Registration of observational studies: is it time?

ISPOR Code of Ethics 2017 (4th Edition)

As the leading health economics and outcomes research (HEOR) professional society, ISPOR has a responsibility to establish a uniform, harmonized international code for ethical conduct. ISPOR has updated its 2008 Code of Ethics to reflect the current research environment. This code addresses what is acceptable and unacceptable in research, from inception to the dissemination of its results. There are nine chapters: 1 - Introduction; 2 - Ethical Principles respect, beneficence and justice with reference to a non-exhaustive compilation of international, regional, and country-specific guidelines and standards; 3 - Scope HEOR definitions and how HEOR and the Code relate to other research fields; 4 - Research Design Considerations primary and secondary data related issues, e.g., participant recruitment, population and research setting, sample size/site selection, incentive/honorarium, administration databases, registration of retrospective observational studies and modeling studies; 5 - Data Considerations privacy and data protection, combining, verification and transparency of research data, scientific misconduct, etc.; 6 - Sponsorship and Relationships with Others (roles of researchers, sponsors, key opinion leaders and advisory board members, research participants and institutional review boards (IRBs) / independent ethics committees (IECs) approval and responsibilities); 7 - Patient Centricity and Patient Engagement new addition, with explanation and guidance; 8 - Publication and Dissemination; and 9 - Conclusion and Limitations.

Inconsistent selection of outcomes and measurement devices found in shoulder arthroplasty research: An analysis of studies on ClinicalTrials.gov

Introduction

Recent evidence suggests a lack of standardization of shoulder arthroplasty outcomes. This issue is a limiting factor in systematic reviews. Core outcome set (COS) methodology could address this problem by delineating a minimum set of outcomes for measurement in all shoulder arthroplasty trials.

Methods

A ClinicalTrials.gov search yielded 114 results. Eligible trials were coded on the following characteristics: study status, study type, arthroplasty type, sample size, measured outcomes, outcome measurement device, specific metric of measurement, method of aggregation, outcome classification, and adverse events.

Results

Sixty-six trials underwent data abstraction and data synthesis. Following abstraction, 383 shoulder arthroplasty outcomes were organized into 11 outcome domains. The most commonly reported outcomes were shoulder outcome score (n = 58), pain (n = 33), and quality of life (n = 15). The most common measurement devices were the Constant-Murley Shoulder Outcome Score (n = 38) and American Shoulder and Elbow Surgeons Shoulder Score (n = 33). Temporal patterns of outcome use was also found.

Conclusion

Our study suggests the need for greater standardization of outcomes and instruments. The lack of consistency across trials indicates that developing a core outcome set for shoulder arthroplasty trials would be worthwhile. Such standardization would allow for more effective comparison across studies in systematic reviews, while at the same time consider important outcomes that may be underrepresented otherwise. This review of outcomes provides an evidence-based foundation for the development of a COS for shoulder arthroplasty.

Oral health status of children and adolescents with intellectual disabilities: a systematic review and meta-analysis

AIM

To compare the oral health status of children and adolescents affected by intellectual disabilities with their unaffected counterparts.

METHOD

Citations published in English were searched from electronic databases (PubMed, Embase, Web of Science, and Scopus) from their start dates to March 2017. The whole process was conducted following PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. The PICO (population, intervention/interest, comparator, outcome) principle was used to formulate the topic. Studies were synthesized through qualitative summary or, whenever possible, meta-analysis.

RESULTS

The initial search yielded 2393 records. Thirty-nine studies from 22 countries were identified for qualitative analysis; 26 studies were eligible for meta-analysis. Participants with intellectual disabilities had higher levels of dental plaque, worse gingival status, fewer decayed and filled permanent teeth, and similar caries experience between males and females. These findings were supported by both qualitative and quantitative analysis. Various patterns of caries experiences were indicated by qualitative analysis, but it was not substantiated by meta-analysis.

INTERPRETATION

There is increasing worldwide interest in oral health status of children with intellectual disabilities. Differences in dental plaque deposition, gingival inflammation, and the number of decayed and filled permanent teeth were investigated between children and adolescents with and without intellectual disabilities. Evidence remains elusive about the pattern of caries experience among those children.

