亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Recommendations for accurate reporting in medical research statistics

医学研究 医学统计学 心理学 统计 统计分析 数据科学 医学 医学教育 计算机科学 数学 病理
作者
Mohammad Alì Mansournia,Maryam Nazemipour
出处
期刊:The Lancet [Elsevier BV]
卷期号:403 (10427): 611-612 被引量:44
标识
DOI:10.1016/s0140-6736(24)00139-9
摘要

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<2indicates serious skewness.3Altman DG Bland JM Detecting skewness from summary information.BMJ. 1996; 3131200Crossref Scopus (334) Google Scholar 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.4Altman DG Royston P The cost of dichotomising continuous variables.BMJ. 2006; 3321080Crossref Google Scholar, 5Binney ZO Mansournia MA Methods matter: (mostly) avoid categorising continuous data—a practical guide.Br J Sports Med. 2023; (published online Nov 28.)https://doi.org/10.1136/bjsports-2023-107599Crossref Scopus (0) Google Scholar 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.6Greenland S Mansournia MA Joffe M To curb research misreporting, replace significance and confidence by compatibility: a Preventive Medicine Golden Jubilee article.Prev Med. 2022; 164107127Crossref PubMed Scopus (22) Google Scholar, 7Mansournia MA Nazemipour M Etminan M p-value, compatibility, and s-value.Glob Epidemiol. 2022; 4100085Google Scholar Over-reliance 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.8Altman DG Bland JM Statistics notes: absence of evidence is not evidence of absence.BMJ. 1995; 311: 485Crossref PubMed Scopus (1287) Google Scholar 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. The research question for many studies is causality, for which confounding adjustment is crucial. Confounders should be selected on the basis of background causal information—eg, as depicted in a directed acyclic graph.9Greenland S Pearl J Robins JM Causal diagrams for epidemiologic research.Epidemiology. 1999; 10: 37-48Crossref PubMed Scopus (2821) Google Scholar, 10Lipsky AM Greenland S Causal directed acyclic graphs.JAMA. 2022; 327: 1083-1084Crossref PubMed Scopus (65) Google Scholar Significance-based methodologies, such as stepwise selection algorithms, can be highly misleading because they could omit important confounders.11Etminan M Collins GS Mansournia MA Using causal diagrams to improve the design and interpretation of medical research.Chest. 2020; 158: S21-S28Summary Full Text Full Text PDF PubMed Scopus (71) Google Scholar, 12Etminan M Brophy JM Collins G Nazemipour M Mansournia MA To adjust or not to adjust: the role of different covariates in cardiovascular observational studies.Am Heart J. 2021; 237: 62-67Crossref PubMed Scopus (38) Google Scholar, 13Kyriacou DN Greenland P Mansournia MA Using causal diagrams for biomedical research.Ann Emerg Med. 2023; 81: 606-613Summary Full Text Full Text PDF Scopus (3) Google Scholar Missing data is common. Simple methods of handling missing data, such as complete-case analysis (ie, listwise deletion), missingness indicators, or last-observation-carried-forward, can be subject to considerable bias and should be avoided if the proportion of missing data is high (eg, >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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
wanci应助淡然绝山采纳,获得10
3秒前
40秒前
wkjfh应助YaoQi采纳,获得10
1分钟前
dyuguo3完成签到 ,获得积分10
1分钟前
某某某发布了新的文献求助10
1分钟前
1分钟前
淡然绝山发布了新的文献求助10
1分钟前
Benhnhk21完成签到,获得积分10
1分钟前
JamesPei应助andrele采纳,获得10
2分钟前
完美世界应助xiongdi521采纳,获得10
2分钟前
2分钟前
2分钟前
xiongdi521发布了新的文献求助10
2分钟前
2分钟前
xiao涂完成签到,获得积分10
3分钟前
3分钟前
闫雪发布了新的文献求助10
3分钟前
打打应助闫雪采纳,获得10
3分钟前
吗喽完成签到,获得积分10
3分钟前
某某某完成签到,获得积分10
3分钟前
4分钟前
爱吃橙子完成签到 ,获得积分10
4分钟前
丰富莹芝发布了新的文献求助10
4分钟前
思源应助丰富莹芝采纳,获得10
4分钟前
Akitten发布了新的文献求助10
4分钟前
qqq完成签到,获得积分10
4分钟前
4分钟前
小博发布了新的文献求助10
5分钟前
童大大完成签到,获得积分20
5分钟前
小博完成签到,获得积分10
5分钟前
CodeCraft应助糯糯采纳,获得10
6分钟前
汉堡包应助Plum22采纳,获得10
6分钟前
岁岁完成签到 ,获得积分10
7分钟前
7分钟前
Plum22发布了新的文献求助10
7分钟前
7分钟前
充电宝应助科研通管家采纳,获得10
7分钟前
cherlie应助Plum22采纳,获得20
8分钟前
8分钟前
8分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3990171
求助须知:如何正确求助?哪些是违规求助? 3532136
关于积分的说明 11256472
捐赠科研通 3271042
什么是DOI,文献DOI怎么找? 1805171
邀请新用户注册赠送积分活动 882302
科研通“疑难数据库(出版商)”最低求助积分说明 809234