title: Effect of assay measurement error on parameter estimation in concentration-QTc interval modeling.
abstract: Linear mixed-effects models (LMEMs) of concentration-double-delta QTc intervals (QTc intervals corrected for placebo and baseline effects) assume that the concentration measurement error is negligible, which is an incorrect assumption. Previous studies have shown in linear models that independent variable error can attenuate the slope estimate with a corresponding increase in the intercept. Monte Carlo simulation was used to examine the impact of assay measurement error (AME) on the parameter estimates of an LMEM and nonlinear MEM (NMEM) concentration-ddQTc interval model from a 'typical' thorough QT study. For the LMEM, the type I error rate was unaffected by assay measurement error. Significant slope attenuation ( > 10%) occurred when the AME exceeded > 40% independent of the sample size. Increasing AME also decreased the between-subject variance of the slope, increased the residual variance, and had no effect on the between-subject variance of the intercept. For a typical analytical assay having an assay measurement error of less than 15%, the relative bias in the estimates of the model parameters and variance components was less than 15% in all cases. The NMEM appeared to be more robust to AME error as most parameters were unaffected by measurement error. Monte Carlo simulation was then used to determine whether the simulation-extrapolation method of parameter bias correction could be applied to cases of large AME in LMEMs. For analytical assays with large AME ( > 30%), the simulation-extrapolation method could correct biased model parameter estimates to near-unbiased levels.
authors.affiliation: Astellas Pharma Global Development, Inc., Northbrook, IL, USA.
authors.forename: Peter L
authors.initials: PL
authors.lastname: Bonate
mesh_terms: D015982:Bias; D015195:Drug Design; D004562:Electrocardiography; D006801:Humans; D016014:Linear Models; D008133:Long QT Syndrome / Q000139:chemically induced*; D008954:Models, Biological; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D018401:Sample Size