Perspective: An Extension of the STROBE Statement for Observational Studies in Nutritional Epidemiology (STROBE-nut): Explanation and Elaboration

Nutritional epidemiology is an inherently complex and multifaceted research area. Dietary intake is a complex exposure and is challenging to describe and assess, and links between diet, health, and disease are difficult to ascertain. Consequently, adequate reporting is necessary to facilitate comprehension, interpretation, and generalizability of results and conclusions. The STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement is an international and collaborative initiative aiming to enhance the quality of reporting of observational studies. We previously presented a checklist of 24 reporting recommendations for the field of nutritional epidemiology, called "the STROBE-nut." The STROBE-nut is an extension of the general STROBE statement, intended to complement the STROBE recommendations to improve and standardize the reporting in nutritional epidemiology. The aim of the present article is to explain the rationale for, and elaborate on, the STROBE-nut recommendations to enhance the clarity and to facilitate the understanding of the guidelines. Examples from the published literature are used as illustrations, and references are provided for further reading.

A review of routinely collected data studies in urology: Methodological considerations, reporting quality, and future directions

Studies using routinely collected data (RCD) are common in the urological literature; however, there are important considerations in the creation and review of RCD discoveries. A recent reporting guideline (REporting of studies Conducted using Observational Routinely-collected health Data, RECORD) was developed to improve the reporting of these studies. This narrative review examines important considerations for RCD studies. To assess the current level of reporting in the urological literature, we reviewed all the original research articles published in Journal of Urology and European Urology in 2014, and determined the proportion of the RECORD checklist items that were reported for RCD studies. There were 56 RCD studies identified among the 608 articles. When the RECORD items were considered applicable to the specific study, they were reported in 52.5% of cases. Studies most consistently (>80% of them) reported the names of the data sources, the study time frame, the extent to which the authors could access the database source, the patient selection, and discussed missing data. Few studies (<25%) discussed validation of key coding elements, details on data-linkage, data-cleaning, the impact of changing eligibility over time, or provided the complete list of coding elements used to define key study variables. Reporting factors specifically relevant in RCD studies may serve to increase the quality of these studies in the urological literature. With increased technological integration in healthcare and the proliferation of electronic medical records, RCD will continue to be an important source for urological research.

Publication proportions for registered breast cancer trials: before and following the introduction of the ClinicalTrials.gov results database

Background

To limit selective and incomplete publication of the results of clinical trials, registries including ClinicalTrials.gov were introduced. The ClinicalTrials.gov registry added a results database in 2008 to enable researchers to post the results of their trials as stipulated by the Food and Drug Administration Amendment Act of 2007. This study aimed to determine the direction and magnitude of any change in publication proportions of registered breast cancer trials that occurred since the inception of the ClinicalTrials.gov results database.

Methods

A cross-sectional study design was employed using ClinicalTrials.gov, a publicly available registry/results database as the primary data source. Registry contents under the subcategories ‘Breast Neoplasms’ and ‘Breast Neoplasms, Male’ were downloaded on 1 August 2015. A literature search for included trials was afterwards conducted using MEDLINE and DISCOVER databases to determine publication status of the registered breast cancer trials.

Results

Nearly half (168/340) of the listed trials had been published, with a median time to publication of 24 months (Q1 = 14 months, Q3 = 42 months). Only 86 trials were published within 24 months of completion. There was no significant increase in publication proportions of trials that were completed before the introduction of the results database compared to those completed after (OR = 1.00, 95 % CI = .61 to 1.63; adjusted OR = 0.84, 95 % CI = .51 to 1.39). Characteristics associated with publication included trial type (observational versus interventional adjusted OR = .28, 95 % CI = .10 to .74) and completion/termination status (terminated versus completed adjusted OR = .22, 95 % CI = .09 to .51).

Conclusions

Less than a half of breast cancer trials registered in ClinicalTrials.gov are published in peer-reviewed journals.

