# Table 1 Review of statistical analysis methods [22,23,24,25,26,27]

Analysis method Outcome type Statistical assumptions Advantages Disadvantages
Binary logistic regression (BLR) Binary • No assumptions made about explanatory variables • Can adjust for covariates • Large number of observations required
Cox proportional hazards (CPH) Binary • Proportionality of hazards over time
• Censoring of observations is unrelated to prognosis
• Can adjust for covariates • If assumptions of the model not met then subsequent analyses and risk estimates will possibly be biased
Chi-square (χ2) (CS) Binary and ordered categorical • Chi-Square – Total count is > 40 or total count is 20–40 and the expected value of each exposure-outcome category is > 5 • Simple to implement • Cannot adjust for covariates
Cochran-Armitage trend test (CAT) Ordered categorical • Similar to the Chi-square test but it takes into account the ordering across categories • Easy to interpret • Cannot adjust for covariates
Ordinal logistic regression (OLR) Ordered categorical • Response is ordinal
• Proportionality of odds
• Can adjust for covariates • If assumptions of the model not met then subsequent analyses and odds estimates will possibly be biased
Mann-Whitney U test (MWU) Ordered categorical • Non-parametric test
• Response is ordinal / continuous
• Observations from both groups are independent of one another
• Easy to interpret • Cannot adjust for covariates – there are extensions of this method, which allow for adjustment [28,29,30]
Median test (MT) Ordered categorical • Non-parametric test
• Considers the position of each observation relative to the overall median.
• Easy to interpret • Cannot adjust for covariates
• Inefficient (low power) to detect differences if sample size is large.
t-test Continuous (used on the ordered categorical) • Response is continuous
• Homogeneity of variances
• Easy to interpret • Cannot adjust for covariates
Multiple linear regression (MLR) Continuous (used on the ordered categorical) • Response is continuous
• Linear relationship
• Homogeneity of variances
• No or little multicollinearity
• Can adjust for covariates • Assumes linear relationship
• Sensitive to outliers
Win Ratio testWins/losses version (WR) Combination of binary outcomes • Responses for each outcome are binary
• Accounts for clinical priorities of endpoints
• Prioritises the more major component of the outcome
• Useful for composite outcomes
• Extensions of this approach allow for covariate adjustment [31]
• Easy to interpret
• New method
• Doesn’t use the precise times from randomisation to event occurrence
Bootstrapping
(BS)
Ordered categorical • None • No assumptions made about the distribution of the data • Cannot adjust for covariates
• Computationally intensive
• Doesn’t provide a meaningful point estimate