To encourage dissemination of the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration, this article has also been published in PLoS Medicine.
doi:10.1186/1741-7015-10-51
PMCID: PMC3362748  PMID: 22642691
18.  The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration 
PLoS Medicine  2009;6(7):e1000100.
Alessandro Liberati and colleagues present an Explanation and Elaboration of the PRISMA Statement, updated guidelines for the reporting of systematic reviews and meta-analyses.
Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users.
Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions.
The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.
doi:10.1371/journal.pmed.1000100
PMCID: PMC2707010  PMID: 19621070
19.  Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration 
PLoS Medicine  2007;4(10):e297.
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.
In this explanatory and elaboration document Mattias Egger and colleagues provide the meaning and rationale of each checklist item on the STROBE Statement.
doi:10.1371/journal.pmed.0040297
PMCID: PMC2020496  PMID: 17941715
20.  The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration 
Systematic reviews and meta-analyses are essential to summarise evidence relating to efficacy and safety of healthcare interventions accurately and reliably. The clarity and transparency of these reports, however, are not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users.
Since the development of the QUOROM (quality of reporting of meta-analysis) statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realising these issues, an international group that included experienced authors and methodologists developed PRISMA (preferred reporting items for systematic reviews and meta-analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions.
The PRISMA statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this explanation and elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA statement, this document, and the associated website (www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.
doi:10.1136/bmj.b2700
PMCID: PMC2714672  PMID: 19622552
21.  Genome-Wide Association Studies, Field Synopses, and the Development of the Knowledge Base on Genetic Variation and Human Diseases 
American Journal of Epidemiology  2009;170(3):269-279.
Genome-wide association studies (GWAS) have led to a rapid increase in available data on common genetic variants and phenotypes and numerous discoveries of new loci associated with susceptibility to common complex diseases. Integrating the evidence from GWAS and candidate gene studies depends on concerted efforts in data production, online publication, database development, and continuously updated data synthesis. Here the authors summarize current experience and challenges on these fronts, which were discussed at a 2008 multidisciplinary workshop sponsored by the Human Genome Epidemiology Network. Comprehensive field synopses that integrate many reported gene-disease associations have been systematically developed for several fields, including Alzheimer's disease, schizophrenia, bladder cancer, coronary heart disease, preterm birth, and DNA repair genes in various cancers. The authors summarize insights from these field synopses and discuss remaining unresolved issues—especially in the light of evidence from GWAS, for which they summarize empirical P-value and effect-size data on 223 discovered associations for binary outcomes (142 with P < 10−7). They also present a vision of collaboration that builds reliable cumulative evidence for genetic associations with common complex diseases and a transparent, distributed, authoritative knowledge base on genetic variation and human health. As a next step in the evolution of Human Genome Epidemiology reviews, the authors invite investigators to submit field synopses for possible publication in the American Journal of Epidemiology.
doi:10.1093/aje/kwp119
PMCID: PMC2714948  PMID: 19498075
association; database; encyclopedias; epidemiologic methods; genome, human; genome-wide association study; genomics; meta-analysis
22.  Comparison of tools for assessing the methodological quality of primary and secondary studies in health technology assessment reports in Germany 
Health care policy background
Findings from scientific studies form the basis for evidence-based health policy decisions.
Scientific background
Quality assessments to evaluate the credibility of study results are an essential part of health technology assessment reports and systematic reviews. Quality assessment tools (QAT) for assessing the study quality examine to what extent study results are systematically distorted by confounding or bias (internal validity). The tools can be divided into checklists, scales and component ratings.
Research questions
What QAT are available to assess the quality of interventional studies or studies in the field of health economics, how do they differ from each other and what conclusions can be drawn from these results for quality assessments?
Methods
A systematic search of relevant databases from 1988 onwards is done, supplemented by screening of the references, of the HTA reports of the German Agency for Health Technology Assessment (DAHTA) and an internet search. The selection of relevant literature, the data extraction and the quality assessment are carried out by two independent reviewers. The substantive elements of the QAT are extracted using a modified criteria list consisting of items and domains specific to randomized trials, observational studies, diagnostic studies, systematic reviews and health economic studies. Based on the number of covered items and domains, more and less comprehensive QAT are distinguished. In order to exchange experiences regarding problems in the practical application of tools, a workshop is hosted.