Electronic supplementary material

The online version of this article (doi:10.1186/s41073-016-0017-4) contains supplementary material, which is available to authorized users.

Transparency and Reproducibility of Observational Cohort Studies Using Large Healthcare Databases

The scientific community and decision-makers are increasingly concerned about transparency and reproducibility of epidemiologic studies using longitudinal healthcare databases. We explored the extent to which published pharmacoepidemiologic studies using commercially available databases could be reproduced by other investigators. We identified a nonsystematic sample of 38 descriptive or comparative safety/effectiveness cohort studies. Seven studies were excluded from reproduction, five because of violation of fundamental design principles, and two because of grossly inadequate reporting. In the remaining studies, >1,000 patient characteristics and measures of association were reproduced with a high degree of accuracy (median differences between original and reproduction <2% and <0.1). An essential component of transparent and reproducible research with healthcare databases is more complete reporting of study implementation. Once reproducibility is achieved, the conversation can be elevated to assess whether suboptimal design choices led to avoidable bias and whether findings are replicable in other data sources.

Social relationships and cognitive decline: a systematic review and meta-analysis of longitudinal cohort studies

BACKGROUND

Although poor social relationships are assumed to contribute to cognitive decline, meta-analytic approaches have not been applied. Individual study results are mixed and difficult to interpret due to heterogeneity in measures of social relationships. We conducted a systematic review and meta-analysis to investigate the relation between poor social relationships and cognitive decline.

METHODS

MEDLINE, Embase and PsycINFO were searched for longitudinal cohort studies examining various aspects of social relationships and cognitive decline in the general population. Odds ratios (ORs) with 95% confidence intervals (CIs) were pooled using random effects meta-analysis. Sources of heterogeneity were explored and likelihood of publication bias was assessed. We stratified analyses according to three aspects of social relationships: structural, functional and a combination of these.

RESULTS

We identified 43 articles. Poor social relationships predicted cognitive decline; for structural (19 studies): pooled OR: 1.08 (95% CI: 1.05-1.11); functional (8 studies): pooled OR: 1.15 (95% CI: 1.00-1.32); and combined measures (7 studies): pooled OR: 1.12 (95% CI: 1.01-1.24). Meta-regression and subgroup analyses showed that the heterogeneity could be explained by the type of social relationship measurement and methodological quality of included studies.

CONCLUSIONS

Despite heterogeneity in study design and measures, our meta-analyses show that multiple aspects of social relationships are associated with cognitive decline. As evidence for publication bias was found, the association might be overestimated and should therefore be interpreted with caution. Future studies are needed to better define the mechanisms underlying these associations. Potential causality of this prognostic association should be examined in future randomized controlled studies.

Common scientific and statistical errors in obesity research

This review identifies 10 common errors and problems in the statistical analysis, design, interpretation, and reporting of obesity research and discuss how they can be avoided. The 10 topics are: 1) misinterpretation of statistical significance, 2) inappropriate testing against baseline values, 3) excessive and undisclosed multiple testing and "P-value hacking," 4) mishandling of clustering in cluster randomized trials, 5) misconceptions about nonparametric tests, 6) mishandling of missing data, 7) miscalculation of effect sizes, 8) ignoring regression to the mean, 9) ignoring confirmation bias, and 10) insufficient statistical reporting. It is hoped that discussion of these errors can improve the quality of obesity research by helping researchers to implement proper statistical practice and to know when to seek the help of a statistician.

Public availability of results of observational studies evaluating an intervention registered at ClinicalTrials.gov

BACKGROUND

Observational studies are essential for assessing safety. The aims of this study were to evaluate whether results of observational studies evaluating an intervention with safety outcome(s) registered at ClinicalTrials.gov were published and, if not, whether they were available through posting on ClinicalTrials.gov or the sponsor website.

