Forward stepwise multiple linear regression

Stepwise multiple linear regression. This is a modified form of forward selection. The model starts out including only one variable, and more variables are subsequently added. But at each stage a BE-style test is also applied. If a variable is added, but becomes less important as a result of subsequent additions, SMLR will allow its removal from the model. 

Alternatively, instead of using the EBE of the parameter of interest as the dependent variable, an estimate of the random effect (t ) can be used as the dependent variable, similar to how partial residuals are used in stepwise linear regression. Early population pharmacokinetic methodology advocated multiple linear regression using either forward, backwards, or stepwise models. A modification of this is to use multiple simple linear models, one for each covariate. For categorical covariates, analysis of variance is used instead. If the p-value for the omnibus F-test or p-value for the T-test is less than some cut-off value, usually 0.05, the covariate is moved forward for further examination. Many reports in the literature use this approach. 

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