Table 1 Specifications of the four models (simple linear regression and RE)
From: Use of Bayesian methods to model the SF-6D health state preference based data
Model | Conditional Distribution | Specification of the mean |
|---|---|---|
M1- Linear regression | Yi~N(μi, σ2) εi~N(0, σ2) | μi = β0 + Parai |
M2- Random effect | Yij~N(μij, σ2) \( {u}_i\sim N\left(0,{\upsigma}_u^2\right) \) \( {e}_{ij}\sim N\left(0,{\upsigma}_e^2\right) \) | μij = β0 + Paraij + ui |
M3- Random effect: intercept forced to unity | Yij~N(μij, σ2) \( {u}_i\sim N\left(0,{\upsigma}_u^2\right) \) \( {e}_{ij}\sim N\left(0,{\upsigma}_e^2\right) \) | μij = Paraij + ui |
M4- Random effect: intercept forced to unity and inclusion of most | Yij~N(μij, σ2) \( {u}_i\sim N\left(0,{\upsigma}_u^2\right) \) \( {e}_{ij}\sim N\left(0,{\upsigma}_e^2\right) \) | μij = Paraij + βmostmostij + ui |