A CHecklist for statistical Assessment of Medical Papers (the CHAMP statement): explanation and elaboration

Mansournia, Mohammad Ali; Collins, Gary S.; Nielsen, Rasmus Oestergaard; Nazemipour, Maryam; Jewell, Nicholas P.; Altman, Douglas G.; Campbell, Mike

doi:10.1136/bjsports-2020-103652

Cited by 131 publications

(104 citation statements)

References 111 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These references provide more detailed information on how to report the results of statistical analyses for a trial: (44,45).…”

Section: Results: Focus On Main Analysis and Between-group Differencesmentioning

confidence: 99%

See 1 more Smart Citation

Writing up your clinical trial report for a scientific journal: The REPORT trial guide for effective and transparent research reporting without spin

Bandholm¹,

Thorborg²,

Ardern³

et al. 2021

Preprint

View full text Add to dashboard Cite

The REPORT guide is a “How to” guide to help you achieve effective and transparent clinical trial reporting. It is intended to supplement “first choice” reporting tools, such as CONSORT, by adding tacit knowledge about reporting topics we have struggled with as authors or see others struggle with as journal reviewers or editors. Focus is the randomised controlled trial, but the guide is useful for other study designs as well. Topics included in the REPORT guide cover reporting checklists, trial report structure, choice of title, writing style, trial registry and reporting consistency, spin or reporting bias, transparent data presentation (figures), open access considerations, data sharing, and much more. We hope you find it useful.Preprint (open access): https://doi.org/xxxxxxxxxxxxxxx

show abstract

“…These references provide more detailed information on how to report the results of statistical analyses for a trial: (44,45).…”

Section: Results: Focus On Main Analysis and Between-group Differencesmentioning

confidence: 99%

“…Excellent educational resources exist to assist you. They include the CONSORT explanation and elaboration paper (4), the SAMPL guidelines for statistical reporting (44), and the recently developed Checklist for statistical Assessment of Medical Papers: the CHAMP statement (45).…”

Section: Methods: Statistical Analysesmentioning

confidence: 99%

Writing up your clinical trial report for a scientific journal: The REPORT trial guide for effective and transparent research reporting without spin

Bandholm¹,

Thorborg²,

Ardern³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Lastly, in criterion (8), we assessed if funding instructions incentivised applicants to collaborate with a statistician. We included this criterion because statisticians are often involved in research design [ 24 ] and as experts in the peer review process [ 25 ], but many published studies in our fields still describe common errors from inappropriate statistical techniques. We wanted to assess if funders are aware of these limitations and seek to rectify them.…”

Section: Discussionmentioning

confidence: 99%

Strengthening the incentives for responsible research practices in Australian health and medical research funding

Diong

Kroeger

Reynolds

et al. 2021

Res Integr Peer Rev

View full text Add to dashboard Cite

Background Australian health and medical research funders support substantial research efforts, and incentives within grant funding schemes influence researcher behaviour. We aimed to determine to what extent Australian health and medical funders incentivise responsible research practices. Methods We conducted an audit of instructions from research grant and fellowship schemes. Eight national research grants and fellowships were purposively sampled to select schemes that awarded the largest amount of funds. The funding scheme instructions were assessed against 9 criteria to determine to what extent they incentivised these responsible research and reporting practices: (1) publicly register study protocols before starting data collection, (2) register analysis protocols before starting data analysis, (3) make study data openly available, (4) make analysis code openly available, (5) make research materials openly available, (6) discourage use of publication metrics, (7) conduct quality research (e.g. adhere to reporting guidelines), (8) collaborate with a statistician, and (9) adhere to other responsible research practices. Each criterion was answered using one of the following responses: “Instructed”, “Encouraged”, or “No mention”. Results Across the 8 schemes from 5 funders, applicants were instructed or encouraged to address a median of 4 (range 0 to 5) of the 9 criteria. Three criteria received no mention in any scheme (register analysis protocols, make analysis code open, collaborate with a statistician). Importantly, most incentives did not seem strong as applicants were only instructed to register study protocols, discourage use of publication metrics and conduct quality research. Other criteria were encouraged but were not required. Conclusions Funders could strengthen the incentives for responsible research practices by requiring grant and fellowship applicants to implement these practices in their proposals. Administering institutions could be required to implement these practices to be eligible for funding. Strongly rewarding researchers for implementing robust research practices could lead to sustained improvements in the quality of health and medical research.

show abstract

“…We derived the point estimates and 95% simulation intervals (for simplicity, called confidence intervals in this paper) using the median and 2.5 th and 97.5 th percentiles of Monte Carlo distribution (15). Multiple logistic regression was used to obtain the adjusted odds ratios (ORs) with 95% CIs between variables and anti_N IgG seropositivity (16)(17)(18). Paired t-test was used for comparing anti-N and anti-S titers on the logarithmic scale, and the results were presented as geometric mean ratio with 95% CI.…”

Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 seroprevalence in asymptomatic or mild symptomatic people and symptomatic patients with negative PCR results: The hidden perspective in epidemiological reports

Hajiahmadi

mojtahedzade

Yari

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Background: SARS-CoV-2 has led to the current pandemic of respiratory disease. The reports of confirmed COVID-19 cases based on molecular tests do not completely cover the total number of infected people. These reports do not include the asymptomatic or mildly symptomatic patients and also the patients with false-negative RT-PCR results, while the infection is contagious in all of these conditions. Objective: In this study, we tried to improve our conception of the hidden perspective of SARS-CoV-2 in epidemiological reports. Methods: From May 30 to June 17, 2020, blood samples were collected from two groups of people: asymptomatic or mild symptomatic volunteer participants and severe symptomatic hospitalized patients with negative PCR results. Detection of SARS-CoV-2 antibody was done with ELISA kit targeting N or S proteins. Results: Totally 716 samples from volunteer participants and 81 samples from symptomatic hospitalized patients with negative PCR were evaluated. The test performance-adjusted seroprevalence (95% CI) of SARS-CoV-2 anti-N IgG was 17.3% (8.8%, 25.8%) for volunteers and 25.5% (12.8%, 39.7%) for anti-N and S IgM in hospitalized group. There was an association between high-risk occupations, high-risk behaviors, or symptomatic diseases with positive SARA-Cov-2 N antibody results. Among anti-N positive infected individuals, 49.2% (21.4%, 78.8%) were anti-S positive. Conclusion: The results showed that SARS-COV-2 infection occurs in asymptomatic or mildly symptomatic individuals, but in more than half of them, the produced antibody is not protective. Findings of hospitalized patients also showed that the combination of IgM assay with real-time PCR improves the detection of the disease by more than 25% in negative molecular cases.

show abstract

A CHecklist for statistical Assessment of Medical Papers (the CHAMP statement): explanation and elaboration

Cited by 131 publications

References 111 publications

Writing up your clinical trial report for a scientific journal: The REPORT trial guide for effective and transparent research reporting without spin

Writing up your clinical trial report for a scientific journal: The REPORT trial guide for effective and transparent research reporting without spin

Strengthening the incentives for responsible research practices in Australian health and medical research funding

SARS-CoV-2 seroprevalence in asymptomatic or mild symptomatic people and symptomatic patients with negative PCR results: The hidden perspective in epidemiological reports

Contact Info

Product

Resources

About