Misuse of statistics in medical and sports science research is common and may lead to detrimental consequences to healthcare. Many authors, editors and peer reviewers of medical papers will not have expert knowledge of statistics or may be unconvinced about the importance of applying correct statistics in medical research. Although there are guidelines on reporting statistics in medical papers, a checklist on the more general and commonly seen aspects of statistics to assess when peer-reviewing an article is needed. In this article, we propose a CHecklist for statistical Assessment of Medical Papers (CHAMP) comprising 30 items related to the design and conduct, data analysis, reporting and presentation, and interpretation of a research paper. While CHAMP is primarily aimed at editors and peer reviewers during the statistical assessment of a medical paper, we believe it will serve as a useful reference to improve authors’ and readers’ practice in their use of statistics in medical research. We strongly encourage editors and peer reviewers to consult CHAMP when assessing manuscripts for potential publication. Authors also may apply CHAMP to ensure the validity of their statistical approach and reporting of medical research, and readers may consider using CHAMP to enhance their statistical assessment of a paper.

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We determined the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in an affected area in northern Iran in April 2020. Antibodies to SARS-CoV-2 were detected in 528 persons by using rapid tests. Adjusted prevalence of SARS-CoV-2 seropositivity was 22.2% (95% CI 16.4%-28.5%).

All statistical estimates from data have uncertainty due to sampling variability. A standard error is one measure of uncertainty of a sample estimate (such as the mean of a set of observations or a regression coefficient). Standard errors are usually calculated based on assumptions underpinning the statistical model used in the estimation. However, there are situations in which some assumptions of the statistical model including the variance or covariance of the outcome across observations are violated, which leads to biased standard errors. One simple remedy is to userobust standard errors, which are robust to violations of certain assumptions of the statistical model. Robust standard errors are frequently used in clinical papers (e.g. to account for clustering of observations), although the underlying concepts behind robust standard errors and when to use them are often not well understood. In this paper, we demystify robust standard errors using several worked examples in simple situations in which model assumptions involving the variance or covariance of the outcome are misspecified. These are: (i) when the observed variances are different, (ii) when the variance specified in the model is wrong and (iii) when the assumption of independence is wrong.

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