**Analysis method** |
**Strengths** |
**Limitations** |
**Notes** |

The van Elteren test and the Tadap2 tests |
No underlying assumptions about the distribution of the observed data are necessary.
It is possible to adjust for stratum effects. |
Only possible to adjust the data for one stratification variable.
Only two intervention groups can be compared.
The Tadap2 test is available only in a code written in R. |
Non-parametric test.
Often the optimal choice of analysis method. |

Bootstrapping |
No underlying assumptions about the distribution of the observed data are necessary. |
The uncertainty of the non-parametric confidence interval provided via bootstrapping could be large if it is based on a limited number of observations. |
Non-parametric method to estimate the confidence interval of, e.g., means difference or median difference of two treatments. |

The Kruskalâ€“Wallis tests |
Multiple intervention groups can be compared.
No underlying assumptions about the distribution of the observed data are necessary. |
The stratified version of the test is available in R only. |
Non-parametric method.
A valid method but only if more than two groups have to be compared. |

The Wilcoxon rank sum test |
No underlying assumptions about the distribution of the observed data are necessar |
Not possible to adjust for stratum effects. |
Non-parametric test.
The van Elteren test is often a better choice. |

Generalised linear model |
Trial results can be adjusted for multiple stratification variables.
Odds ratios and confidence intervals can demonstrate the intervention effects.
Multiple intervention groups can be compared. |
It can be impossible to deal with some data due to overdispersion[12].
Model checking will often show that the underlying assumptions of the models are not fulfilled. |
Parametric method.
Often not an optimal choice of analysis method. |

Generalised linear mixed model |
Trial results can be adjusted for multiple stratification variables.
Odds ratios and confidence intervals can demonstrate the intervention effects.
Multiple groups can be compared.
Can often handle overdispersion. |
Complicated analysis.
Checking the underlying assumptions is not easy. |
Parametric method.
Often not an optimal choice of analysis method. |