METHODS

We identified a cohort of observational studies with safety outcome(s) registered on ClinicalTrials.gov after October 1, 2007, and completed between October 1, 2007, and December 31, 2011. We systematically searched PubMed for a publication, as well as ClinicalTrials.gov and the sponsor website for results. The main outcomes were the time to the first publication in journals and to the first public availability of the study results (i.e. published or posted on ClinicalTrials.gov or the sponsor website). For all studies with results publicly available, we evaluated the completeness of reporting (i.e. reported with the number of events per arm) of safety outcomes.

RESULTS

We identified 489 studies; 334 (68%) were partially or completely funded by industry. Results for only 189 (39%, i.e. 65% of the total target number of participants) were published at least 30 months after the study completion. When searching other data sources, we obtained the results for 53% (n = 158; i.e. 93% of the total target number of participants) of unpublished studies; 31% (n = 94) were posted on ClinicalTrials.gov and 21% (n = 64) on the sponsor website. As compared with non-industry-funded studies, industry-funded study results were less likely to be published but not less likely to be publicly available. Of the 242 studies with a primary outcome recorded as a safety issue, all these outcomes were adequately reported in 86% (114/133) when available in a publication, 91% (62/68) when available on ClinicalTrials.gov, and 80% (33/41) when available on the sponsor website.

CONCLUSIONS

Only 39% of observational studies evaluating an intervention with safety outcome(s) registered at ClinicalTrials.gov had their results published at least 30 months after study completion. The registration of these observational studies allowed searching other sources (results posted at ClinicalTrials.gov and sponsor website) and obtaining results for half of unpublished studies and 93% of the total target number of participants.

The Ethics of Sports Medicine Research

This article explores the background and foundations of ethics in research. Some important documents and codes are mentioned, such as The Belmont Report and the International Conference of Harmonisation. Some influential historical events involving research ethics are recounted. The article provides a detailed discussion of the Declaration of Helsinki, which is considered the international standard for guidelines in medical research ethics. The most salient features of the Declaration are described and related to orthopaedic surgery and sports medicine. Some of the most controversial aspects of the Declaration are discussed, which helps examine contentious areas of research in sports medicine.

Trends in global clinical trial registration: an analysis of numbers of registered clinical trials in different parts of the world from 2004 to 2013

OBJECTIVES

To analyse developments (and their causes) in the number and proportion of clinical trials that were registered in different parts of the world after the International Committee of Medical Journal Editors (ICMJE) announced in 2004 that it would require registration of clinical trials as a condition for publication.

SETTING

The International Clinical Trials Registry Platform (ICTRP).

DESIGN

The ICTRP database was searched for all clinical trials that were registered up to 31 December 2013.

RESULTS

The ICTRP database contained data on 186,523 interventional clinical trials. The annual number of registered clinical trials increased from 3294 in 2004 to 23,384 in 2013. Relative to the number of clinical trial research publications, the global number of registered clinical trials increased fivefold between 2004 and 2013, rising particularly strongly between 2004 and 2005. In certain regions, especially Asia, the annual number of registered trials increased more gradually and continued to increase up to 2013. In India and Japan, two countries with marked but more gradual increases, these increases only happened after several local measures were implemented that encouraged and enforced registration. In most regions, there was a trend toward trials being registered at local registries.

CONCLUSIONS

Clinical trial registration has greatly improved transparency in clinical trial research. However, these improvements have not taken place equally in all parts of the world. Achieving compliance with registration requires a coalescence of global and local measures, and remains a key challenge in many countries. Poor quality of registered trial data and the inaccessibility of trial protocols, results and participant-level data further undermine the potential benefits of clinical trial registration. National and regional registries and the ICTRP have played a leading role in achieving the successes of trial registration to date and should be supported in addressing these challenges in the future.

Registration practices for observational studies on ClinicalTrials.gov indicated low adherence

OBJECTIVE

The study aims to assess the status of registration of observational studies.

STUDY DESIGN AND SETTING

We identified studies on cancer research with prospective recruitment of participants that were registered from February 2000 to December 2011 in ClinicalTrials.gov. We recorded the dates of registration and start of recruitment, outcomes, and description of statistical method. We searched for publications corresponding to the registered studies through May 31, 2014.

