Chapter 3 98 3.2. Statistical analysis specifications To improve replicability, Supplementary Table S3.3 below displays the model specifications of the study outcomes. All analyses were conducted in Rstudio, using the lme4 package (Bates et al., 2015) and lmerTest package (Kuznetsova et al., 2017). Time was added as a categorical predictor, since we hypothesized was that there would be a non-linear effect of time on symptoms after GAHT start, with possible distinct differences between changes after 3 months vs. 12 months of GAHT. Table S3.3. Statistical model specifications per study outcome. Study aim Parameter Specification Results section Exploratory factor analysis Dataset All baseline measurements 3.2 Depression profile at the start of GAHT Model Exploratory factor analysis using Maximum Likelihood estimation and Oblimin rotation, testing a 2-, 3-, 4- and 5factor model. Comparison of TM and TF groups at baseline Dataset All baseline measurements 3.2 Depression profile at the start of GAHT Model Linear Regression: Outcome score 1. Group (categorical; TM or TF) Longitudinal analyses in the overall group Dataset All measurements (i.e. baseline, 3 monthfollow up, 12-month follow-up) of participants who contributed both a baseline and at least one follow-up measurement. 3.3 Depression profile changes after start of GAHT Model Linear Mixed model: Outcome score 1. Baseline score (continuous) + Measurement time point (categorical; baseline, 3 months or 12 months) + (1 | Participant number) Longitudinal analyses stratified by group (i.e. TM and TF) and differences between the group Dataset All measurements (i.e. baseline, 3 monthfollow up, 12-month follow-up) of participants who contributed both a baseline and at least one follow-up measurement. 3.3 Depression profile changes after start of GAHT Model Linear Mixed model: Outcome score 1. Baseline score (continuous) + Measurement time point (categorical; baseline, 3 months or 12 months) * Group (categorical; TM or TF) + (1 | Participant number)
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