Results
A total of eight systematic methodological reviews is identified as well as 147 QAT: 15 for systematic reviews, 80 for randomized trials, 30 for observational studies, 17 for diagnostic studies and 22 for health economic studies. The tools vary considerably with regard to the content, the performance and quality of operationalisation. Some tools do not only include the items of internal validity but also the items of quality of reporting and external validity. No tool covers all elements or domains. Design-specific generic tools are presented, which cover most of the content criteria.
Discussion
The evaluation of QAT by using content criteria is difficult, because there is no scientific consensus on the necessary elements of internal validity, and not all of the generally accepted elements are based on empirical evidence. Comparing QAT with regard to contents neglects the operationalisation of the respective parameters, for which the quality and precision are important for transparency, replicability, the correct assessment and interrater reliability. QAT, which mix items on the quality of reporting and internal validity, should be avoided.
Conclusions
There are different, design-specific tools available which can be preferred for quality assessment, because of its wider coverage of substantive elements of internal validity. To minimise the subjectivity of the assessment, tools with a detailed and precise operationalisation of the individual elements should be applied. For health economic studies, tools should be developed and complemented with instructions, which define the appropriateness of the criteria. Further research is needed to identify study characteristics that influence the internal validity of studies.
doi:10.3205/hta000085
PMCID: PMC3010881  PMID: 21289880
quality assessment; assessment quality; quality assessment tools; assessment tools; study quality; study assessment; clinical trials; evaluation criteria; methodologic quality; validity; quality; science; risk of bias; bias; confounding; systematic reviews; health technology assessment; HTA; health economics; health economic studies; critical appraisal; quality appraisal; checklists; scales; component ratings; components; tool; studies; interventional studies; observational studies; diagnostic studies; item; meta-analysis; QAT; EBM; evidence-based medicine; standard; epidemiology
23.  How Evidence-Based Are the Recommendations in Evidence-Based Guidelines? 
PLoS Medicine  2007;4(8):e250.
Background
Treatment recommendations for the same condition from different guideline bodies often disagree, even when the same randomized controlled trial (RCT) evidence is cited. Guideline appraisal tools focus on methodology and quality of reporting, but not on the nature of the supporting evidence. This study was done to evaluate the quality of the evidence (based on consideration of its internal validity, clinical relevance, and applicability) underlying therapy recommendations in evidence-based clinical practice guidelines.
Methods and Findings
A cross-sectional analysis of cardiovascular risk management recommendations was performed for three different conditions (diabetes mellitus, dyslipidemia, and hypertension) from three pan-national guideline panels (from the United States, Canada, and Europe). Of the 338 treatment recommendations in these nine guidelines, 231 (68%) cited RCT evidence but only 105 (45%) of these RCT-based recommendations were based on high-quality evidence. RCT-based evidence was downgraded most often because of reservations about the applicability of the RCT to the populations specified in the guideline recommendation (64/126 cases, 51%) or because the RCT reported surrogate outcomes (59/126 cases, 47%).
Conclusions
The results of internally valid RCTs may not be applicable to the populations, interventions, or outcomes specified in a guideline recommendation and therefore should not always be assumed to provide high-quality evidence for therapy recommendations.
From an analysis of cardiovascular risk-management recommendations in guidelines produced by pan-national panels, McAlister and colleagues concluded that fewer than half were based on high-quality evidence.
Editors' Summary
Background.
Until recently, doctors largely relied on their own experience to choose the best treatment for their patients. Faced with a patient with high blood pressure (hypertension), for example, the doctor had to decide whether to recommend lifestyle changes or to prescribe drugs to reduce the blood pressure. If he or she chose the latter, he or she then had to decide which drug to prescribe, set a target blood pressure, and decide how long to wait before changing the prescription if this target was not reached. But, over the past decade, numerous clinical practice guidelines have been produced by governmental bodies and medical associations to help doctors make treatment decisions like these. For each guideline, experts have searched the medical literature for the current evidence about the diagnosis and treatment of a disease, evaluated the quality of that evidence, and then made recommendations based on the best evidence available.
Why Was This Study Done?
