Methods
Grouped by outcome type. Not sure which one you need? Start the test chooser.
Continuous outcomes
BP, HbA1c, length of stay
- ANCOVA (adjusting for baseline)Compares groups on a continuous follow-up outcome while adjusting for the baseline value of that same outcome.Adjusted difference in means
- Friedman testCompares a skewed measurement or an ordered score across three or more conditions in the same patients, using ranks.
- Kruskal-Wallis testRank-based test of whether a skewed measurement or an ordered score differs between three or more independent groups.
- Linear mixed modelAnalyzes a continuous outcome measured repeatedly in the same patients, or in clustered patients, accounting for the correlation between measurements.Difference in means
- Linear regressionEstimates how a continuous outcome changes with one or more factors, holding the others fixed.Difference in means per unit / vs reference
- Mann-Whitney U testCompares a skewed measurement or an ordered score between two independent groups using ranks instead of raw values.Hodges-Lehmann shift or median difference
- One-sample t-testCompares the mean of a measurement in one group with a fixed reference value that was specified in advance.
- One-way ANOVATests in one step whether the mean of a measurement differs between three or more independent groups.Differences in means (post hoc)
- Paired t-testCompares the mean of a measurement between two time points or conditions in the same patients, using each patient's difference.Mean within-patient difference
- Pearson correlationMeasures how strongly two numeric measurements are related in a straight line, from −1 to 1.
- Sign test or Wilcoxon signed-rank testCompares the centre of a skewed measurement in one group with a known reference value, using signs or ranks.
- Spearman rank correlationMeasures how strongly two measurements move together using ranks, so skew and curves that keep one direction are fine.
- Summary-measures analysisReduce each patient to one number, then use a standard two-group test
- Two-way ANOVACompares the mean of a measurement between the groups formed by two factors, and tests whether the effect of one factor depends on the other (an interaction).Differences in means, and the interaction
- Unpaired t-test on log-transformed dataCompares two independent groups on the log scale for right-skewed data, and reports a ratio of geometric means.Ratio of geometric means
- Welch's t-testCompares the mean of a measurement between two independent groups, without assuming both groups have the same spread.Difference in means
- Wilcoxon signed-rank testCompares paired measurements using the ranks of the within-patient differences, for skewed differences or ordered scores.Pseudo-median of differences
Binary outcomes
Death, complication, readmission
- Chi-square testCompares the proportion of patients with a yes/no outcome between independent groups.Risk difference / risk ratio
- Chi-square test (r × c table)Compares a category or yes/no outcome across three or more independent groups.
- Cochran's Q testCompares a yes/no outcome across three or more conditions in the same patients, as an extension of McNemar's test.
- Conditional logistic regressionEstimates an odds ratio in matched case-control data, adjusted for other variables, by comparing within matched sets.Odds ratio
- Fisher's exact testCompares the proportion of patients with a yes/no outcome between independent groups when some groups have very few events.Odds ratio
- GEE and mixed-effects logistic regressionAnalyzes a yes/no outcome measured repeatedly in the same patients, or in clustered patients, accounting for the correlation within patients or clusters.Odds ratio (marginal vs conditional)
- Log-binomial regressionEstimates a prevalence ratio in a cross-sectional study or a risk ratio in a cohort or trial with fixed follow-up. Poisson with robust SE if it fails to converge.Prevalence ratio or risk ratio
- Logistic regressionEstimates how the odds of a yes/no outcome change with one or more factors, holding the others fixed.Odds ratio
- McNemar testCompares a yes/no outcome measured twice in the same patients, or in matched pairs, using only the pairs that changed.Difference in paired proportions
- z-test for a single proportionCompares the proportion of patients with a yes/no outcome in one group with a known reference proportion.
Categorical outcomes
Blood group, other unordered categories
Time-to-event outcomes
Survival, relapse, time to discharge
- Competing risks: cumulative incidence and Fine-GrayEstimates the cumulative incidence of an event when another event can prevent it, instead of treating the competing event as censoring.Subdistribution hazard ratio; cumulative incidence
- Cox proportional hazards regressionEstimates how the hazard of an event changes with one or more factors, holding the others fixed.Hazard ratio
- Kaplan-Meier curves and log-rank testDescribes the probability of staying event-free over time in each group, and compares groups with the log-rank test.Survival probability at time t; median survival
Ordinal outcomes
mRS, pain scores, Likert items
- Friedman testCompares a skewed measurement or an ordered score across three or more conditions in the same patients, using ranks.
- Kruskal-Wallis testRank-based test of whether a skewed measurement or an ordered score differs between three or more independent groups.
- Mann-Whitney U testCompares a skewed measurement or an ordered score between two independent groups using ranks instead of raw values.Hodges-Lehmann shift or median difference
- Spearman rank correlationMeasures how strongly two measurements move together using ranks, so skew and curves that keep one direction are fine.
- Wilcoxon signed-rank testCompares paired measurements using the ranks of the within-patient differences, for skewed differences or ordered scores.Pseudo-median of differences
- Proportional odds (ordinal logistic) regressionCompares an ordered score between groups while adjusting for other variables, giving one common odds ratio across the cut-points of the score.Common odds ratio
Counts and rates
Exacerbations, admissions per year
Accuracy and agreement
A new test, two raters, two devices
- Bland-Altman plot and ICCShows how closely two methods that measure the same quantity agree, with the average difference and the range in which most differences fall.Bias and 95% limits of agreement; ICC
- Cohen's kappaMeasures agreement between two raters or methods that assign categories to the same patients, beyond what chance alone would give.Kappa (weighted for ordered categories)
- Diagnostic accuracy: sensitivity, specificity, ROCQuantifies how well a new test identifies patients with the condition, compared with a reference standard.Sensitivity, specificity, AUC with CI
Prediction
Risk for individual patients