RESULTS

One thousand one hundred nine registered studies were eligible. Primary and secondary outcomes were reported in 809 (73.0%) and 464 (41.8%) of them. The date of registration preceded the month of the study start in 145 (13.8%) and coincided in 205 (19.5%). A total of 151 publications from 120 (10.8%) registered studies were identified. In 2 (33.3%) of the 6 publications where ClinicalTrials.gov reported that the study started recruitment after registration, and in 9 (50.0%) of 18 publications where ClinicalTrials.gov reported the same date for registration and start of recruitment, the articles showed that the study had actually started recruiting before registration.

CONCLUSION

During the period reviewed, few observational studies have been registered. Registration usually occurred after the study started, and prespecification of outcomes and statistical analysis rarely occurred.

Disclosure of research results: a randomized study on GENEPSO-PS cohort participants

BACKGROUND

There exist no recommendations as to how aggregate research results should best be disclosed to long-term cohort participants.

OBJECTIVE

To study the impact of cohort results disclosure documents of various kinds on participants' satisfaction.

DESIGN

Randomized study with a 2x2 factorial design.

SETTING AND PARTICIPANTS

The GENEPSO-PS cohort is used to study the psychosocial characteristics and preventive behaviour of both BRCA1/2 carriers and non-carriers; 235 participants wishing to receive 'information about the survey results' answered a self-administered questionnaire.

INTERVENTIONS

The impact of providing the following items in addition to a leaflet about aggregate psychosocial research results was investigated (i) an up-to-date medical information sheet about BRCA1/2 genetic topics, (ii) a photograph with the names of the researchers.

MAIN OUTCOME MEASURES

Satisfaction profiles drawn up using cluster analysis methods.

RESULTS

Providing additional medical and/or research team information had no significant effect on satisfaction. The patients attributed to the 'poorly satisfied' group (n = 60, 25.5%) differed significantly from those in the 'highly satisfied' group (n = 51, 21.7%): they were younger [odds ratio (OR) = 0.96, 95% confidence interval (0.92-0.99), P = 0.028], less often had a daughter [OR = 4.87 (1.80-13.20), P = 0.002], had reached a higher educational level [OR = 2.94 (1.24-6.95), P = 0.014] and more frequently carried a BRCA1/2 mutation [OR = 2.73 (1.20-6.23), P = 0.017].

CONCLUSIONS

This original approach to disclosing research results to cohort participants was welcomed by most of the participants, but less by the more educated and by BRCA1/2 carriers. Although an easily understandable document is necessary, it might also be worth providing some participants with more in-depth information.

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement

CONTEXT

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.

OBJECTIVE

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.

EVIDENCE ACQUISITION

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.

EVIDENCE SYNTHESIS

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).

PATIENT SUMMARY

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.

Raising the bar for reproducible science at the U.S. Environmental Protection Agency Office of Research and Development

Considerable concern has been raised regarding research reproducibility both within and outside the scientific community. Several factors possibly contribute to a lack of reproducibility, including a failure to adequately employ statistical considerations during study design, bias in sample selection or subject recruitment, errors in developing data inclusion/exclusion criteria, and flawed statistical analysis. To address some of these issues, several publishers have developed checklists that authors must complete. Others have either enhanced statistical expertise on existing editorial boards, or formed distinct statistics editorial boards. Although the U.S. Environmental Protection Agency, Office of Research and Development, already has a strong Quality Assurance Program, an initiative was undertaken to further strengthen statistics consideration and other factors in study design and also to ensure these same factors are evaluated during the review and approval of study protocols. To raise awareness of the importance of statistical issues and provide a forum for robust discussion, a Community of Practice for Statistics was formed in January 2014. In addition, three working groups were established to develop a series of questions or criteria that should be considered when designing or reviewing experimental, observational, or modeling focused research. This article describes the process used to develop these study design guidance documents, their contents, how they are being employed by the Agency’s research enterprise, and expected benefits to Agency science. The process and guidance documents presented here may be of utility for any research enterprise interested in enhancing the reproducibility of its science.

Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement

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).

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

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).