The recommendations made in different clinical practice guidelines vary, in part because they are based on evidence of varying quality. To help clinicians decide which recommendations to follow, some guidelines indicate the strength of their recommendations by grading them, based on the methods used to collect the underlying evidence. Thus, a randomized clinical trial (RCT)—one in which patients are randomly allocated to different treatments without the patient or clinician knowing the allocation—provides higher-quality evidence than a nonrandomized trial. Similarly, internally valid trials—in which the differences between patient groups are solely due to their different treatments and not to other aspects of the trial—provide high-quality evidence. However, grading schemes rarely consider the size of studies and whether they have focused on clinical or so-called “surrogate” measures. (For example, an RCT of a treatment to reduce heart or circulation [“cardiovascular”] problems caused by high blood pressure might have death rate as a clinical measure; a surrogate endpoint would be blood pressure reduction.) Most guidelines also do not consider how generalizable (applicable) the results of a trial are to the populations, interventions, and outcomes specified in the guideline recommendation. In this study, the researchers have investigated the quality of the evidence underlying recommendations for cardiovascular risk management in nine evidence-based clinical practice guides using these additional criteria.
What Did the Researchers Do and Find?
The researchers extracted the recommendations for managing cardiovascular risk from the current US, Canadian, and European guidelines for the management of diabetes, abnormal blood lipid levels (dyslipidemia), and hypertension. They graded the quality of evidence for each recommendation using the Canadian Hypertension Education Program (CHEP) grading scheme, which considers the type of study, its internal validity, its clinical relevance, and how generally applicable the evidence is considered to be. Of 338 evidence-based recommendations, two-thirds were based on evidence collected in internally valid RCTs, but only half of these RCT-based recommendations were based on high-quality evidence. The evidence underlying 64 of the guideline recommendations failed to achieve a high CHEP grade because the RCT data were collected in a population of people with different characteristics to those covered by the guideline. For example, a recommendation to use spironolactone to reduce blood pressure in people with hypertension was based on an RCT in which the participants initially had congestive heart failure with normal blood pressure. Another 59 recommendations were downgraded because they were based on evidence from RCTs that had not focused on clinical measures of effectiveness.
What Do These Findings Mean?
These findings indicate that although most of the recommendations for cardiovascular risk management therapies in the selected guidelines were based on evidence collected in internally valid RCTs, less than one-third were based on high-quality evidence applicable to the populations, treatments, and outcomes specified in guideline recommendations. A limitation of this study is that it analyzed a subset of recommendations in only a few guidelines. Nevertheless, the findings serve to warn clinicians that evidence-based guidelines are not necessarily based on high-quality evidence. In addition, they emphasize the need to make the evidence base underlying guideline recommendations more transparent by using an extended grading system like the CHEP scheme. If this were done, the researchers suggest, it would help clinicians apply guideline recommendations appropriately to their individual patients.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040250.
• Wikipedia contains pages on evidence-based medicine and on clinical practice guidelines (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
• The National Guideline Clearinghouse provides information on US national guidelines
• The Guidelines International Network promotes the systematic development and application of clinical practice guidelines
• Information is available on the Canadian Hypertension Education Program (CHEP) (in French and English)
• See information on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group, an organization that has developed an grading scheme similar to the CHEP scheme (in English, Spanish, French, German, and Italian)
doi:10.1371/journal.pmed.0040250
PMCID: PMC1939859  PMID: 17683197
24.  Uses and misuses of the STROBE statement: bibliographic study 
BMJ Open  2011;1(1):e000048.
Objectives
Appropriate reporting is central to the application of findings from research to clinical practice. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations consist of a checklist of 22 items that provide guidance on the reporting of cohort, case–control and cross-sectional studies, in order to facilitate critical appraisal and interpretation of results. STROBE was published in October 2007 in several journals including The Lancet, BMJ, Annals of Internal Medicine and PLoS Medicine. Within the framework of the revision of the STROBE recommendations, the authors examined the context and circumstances in which the STROBE statement was used in the past.
