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. 2012 Jul 6;39(4):393–414. doi: 10.1007/s10928-012-9258-0

Table 5.

True and spurious covariate relationships identified in the simulated data by the automated stepwise covariate modeling, Lasso, and SOHGA approaches and the models fit characteristics

Method “True” covariates Spurious covariates Objective function value
Clearance Volume of distribution Clearance Volume of distribution
Original model BMI, CRCL BSA, Sex 6101.2
 Stepwise covariate modeling (SCM): p value for inclusion, p value for elimination
  0.05, 0.05 BMI, CRCL Sex WT HT, CV1 6085.9
  0.05, 0.01 BMI, CRCL Sex HT, CV1 6091.1
  0.10, 0.01 BMI, CRCL Sex HT, CV1 6091.1
Lasso model BMI, CRCL 6254.2
 Single-objective, hybrid genetic algorithm
  3.84 point penalty per parameter BMI, CRCL Sex BSA HT, CV1 6086.7
  10 point penalty per parameter BMI, CRCL Sex HT 6097.9

BMI body mass index, BSA body surface area, CRCL creatinine clearance, CV1 unrelated covariate 1, HT height, WT weight