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement

Prediction models are developed to aid healthcare 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, healthcare 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).

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement

BACKGROUND

Prediction models are developed to aid healthcare 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.

MATERIALS AND 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, healthcare 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).

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement

BACKGROUND

Prediction models are developed to aid healthcare 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.

METHODS

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, healthcare 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.

CONCLUSION

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. A complete checklist is available at http://www.tripod-statement.org.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

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).

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group

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).

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration

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.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement

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).

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement

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).

Improving Concordance in Environmental Epidemiology: A Three-Part Proposal

In observational research, evidence is usually derived from multiple studies, and any single result is rarely considered sufficient for public health decision making. Despite more than five decades of research and thousands of studies published, the ability to draw robust conclusions regarding the presence or absence of causal links between specific environmental exposures and human health remains limited. To develop policies that are protective of public health and can withstand scrutiny, agencies need to rely on investigations of satisfactory quality that follow sufficiently concordant protocols in terms of exposure assessment, outcome ascertainment, data analysis, and reporting of results. Absent such concordance, the ability of environmental epidemiology studies to inform decision making is greatly diminished. Systems and tools are proposed here to improve concordance among environmental epidemiology studies. Specifically, working systems in place in other fields of research are critically examined and used as guidelines to develop analogous policies and procedures for environmental epidemiology. A three-part path forward toward more concordant, transparent, and readily accessible environmental epidemiology evidence that parallels ongoing efforts in medical research is proposed. The three parts address methods for improving quality and accessibility of systematic reviews, access to information on ongoing and completed studies, and principles for reporting. The goals are to increase the value of epidemiological research in public health decision making and to stimulate discussions around solutions proposed herein.

Observational studies: getting clear about transparency

The PLOS Medicine Editors endorse four measures to ensure transparency in the analysis and reporting of observational studies. Please see later in the article for the Editors' Summary.

Improving the transparency of prognosis research: the role of reporting, data sharing, registration, and protocols

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

Infrequent and incomplete registration of test accuracy studies: analysis of recent study reports

OBJECTIVES

To identify the proportion of articles reporting on test accuracy for which the corresponding study had been registered.

DESIGN

Analysis of a consecutive sample of published study reports.

PARTICIPANTS

PubMed was searched for publications in journals with an impact factor of 5 or higher in May and June 2012. Articles were included if they reported on original studies evaluating the accuracy of one or more diagnostic or prognostic tests or markers against a clinical reference standard in humans.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome was registration of the reported test accuracy study. We additionally explored study characteristics associated with registration.

RESULTS

We found 1941 references; 351 study reports fulfilled the inclusion criteria, of which 52 studies (15%) had been registered. Of these, 27 (52%) provided a registration number in the publication, and 12 (23%) provided a reference to the publication in the registry. Registration rates were similar for studies on diagnostic versus those on prognostic tests, and among studies on imaging tests versus those on laboratory techniques. Studies reporting some form of industry involvement were more often registered (33%) than studies reporting another source of funding (11%), and studies without a (reported) source of (external) funding (9%; p<0.001). Of the registered studies, 8 (15%) had been registered after completion, 14 were registered before initiation (27%) and 30 (58%) between initiation and completion. Only 16 (31%; 5% of the total sample) had registered the published primary outcome measures before completion.

CONCLUSIONS

Few test accuracy studies published in higher impact journals are registered. Only 1 in 22 of such studies register their primary outcomes before study completion. Owing to the reasons for registering studies that investigate the cause-and-effect relationship between health-related interventions and health outcomes also apply to test accuracy studies, prospective study registration of these studies should be further promoted among investigators and journal editors.

