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A Modified Hybrid Wald’s Approximation Method for Efficient Covariate Selection in Population Pharmacokinetic Analysis

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Abstract

One important objective of population pharmacokinetic (PPK) analyses is to identify and quantify relationships between covariates and model parameters such as clearance and volume. To improve upon existing covariate model development methods including stepwise procedures and Wald’s approximation method (WAM), this paper introduces an innovative method named the hybrid first-order conditional estimation (FOCE)/Monte-Carlo parametric expectation maximization (MCPEM)-based Wald’s approximation method with backward elimination (BE), or H-WAM-BE. Compared with WAM, this new method uses MCPEM to obtain full covariance matrix after running FOCE to obtain full model parameter estimates, followed by BE to select the final covariate model. Two groups of datasets (simulation datasets and rituximab datasets) were used to compare the performance of H-WAM-BE with two other methods, likelihood ratio test (LRT)-based stepwise covariate method (SCM) and H-WAM with full subset approach (H-WAM-F) in NONMEM. Different scenarios with different sample sizes and sampling schemes were used for simulating datasets. The nominal model was used as the reference to evaluate the three methods for their ability to accurately identify parameter-covariate relationships. The methods were compared using the number of true and false positive covariates identified, number of times that they identified the reference model, computation times, and predictive performance. Best-performing H-WAM-BE methods (M2 and M4) showed comparable results with LRT-based SCM. H-WAM-BE required shorter or comparable computation times than LRT-based SCM and H-WAM-F regardless of the model structure, sample size, or sampling design used in this study.

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, Lexington, Kentucky, USA

    Yixuan Zou, Fei Tang & Chee M. Ng

  2. Department of Statistics, University of Kentucky, Lexington, Kentucky, USA

    Yixuan Zou

  3. NewGround Pharmaceutical Consulting LLC, Foster City, California, USA

    Chee M. Ng

Authors
  1. Yixuan Zou
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  2. Fei Tang
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  3. Chee M. Ng
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Correspondence to Chee M. Ng.

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Glossary

ALB, Albumin; ALK, Alkaline phosphatase; ALT, Alanine transaminase; AST, Aspartate transaminase; BE, Backward elimination; BSA, Body surface area; CL, Clearance; COV, Variance-covariance matrix; EM, Expectation maximization; FOCE, First-order conditional estimation; FS, Forward selection; GEN, Gender; H-WAM-BE, Hybrid first-order conditional estimation/Monte-Carlo parametric expectation maximization-based Wald’s approximation method with backward elimination; H-WAM-F, H-WAM with full subset approach; HCT, Hematocrit; IV, Intravenous; LDH, Lactate dehydrogenase; LRT, Likelihood ratio test; M0a, H-WAM-BE interim model with pbe1 = 0.05; M0b, H-WAM-BE interim model with pbe1 = 0.01; M1, H-WAM-BE pbe1 = 0.05 & pbe2 = 0.05; M2, H-WAM-BE pbe1 = 0.05 & pbe2 = 0.01; M3, H-WAM-BE pbe1 = 0.01 & pbe2 = 0.05; M4, H-WAM-BE pbe1 = 0.01 & pbe2 = 0.01; MCPEM, Monte-Carlo parametric expectation maximization; NHANES, National Health and Nutrition Examination Survey; OFV, Objective function value; PC, Platelet count; PD, Pharmacodynamic; PK, Pharmacokinetic; PPK, Population pharmacokinetics; Q, Distribution clearance; RMSE, Root mean squared error; SBC, Schwarz’s Bayesian criterion; SCR, Serum creatinine; SCM, Stepwise covariate method; SCM1, SCM with pfs = 0.05 and pbe = 0.01; SCM2, SCM with pfs = 0.01 and pbe = 0.01; TB, Total bilirubin; V, Volume of distribution; Vc, Volume of the central compartment; Vp, Volume of the peripheral compartment; WAM, Wald’s approximation method; WT, Weight

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Zou, Y., Tang, F. & Ng, C.M. A Modified Hybrid Wald’s Approximation Method for Efficient Covariate Selection in Population Pharmacokinetic Analysis. AAPS J 23, 37 (2021). https://doi.org/10.1208/s12248-021-00572-2

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