In principle, one could use other classification models, for example linear discriminant analysis (LDA), a standard classification algorithm that combines a model such as (7) with a marginal model for (x_{t}.) However, Hastie et al. (2009) have argued that it is often preferable to stick to a model such as (7) rather than rely on LDA in practice.
Indeed this was the resampling method for the longitudinal marginal model for the Early RA data.
The first specifies a (marginal) model for and assumes (1) so that we can define.
This leads to the following marginal model for (ξ i, η i ): (3) This model is just the same as (1), only with a different parametrization.
The hazard function of the marginal model for the j t h event for the i t h subject is h ij (t ) = h 0 j (t ) exp (β j ′ Z i (t ) ).
Our primary analyses will be based on changes in two outcomes: depressive symptoms, and a multivariable, scaled marginal model for the combined outcome of global disease control (i.e., A1c, systolic blood pressure, LDL cholesterol).
Marginal model for methylation (M ihl) is given in a similar fashion: (3) M i h l = η i l + a i h + d i h l, i = 1, ⋯, I, h = 1, ⋯, H i, l = 1, 2 Similarly, η il is gene effect in each group, a ij is the probe effect in each gene, and d ihl is the error term.
The marginal model for gene expression (G ijkl) is given by: (1) G i j k l = μ i l + b i j + ϵ i j k l, i = 1, ⋯, I, j = 1, ⋯, J i, k = 1, ⋯, K , l = 1, 2 where i indexes gene, j indexes probe in a gene, k indexes replicates, and l denotes WT (l = 1) and resistant group (l = 2).
Results from marginal models controlling for blood pressure group assignment and waist-to-height quartile fit a single linear covariate (Table 3).
