Statistical methods for primary and secondary outcomes {20a}
REGRESSION
Processing and evaluation of data include checks for the plausibility of spirometry data and microbiome data. Moreover, descriptive data analysis will be used to check the plausibility of the distribution of data. For evaluating contextual and compositional determinants, low and high levels of outcome data will be used. In addition, potential determinants for each of the outcomes will be explored by linear regression modelling and will be checked for potential adjustment. Within these approaches, different methods of confounder adjustment (general fixed and random mixed models) will be applied for different association patterns of determinants and risk factors and for risk factor-outcome relationships in cases of clustered risk factors. For repeatedly evaluated measures throughout regression models for longitudinal data, such as the generalized estimating equations (GEE) approach may be applied.To account for the control arm of this interventional study, multilevel statistical models can be used that integrate information on risk factor-outcome relationships on two complementary levels of intervention and non-interventional group. Such multilevel models can take confounding variables fully into account and are well-suited for studying or identifying specific group effects.