Recommendations for accurate reporting in medical research statistics An important requirement for validity of medical research is sound methodology and statistics, yet this is still often overlooked by medical researchers.1,2 Based on the experience of reviewing statistics in more than 1000 manuscripts submitted to The Lancet Group of journals over the past 3 years, this Correspondence provides guidance to commonly encountered statistical deficiencies in reports and how to avoid them (panel). Data description is crucial to making sense of data. The mean and SD are often used for the description of quantitative variables. Nonetheless, for highly skewed variables (eg, typical environmental exposures) the median and IQR should be used instead; for variables that take only positive values, indicates serious skewness.3 Full data descriptions also require histograms of continuous variables and tabulation of counts for categorical variables, along with percentages of missing data. Due to the volume of such descriptions, they can be given as supplementary material. All statistical analyses are based on fundamental assumptions, such as randomness of selection or treatment assignment. The validity of statistical modelling depends on further assumptions that should be assessed and, for this purpose, statistical tests are inadequate—graphical methods are needed. An important assumption underlying most regression models is linearity (on some scale) for quantitative predictors, which should be assessed with methods such as fractional polynomials or regression splines. In particular, categorisation of quantitative variables assumes an unrealistic step function, which can result in power loss or uncontrolled confounding.4,5 Statistical inference remains heavily based on hypothesis testing and estimation. However, p values can provide useful information about the compatibility of data with statistical hypotheses or models and so should be reported precisely, not replaced by qualitative comments about being significant or not. Compatibility can be gauged through transformations of p values, called s values, based on coin-tossing experiments.6,7 Overreliance on statistical testing should be avoided and p values should not be dichotomised at levels such as 0·05 or 0·01. In particular, large p values should not be interpreted as showing no association or no effect: absence of evidence is not evidence of absence.8 Only a very narrow interval estimate near the null value (0 for differences, 1 for ratios) warrants inferring that the study found no important association or effect. More generally, the clinical importance of results should be judged on the basis of interval estimates of appropriate measures, such as the difference of means or of risks.

Basic recommendations for accurate reporting of statistics

LEGGI TUTTO https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00139-9/fulltext?dgcid=raven_jbs_etoc_email#:~:text=DOI%3A-,https%3A//doi.org/10.1016/S0140%2D6736(24)00139%2D9,-Previous%20Article