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Effect of assay measurement error on parameter estimation in concentration-QTc interval modeling
1Astellas Pharma Global Development, Inc., Northbrook, IL, USA.
Pharmaceutical Statistics
|April 23, 2013
Summary
Assay measurement error (AME) in linear mixed-effects models (LMEMs) for concentration-corrected QTc intervals can bias results, but nonlinear models are more robust. The simulation-extrapolation method can correct bias in LMEMs with significant AME.
Area of Science:
- Pharmacometrics
- Statistical Modeling
- Clinical Trial Analysis
Background:
- Linear mixed-effects models (LMEMs) for concentration-corrected QTc intervals often assume negligible concentration measurement error.
- This assumption is incorrect and can lead to biased parameter estimates, similar to errors in independent variables in linear models.
Purpose of the Study:
- To investigate the impact of assay measurement error (AME) on parameter estimates in LMEMs and nonlinear mixed-effects models (NMEMs) for concentration-double-delta QTc intervals.
- To evaluate the effectiveness of the simulation-extrapolation (SIMEX) method for correcting bias caused by large AME in LMEMs.
Main Methods:
- Monte Carlo simulations were employed to assess the effects of varying AME levels on LMEM and NMEM parameter estimates in a typical thorough QT study.
- The type I error rate, slope attenuation, intercept bias, and variance components were analyzed for LMEMs.
- The robustness of NMEMs to AME was also examined, followed by an evaluation of SIMEX for bias correction in LMEMs with substantial AME.
Main Results:
- LMEMs showed significant slope attenuation (>10%) when AME exceeded 40%, regardless of sample size. Between-subject slope variance decreased, and residual variance increased with higher AME.
- For typical assays with AME <15%, relative bias in LMEM parameters and variance components was <15%.
- NMEMs demonstrated greater robustness to AME, with most parameters remaining unaffected. The SIMEX method effectively corrected parameter bias in LMEMs for assays with AME >30%.
Conclusions:
- Assay measurement error can significantly impact LMEM parameter estimates for concentration-corrected QTc intervals, particularly at higher error levels.
- Nonlinear mixed-effects models offer a more robust alternative when dealing with assay measurement error.
- The simulation-extrapolation method provides a viable approach to correct for parameter bias in LMEMs when assay measurement error is substantial.
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