Statistical analyses
TB, MI-RT, PreMeno MI-RT, FM
TRANSFORMATION
Data were analyzed by the R statistical language version 4.0.4 (R Core Team, 2021). Only data from participants with an adherence of > 85% were included in the analyses. The raw data of VL, RF, and TB were box-cox transformed prior to analysis. The transformation was applied to obtain approximately Gaussian distributions of the raw data, which otherwise exhibited highly skewed distributions. Transformations were applied to the data at TThe values of FFM, MM, FM, GS, 1-RM SQ and BP were analyzed unchanged. Individual time intervals (∆t [h]) since the start of the study were introduced as an additional covariate. Data analysis was performed using linear mixed effects (LME) models with FFMWe were specifically interested in the effects of menopause on the trends of strength capacity and muscle growth. Therefore, all models included the interaction term of menopause with PreMeno MI-RT (T0 to T2) as a fixed effect. Likewise, PreMeno MI-RT itself was axiomatically included as a fixed effect. As it represents an ordered factor with three levels, second-order orthogonal polynomials were chosen as contrasts for PreMeno MI-RT.Initially, random effects were merely assumed between the individual intercepts of each measure. Subsequently, ∆t was included as a random effect, where linear individual trends were assumed. The presence of potentially non-linear individual trends was then investigated by upgrading to 2nd or 3rd-order natural splines of ∆t. After developing appropriate random effect structures, it was tested whether ∆t also contributed to general trends in the population, i.e., whether it represented a significant fixed effect. In either case, model comparisons were based on likelihood statistics and changes in Akaike’s information criterion. Significant differences were set at p ≤ .05.Finally, effect sizes between discrete time levels were determined according to the approximation of Cohen’s 