Table 4.
The number of studies using various statistical analysis techniques
| Analytical method | N |
| Regression modelling | |
| Logistic | |
| Regular | 10 |
| Generalised estimating equation | 5 |
| Multilevel | 1 |
| Linear | 2 |
| Regular | |
| Poisson | |
| Generalised estimating equation* | 1 |
| Multinomial regression | |
| Regular | 1 |
| Cox proportional hazards model | 1 |
| Frailty model | 1 |
| Correlation | |
| Pearson | 9 |
| Spearman | 1 |
| Relative risk/rate ratio† | 8 |
| T-tests | |
| Paired and independent samples | 4 |
| Independent samples only | 2 |
| Χ2 tests | 1 |
| Repeated measures ANOVA (one-way or two-way) | 5 |
If articles used more than one statistical method to analyse workload and injury, they are included more than once in the table. We only report analyses used to analyse workload–injury associations, not other analyses reported in the articles (eg, ANOVA to test for differences in total workloads at separate times of the season).
*Clausen et al 39 also report fitting multilevel models, but do not report any of the results—presenting only their GEE findings in their results and discussion.
†Relative risk here refers to the use of RR as a primary analysis based on risks in different categorical groups, not as an effect estimated from another model. For example, comparing risks among different load groups like Hulin et al 33 47 are counted here, whereas Gabbett and Ullah66 derived RR from their frailty model, and Clausen et al 39 derived RR from their Poisson model, but neither are included in the count for RR.
ANOVA, analysis of variance; GEE, generalised estimating equation.