Thus, the resulting analytical solution explicitly offers all remaining mp−n degrees of freedom beyond eigenvalue assignment which can be used for additional design goals such as response shaping, minimizing the norm of the feedback matrix, and robust control, respectively.
An exchangeable working correlation matrix and robust standard errors were used, with data clustered at the facility level.
The analysis specified an autoregressive (order 1) correlation matrix and robust standard errors and tested linear and quadratic effects of time.
As a sensitivity analysis, model estimates were also obtained using a generalized estimating equations (GEE) with an exchangeable working correlation matrix and robust standard errors.
We used a generalized estimating equations (GEE) model (with an exchangeable correlation matrix and robust standard errors) when analysing the dichotomized outcome, fitness level [ 35].
We also analyzed the data using generalized estimating equations (GEE) with an exchangeable working correlation matrix and robust SEs to adjust for autocorrelation in the data.
Generalized estimating equations (GEE) with a logit link, exchangeable correlation matrix, and robust standard errors were used to test for change in detection of plasma ANG-1 and ANG-2 across time-points.
We applied linear mixed effects modeling with a random intercept, unstructured correlation matrix and robust standard errors, to evaluate changes over time in the primary outcomes in relation to the assigned treatment group.
Once the final model was determined, generalised estimating equations (GEEs) with a logit link, an exchangeable working correlation matrix and robust standard errors were used to obtain the aORs, while accounting for correlation among births to the same mother in the study period [ 42].
We used generalized estimating equations (GEE) models with an auto-regressive correlation matrix and robust standard errors to adjust for the clustered nature of the data here represented by the multiple observation years for each patient (assumption of independence between clusters).
