Table 1.
Parameters used for simulations approximated from Cho et al. (2013).
Table 2.
The reporting characteristics of 58 biomedical animal experiments with repeated measurement of an outcome.
Fig 1.
Data analysis using heterogeneous_CS as in Eq (1), homogeneous_CS in Eq (3), and separate ANOVA based on the null model simulation with ρ = 0.3, n = 12 and a heterogeneous_CS variance covariance structure.
Trt: overall treatment effect. Trt*Time: Treatment by Time interaction. Week 1, Week 7, Week 14, Week 21, and Week 28: p-value distribution at each time point. Proportion = count per bin/1000.
Fig 2.
SEM estimation using heterogeneous_CS as in Eq (1), homogeneous_CS in Eq (3), and separate ANOVA based on the null model simulation with ρ = 0.3, n = 12 and a heterogeneous_CS variance covariance structure.
Week 1, Week 7, Week 14, Week 21, and Week 28: SEM distribution for pairwise comparisons between treatments at each time point. Proportion = count per bin/1000.
Fig 3.
Data analysis using heterogeneous_CS as in Eq (1), homogeneous_CS in Eq (3), and separate ANOVA based on the alternative model simulation with ρ = 0.3, n = 12 and a heterogeneous_CS variance covariance structure.
Trt: overall treatment effect; Trt*Time: the overall treatment-by-time interaction; week 1, Week 7, Week 14, Week 21, and Week 28: p-value distribution at each time point. Proportion = count per bin/1000.
Fig 4.
SEM estimation using repeated measures analysis with either heterogeneous_CS as in Eq (1), homogeneous_CS in Eq (3), and separate ANOVA analysis approaches at each time point based on the alternative model simulation with ρ = 0.3, n = 12 and a heterogeneous_CS variance covariance structure.
Week 1, Week 7, Week 14, Week 21, and Week 28: SEM distribution for pairwise comparison between treatments at each time point. Proportion = count per bin/1000.