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Reliability of pharmacodynamic analysis by logistic regression: mixed-effects modeling
Wei Lu1, James G Ramsay, James M Bailey
1Department of Anesthesiology, Emory University School of Medicine, Atlanta, Georgia 30322, USA.
Anesthesiology
|November 26, 2003
Summary
Accurate estimation of drug concentration (C50) is possible with sparse binary data. However, the steepness of the concentration-effect relationship (gamma) is biased and requires at least 10 data points per patient for unbiased estimation.
Area of Science:
- Pharmacometrics
- Pharmacodynamics
- Statistical modeling
Background:
- Binary pharmacologic data are common, often analyzed with logistic regression.
- Previous studies showed unbiased C50 estimates but biased gamma estimates with pooled sparse data.
- Mixed-effects analysis was investigated to improve parameter estimation accuracy.
Purpose of the Study:
- To determine if mixed-effects analysis improves the accuracy of C50 and gamma estimation from sparse binary pharmacodynamic data.
- To evaluate the impact of data sparsity on the bias and variability of parameter estimates.
Main Methods:
- Simulated pharmacodynamic studies with binary responses analyzed using NONMEM.
- Assessed bias and coefficient of variation for C50 and gamma estimates.
- Generated sparse human data from midazolam sedation studies for comparison with full data sets.
Main Results:
- C50 estimates were unbiased even with sparse data (1 data point/patient), with 30-50% coefficient of variation.
- Gamma estimates were highly biased and overestimated with sparse data (gamma > 1).
- Unbiased gamma estimation required a minimum of 10 data points per patient.
Conclusions:
- Accurate C50 estimation is feasible with sparse binary data.
- Gamma estimation is biased with sparse data, necessitating more data points per patient.
- At least 10 observations per patient are required for accurate gamma estimation.