# Table 3 Categories of statistical methods used to assess the statistical content of articles

CategoryBrief descriptionInclude
No statistical methods or descriptive statistics onlyDescribe basic features of data to provide simple measures of summariesNo statistical content, or descriptive statistics only e.g., percentages, means, standard deviations, standard errors, histograms
Contingency tablesCross tabulations used to summarize the relationship between categorical dataChi-square test, Fisher’s exact test, McNemar’s test
Epidemiologic statisticsMeasures of association for outcome of interest such as disease and some exposure(s)Relative risk, risk ratios, rate ratios, risk difference, rate difference, odds ratio, log odds, risk difference, attributable risk fraction, sensitivity, specificity
Multiway tablesExtend two-way relationships to include three or more variablesMantel–Haenszel procedure, log-linear models, logistic regression
t-testAssess mean differences between groupsOne-sample, matched-pair, two-sample t-tests
Pearson’s correlationMeasures linear correlation between two variablesClassical product-moment correlation
Simple linear regressionRegression that summarizes relationships between two continuous variables, an explanatory and a responseLeast-squares regression with one predictor and one response variable
Multiple regressionExtends the simple regression to include two or more explanatory variables for a responsePolynomial regression and stepwise regression
Analysis of varianceAssess within and between group differences in meansAnalysis of variance, Analysis of covariance, simple linear contrasts, F-tests
Multiple comparisonsMethods for handling multiple inferences on same data setsBonferroni techniques, Scheffé’s contrasts, Duncan multiple-range methods, Newman–Keuls procedure
Non-parametric testTests used when data is not assumed to follow a particular distribution, and are based on ranks of dataSign test, Wilcoxon signed-rank test, Mann–Whitney test, Kruskal–Wallis test, Friedman test, Kolmogorov–Smirnov test
Non-parametric correlationMeasure strength and direction of association between two variablesSpearman’s rho, Kendall’s tau, monotone regression, test for trend
Survival analysisMethods where outcome variable is the time until the occurrence of an eventActuarial life table, Kaplan–Meier estimator for survival, survival function, Cox model, other parametric survival models, rate adjustment, log-rank test, Breslow’s test
Sensitivity analysisExamines sensitivity of outcome to small changes in parameters of model or in other assumptionsSample size, multiple outcomes, model distribution assumptions
TransformationUse of data transformation often in regressionNatural logarithm, square, cubic
Cluster analysisInvolves dividing a multivariate dataset into “natural” clusters (groups) for in-depth assessmentHierarchical, K-means, two-step clustering
Repeated-measures analysisApproaches that account for correlation for within-participant observations and non-constant variance in response over timeGeneralized estimating equations (GEE), mixed-effects models, repeated measures ANOVA
OtherOther methods not specified in aboveReceiver-operating characteristic, principal component analysis, power analysis, propensity score