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Recommendations for accurate reporting in medical research statistics

2024; Elsevier BV; Volume: 403; Issue: 10427 Linguagem: Inglês

10.1016/s0140-6736(24)00139-9

1474-547X

Mohammad Alì Mansournia, Maryam Nazemipour,

Methodology and Impact of Social Science Research

An important requirement for validity of medical research is sound methodology and statistics, yet this is still often overlooked by medical researchers.1Mansournia MA Collins GS Nielsen RO et al.A checklist for statistical assessment of medical papers (the CHAMP statement): explanation and elaboration.Br J Sports Med. 2021; 55: 1009-1017Crossref PubMed Scopus (88) Google Scholar, 2Mansournia MA Collins GS Nielsen RO et al.Checklist for statistical assessment of medical papers: the CHAMP statement.Br J Sports Med. 2021; 55: 1002-1003Crossref PubMed Scopus (38) Google Scholar 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).PanelBasic recommendations for accurate reporting of statistics•Depending on the distribution, report either mean and SD or median and IQR for the description of quantitative variables. Provide supplemental material showing histograms or tables of the variables used in analyses.•Check all model assumptions, preferably with graphs where feasible.•Do not dichotomise p values ≥0·0001; instead, show the precise p value (eg, a p value of 0·032 should be shown as p=0·032, not p<0·05). However, the inequality p<0·0001 can be used to report very small p values.•Do not report results as showing no effect, unless all effects inside the interval estimate are clinically unimportant.•Interpret results on the basis of the clinical importance, with appropriate estimates of association with 95% CIs.•Identify confounders on the basis of background information, as depicted in causal directed acyclic graphs, not significance tests.•If the proportion of missing data is high enough to potentially affect results, use methods beyond simply discarding incomplete records—eg, inverse-probability-of-missingness weighting or multiple imputation.•Assess and handle sparse-data bias in ratio estimates with methods developed for that purpose.•If the outcome frequency is high, report risk ratios or risk differences instead of odds ratios.•Assess additive interactions even if your model is multiplicative. •Depending on the distribution, report either mean and SD or median and IQR for the description of quantitative variables. Provide supplemental material showing histograms or tables of the variables used in analyses.•Check all model assumptions, preferably with graphs where feasible.•Do not dichotomise p values ≥0·0001; instead, show the precise p value (eg, a p value of 0·032 should be shown as p=0·032, not p<0·05). However, the inequality p<0·0001 can be used to report very small p values.•Do not report results as showing no effect, unless all effects inside the interval estimate are clinically unimportant.•Interpret results on the basis of the clinical importance, with appropriate estimates of association with 95% CIs.•Identify confounders on the basis of background information, as depicted in causal directed acyclic graphs, not significance tests.•If the proportion of missing data is high enough to potentially affect results, use methods beyond simply discarding incomplete records—eg, inverse-probability-of-missingness weighting or multiple imputation.•Assess and handle sparse-data bias in ratio estimates with methods developed for that purpose.•If the outcome frequency is high, report risk ratios or risk differences instead of odds ratios.•Assess additive interactions even if your model is multiplicative. 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, meanSD 5%). Better methods include inverse probability weighting and multiple imputation, although these still depend on missingness being conditionally random.14Altman DG Bland JM Missing data.BMJ. 2007; 334: 424Crossref PubMed Scopus (145) Google Scholar, 15Mansournia MA Altman DG Inverse probability weighting.BMJ. 2016; 352: i189Crossref PubMed Scopus (307) Google Scholar An important source of bias in logistic or Cox regression is sparse data—ie, a low number of events in some combinations of levels of variables. Unrealistically large ratio measures with wide interval estimates (eg, an odds ratio >10 with limits of 2 and 50) indicate sparse-data bias, which can be reduced with penalised or Bayesian methods.16Greenland S Mansournia MA Altman DG Sparse data bias: a problem hiding in plain sight.BMJ. 2016; 352i1981PubMed Google Scholar, 17Mansournia MA Geroldinger A Greenland S Heinze G Separation in logistic regression: causes, consequences, and control.Am J Epidemiol. 2018; 187: 864-870Crossref PubMed Scopus (142) Google Scholar When the dependent variable is an indicator of a common outcome, adjusted risk ratios are preferable to odds ratios for assessing clinical relevance, due to their ease of proper interpretation and resistance to sparse-data bias. Risk ratios and differences can be estimated in cohort studies and randomised trials with modified Poisson regression or regression standardisation.18Zou G A modified Poisson regression approach to prospective studies with binary data.Am J Epidemiol. 2004; 159: 702-706Crossref PubMed Scopus (6512) Google Scholar, 19Greenland S Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case-control studies.Am J Epidemiol. 2004; 160: 301-305Crossref PubMed Scopus (599) Google Scholar Many studies try to examine interactions between two treatments on the outcome or want to estimate how much an effect of a treatment is modified by another variable (ie, effect-measure modification). Modellers often add product terms in the regression model such as logistic or Cox, which correspond to multiplicative interactions on the odds or rate scale. However, additive interaction on risks is more relevant for both clinical decisions and public health and so should be assessed as well.20Knol MJ VanderWeele TJ Recommendations for presenting analyses of effect modification and interaction.Int J Epidemiol. 2012; 41: 514-520Crossref PubMed Scopus (746) Google Scholar In either case, studies will usually have little power to establish even the direction of an interaction and risk producing misleading estimates if they screen for interactions with statistical tests. MAM is a statistical reviewer for The Lancet Group. We declare no other competing interests. We thank Sander Greenland and Jay Kaufman for their helpful comments on an earlier draft of this Correspondence.