In addition to the study's secondary hypothesis and exploratory aim pertaining to an age effect on weight gain and metabolic changes, we will conduct exploratory analyses focused on other age-related questions, such as whether age effects the risk of relapse following antipsychotic discontinuation, and if so what are the clinical or biological variables that moderate or mediate this association.
If asymmetry is suggested by a visual assessment, we will perform exploratory analyses to investigate it.
To prepare for the main analyses, we will conduct exploratory analyses to optimize our dataset and statistical models.
Based on these analyses, we will conduct exploratory regression analyses to explore mechanistic pathways to generate hypotheses for future validation.
Additionally, we will perform exploratory analysis to identify clinical indicators for better (cost) effectiveness over a 1-year period using univariable and multivariate logistic regression analysis.
However, to provide some insight in this, we will provide exploratory dose-response analyses in PCMT between participation in each element and the effect on pain and stress.
Finally, we will use exploratory regression methods to determine if baseline disability, psychological factors, MRI variables, and electrodiagnostic measures correlate with clinical outcomes.
We will conduct exploratory linear regression analysis using b as the dependent variable and clinician characteristics listed above as the independent variables.
We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.
We will let exploratory data analysis and model checking determine whether we model the outcome by inverting randomization tests or via robust OLS estimation, though our default is to rely on additive effects and inversion of sharp null hypothesis tests (see Annex A).