Increasing value and reducing waste: addressing inaccessible research

The methods and results of health research are documented in study protocols, full study reports (detailing all analyses), journal reports, and participant-level datasets. However, protocols, full study reports, and participant-level datasets are rarely available, and journal reports are available for only half of all studies and are plagued by selective reporting of methods and results. Furthermore, information provided in study protocols and reports varies in quality and is often incomplete. When full information about studies is inaccessible, billions of dollars in investment are wasted, bias is introduced, and research and care of patients are detrimentally affected. To help to improve this situation at a systemic level, three main actions are warranted. First, academic institutions and funders should reward investigators who fully disseminate their research protocols, reports, and participant-level datasets. Second, standards for the content of protocols and full study reports and for data sharing practices should be rigorously developed and adopted for all types of health research. Finally, journals, funders, sponsors, research ethics committees, regulators, and legislators should endorse and enforce policies supporting study registration and wide availability of journal reports, full study reports, and participant-level datasets.

Applications of propensity score methods in observational comparative effectiveness and safety research: where have we come and where should we go?

Propensity score (PS) methods have proliferated in recent years in observational studies in general and in observational comparative effectiveness research (CER) in particular. PS methods are an important set of tools for estimating treatment effects in observational studies, enabling adjustment for measured confounders in an easy-to-understand and transparent way. This article demonstrates how PS methods have been used to address specific CER questions from 2001 through to 2012 by identifying six impactful studies from this period. This article also discusses areas for improvement, including data infrastructure, and a unified set of guidelines in terms of PS implementation and reporting, which will boost confidence in evidence generated through observational CER using PS methods.

The Ontology of Clinical Research (OCRe): an informatics foundation for the science of clinical research

To date, the scientific process for generating, interpreting, and applying knowledge has received less informatics attention than operational processes for conducting clinical studies. The activities of these scientific processes - the science of clinical research - are centered on the study protocol, which is the abstract representation of the scientific design of a clinical study. The Ontology of Clinical Research (OCRe) is an OWL 2 model of the entities and relationships of study design protocols for the purpose of computationally supporting the design and analysis of human studies. OCRe's modeling is independent of any specific study design or clinical domain. It includes a study design typology and a specialized module called ERGO Annotation for capturing the meaning of eligibility criteria. In this paper, we describe the key informatics use cases of each phase of a study's scientific lifecycle, present OCRe and the principles behind its modeling, and describe applications of OCRe and associated technologies to a range of clinical research use cases. OCRe captures the central semantics that underlies the scientific processes of clinical research and can serve as an informatics foundation for supporting the entire range of knowledge activities that constitute the science of clinical research.

Legal Requirements, Definitions, and Standards for Non-interventional Drug Studies: A Global Picture of Variability-Results and Conclusions From a Single-Institution Survey

Non-interventional studies (NIS) have become increasingly important in the continuous benefit-risk assessment of medicines. However, the diversity in study designs and in requirements necessitates a careful planning of NIS. In view of a changing regulatory environment, a company-internal online survey was initiated to gather information on existing standards, definitions, and requirements for NIS and to draw advice for the future conduct of multinational NIS. Answers from 45 countries worldwide depicted a global picture of variability in terms of legal and regulatory requirements for NIS. Definitions and terminology are lacking harmonization, and different good practice standards are concurrently in use. Variations in terms of applicable standards and requirements were observed within most geographic regions. The methodological variety in terms of study designs and the divergent perspectives on NIS constitute communicative barriers. Because of the absence of one worldwide applicable good practice standard, differences in semantics and regulatory systems contribute to system disparities.

Strategies for postmarketing surveillance of drugs and devices in patients with ESRD undergoing dialysis

The lack of evidence on the effectiveness and safety of interventions in chronic dialysis patients has been a subject of continuing criticism. New technologies are often introduced into the market without having specifically studied or even included patients with advanced kidney disease. Therefore, the need to generate valid effectiveness and safety data in this vulnerable subpopulation is of utmost importance. The US Food and Drug Administration has recently placed an increased focus on safety surveillance, and sponsors must now meet this additional postmarketing commitment. In patients with ESRD, the unique data collection environment in the United States allows for creative and efficient study designs to meet the needs of patients, providers, and sponsors. The purpose of this manuscript is to review the methodological and practical aspects of the different options for postmarketing study design in this field, with critical appraisal of their advantages and disadvantages.