Design
The authors searched the Web of Science database in August 2010 for articles which cited STROBE and examined a random sample of 100 articles using a standardised, piloted data extraction form. The use of STROBE in observational studies and systematic reviews (including meta-analyses) was classified as appropriate or inappropriate. The use of STROBE to guide the reporting of observational studies was considered appropriate. Inappropriate uses included the use of STROBE as a tool to assess the methodological quality of studies or as a guideline on how to design and conduct studies.
Results
The authors identified 640 articles that cited STROBE. In the random sample of 100 articles, about half were observational studies (32%) or systematic reviews (19%). Comments, editorials and letters accounted for 15%, methodological articles for 8%, and recommendations and narrative reviews for 26% of articles. Of the 32 observational studies, 26 (81%) made appropriate use of STROBE, and three uses (10%) were considered inappropriate. Among 19 systematic reviews, 10 (53%) used STROBE inappropriately as a tool to assess study quality.
Conclusions
The STROBE reporting recommendations are frequently used inappropriately in systematic reviews and meta-analyses as an instrument to assess the methodological quality of observational studies.
Article summary
Article focus
Appropriate reporting is central for the proper application of findings from clinical research into clinical practice.
The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations aim to provide guidance to authors on how to improve the reporting of observational studies to facilitate critical appraisal and interpretation of results.
We examined the reasons for citing STROBE and found that most observational studies used STROBE as a reporting guideline, while about half of systematic reviews used STROBE as a tool to assess the methodological quality of the studies.
Key messages
Our study provides further evidence that authors of systematic reviews inappropriately use reporting guidelines to assess methodological study quality. Given the identified common misuse of STROBE, we discuss possible reasons and potential pitfalls of such misuse.
Strengths and limitations of this study
We conducted a systematic review of the literature to address a relevant and insufficiently discussed issue concerning misuses of reporting guidelines. One of the main concerns of such misuse is the potential introduction of bias into systematic reviews and meta-analysis.
A limitation of our findings is the fact that we included only articles which cited STROBE. This may have resulted in a selection bias, since some researchers may use STROBE in their study and mention it in their manuscript but do not formally cite it.
doi:10.1136/bmjopen-2010-000048
PMCID: PMC3191404  PMID: 22021739
Rheumatology; public health; rehabilitation medicine; epidemiology; systematic reviews; prognostic studies; statistics; research designs in field of test evaluations; heterogeneity; bias; diagnostic accuracy; HIV/AIDS; metaanalysis; social medicine; reporting guideline; methodological study; STROBE; methodological quality; quality assessment
25.  Stakeholder Perspectives on a Risk-Benefit Framework for Genetic Testing 
Public Health Genomics  2010;14(2):59-67.
A key to accelerating the appropriate integration of genomic applications into healthcare in the coming decades will be the ability to assess the tradeoffs between clinical benefits and clinical risks of genetic tests in a timely manner. Several factors limit the ability of stakeholders to achieve this objective, including the lack of direct evidence, the lack of a framework to quantitatively assess risk and benefit, and the lack of a formal analytic approach to assess uncertainty. We propose that a formal, quantitative risk-benefit framework may be particularly useful for assessing genetic tests intended to influence health outcomes, and communicating the potential clinical benefits, harms, and uncertainty to stakeholders. As part of the development process for such a framework, a stakeholder meeting was held in Seattle (Wash., USA) in December of 2008, with the objective of discussing a risk-benefit framework, using warfarin pharmacogenomics as a case study. Participants engaged in focused discussion to elucidate the potential role of genetic test risk-benefit analysis in informing decision-making, categorizing genetic tests and directing research prioritization. This research investigation focuses on qualitative analysis of responses elicited from workshop participants during the proceedings of the workshop session. The major findings of the workshop were: (1) stakeholder support for risk-benefit modeling as a tool to structure discussion of the clinical utility of genetic tests; (2) desire for the modeling process to be iterative, transparent, and parsimonious in its presentation to stakeholders, and (3) some concern with the use of quality-adjusted life-years in the evaluation process. The meeting's findings emphasize the potential utility of risk-benefit analysis in genetic test evaluation, and highlight key areas for future research and stakeholder consensus-building.
doi:10.1159/000290452
PMCID: PMC3214932  PMID: 20407215
Genetic testing; Pharmacogenomics; Quality-adjusted life years; Risk-benefit; Stakeholder; Warfarin

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