Efficacy versus effectiveness trials: informing guidelines for asthma management

PURPOSE OF REVIEW

Randomized controlled trials, known as efficacy trials and long considered the gold standard for evidence-based asthma guidelines, are designed to test whether interventions have a benefit for selective patient populations under ideal conditions. The goal of pragmatic trials and observational studies instead is to understand real-life efficacy, known as effectiveness. This review summarizes the strengths and limitations of efficacy and effectiveness trials, results of recent effectiveness trials in asthma and initiatives promoting effectiveness research.

RECENT FINDINGS

Recent pragmatic trials and observational studies have examined outcomes of interventions for diverse real-life patient populations, including smokers and patients with variable adherence, inhaler technique and baseline asthma control. Study results challenge practice guidelines regarding relative effectiveness of leukotriene receptor antagonists and inhaled corticosteroids (ICS); supplement guidelines with regard to effectiveness of interventions in smokers; and begin to address gaps in guidelines regarding choice of ICS and inhaler device. Initiatives are ongoing to refine methods of observational research and to harmonize asthma outcomes for better integration of results from all types of trials.

SUMMARY

Results of pragmatic trials and observational studies are an important component of the evidence needed to inform guideline recommendations and decision-making by healthcare providers, patients and policymakers.

Use of data from registered clinical trials to identify gaps in health research and development

OBJECTIVE

To explore what can be learnt about the current composition of the "global landscape" of health research and development (R&D) from data on the World Health Organization's International Clinical Trials Registry Platform (ICTRP).

METHODS

A random 5% sample of the records of clinical trials that were registered as interventional and actively recruiting was taken from the ICTRP database.

FINDINGS

Overall, 2381 records of trials were investigated. Analysis of these records indicated that, for every million disability-adjusted life years (DALYs) caused by communicable, maternal, perinatal and nutritional conditions, by noncommunicable diseases, or by injuries, the ICTRP database contained an estimated 7.4, 52.4 and 6.0 trials in which these causes of burden of disease were being investigated, respectively. For every million DALYs in high-income, upper-middle-income, lower-middle-income and low-income countries, an estimated 292.7, 13.4, 3.0 and 0.8 registered trials, respectively, were recruiting in such countries.

CONCLUSION

The ICTRP constitutes a valuable resource for assessing the global distribution of clinical trials and for informing policy development for health R&D. Populations in lower-income countries receive much less attention, in terms of clinical trial research, than populations in higher-income countries.

The HOPE (Helping to Outline Paediatric Eating Disorders) Project: development and debut of a paediatric clinical eating disorder registry

BACKGROUND

The HOPE (Helping to Outline Paediatric Eating Disorders) Project is an ongoing registry study made up of a sequential cross-sectional sample prospectively recruited over 17 years, and is designed to answer empirical questions about paediatric eating disorders. This paper introduces the HOPE Project, describes the registry sample to-date, and discusses future directions and challenges and accomplishments. The project and clinical service were established in a tertiary academic hospital in Western Australia in 1996 with a service development grant. Research processes were inbuilt into the initial protocols and data collection was maintained in the following years. Recognisable progress with the research agenda accelerated only when dedicated research resources were obtained. The registry sample consists of consecutive children and adolescents assessed at the eating disorder program from 1996 onward. Standardised multidisciplinary data collected from family intake interview, parent and child clinical interviews, medical review, parent, child and teacher psychometric assessments, and inpatient admission records populate the HOPE Project database.

RESULTS

The registry database to-date contains 941 assessments, of whom 685 met DSM-IV diagnostic criteria for an eating disorder at admission. The majority of the sample were females (91%) from metropolitan Perth (83%). The cases with eating disorders consist of eating disorders not otherwise specified (68%), anorexia nervosa (25%) and bulimia nervosa (7%). Among those with eating disorders, a history of weight loss since illness onset was almost universal (96%) with fear of weight gain (71%) common, and the median duration of illness was 8 months.

CONCLUSIONS

Over the next five years and more, we expect that the HOPE Project will make a strong scientific contribution to paediatric eating disorders research and will have important real-world applications to clinical practice and policy as the research unfolds.