Jae Eun Ahn

Pfizer Inc., New York City, New York, United States

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Publications (6)11.35 Total impact

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    ABSTRACT: The purpose of this study was to describe longitudinal daily seizure count data with respect to the effects of time and pregabalin add-on therapy. Models were developed in a stepwise manner: base model, time effect model, and time and drug effect (final) model, using a negative binomial distribution with Markovian features. Mean daily seizure count (λ) was estimated to be 0.385 (relative standard error [RSE] 3.09%) and was further increased depending on the seizure count on the previous day. An overdispersion parameter (OVDP), representing extra-Poisson variation, was estimated to be 0.330 (RSE 11.7%). Interindividual variances on λ and OVDP were 84.7% and 210%, respectively. Over time, λ tended to increase exponentially with a rate constant of 0.272 year⁻¹ (RSE 26.8%). A mixture model was applied to classify responders/nonresponders to pregabalin treatment. Within the responders, λ decreased exponentially with respect to dose with a constant of 0.00108 mg⁻¹ (RSE 11.9%). The estimated responder rate was 66% (RSE 27.6%). Simulation-based diagnostics showed the model reasonably reproduced the characteristics of observed data. Highly variable daily seizure frequency was successfully characterized incorporating baseline characteristics, time effect, and the effect of pregabalin with classification of responders/nonresponders, all of which are necessary to adequately assess the efficacy of antiepileptic drugs.
    The Journal of Clinical Pharmacology 06/2011; 52(6):880-92. · 2.84 Impact Factor
  • The Journal of Clinical Pharmacology 09/2010; 50(9 Suppl):63S-74S. · 2.84 Impact Factor
  • Jae Eun Ahn, Jonathan L French
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    ABSTRACT: Literature data are often reported as multiple (longitudinal) mean outcomes observed in several groups of patients within a study. Observations within a study are correlated because the patients come from a common population, and the mean observations over time within a treatment arm are correlated because they are based on the same set of patients. As a result, model-based meta-analysis may require more than two levels of random effects to correctly characterize this correlation structure. Using simulation, we explored and evaluated ways to implement multi-level random effects in NONMEM. Simulation models that were linear and non-linear in the random effects were investigated. We compared estimation models that included study and/or treatment arm-level random effects, with and without residual correlation. With all estimation strategies, the fixed random effects parameters were accurately estimated. With regard to correctly characterizing the variability, models that accounted for correlation within a study and treatment arm over time were the best in some situations, while models that accounted for study-level correlation only were better in others. Models that included only treatment arm-level random effects were not superior in any scenario.
    Journal of Pharmacokinetics and Biopharmaceutics 04/2010; 37(2):179-201. · 2.06 Impact Factor
  • Alzheimers & Dementia - ALZHEIMERS DEMENT. 01/2009; 5(4).
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    ABSTRACT: To evaluate the likelihood-based methods for handling data below the quantification limit (BQL) using new features in NONMEM VI. A two-compartment pharmacokinetic model with first-order absorption was chosen for investigation. Methods evaluated were: discarding BQL observations (M1), discarding BQL observations but adjusting the likelihood for the remaining data (M2), maximizing the likelihood for the data above the limit of quantification (LOQ) and treating BQL data as censored (M3), and like M3 but conditioning on the observation being greater than zero (M4). These four methods were compared using data simulated with a proportional error model. M2, M3, and M4 were also compared using data simulated from a positively truncated normal distribution. Successful terminations and bias and precision of parameter estimates were assessed. For the data simulated with a proportional error model, the overall performance was best for M3 followed by M2 and M1. M3 and M4 resulted in similar estimates in analyses without log transformation. For data simulated with the truncated normal distribution, M4 performed better than M3. Analyses that maximized the likelihood of the data above the LOQ and treated BQL data as censored provided the most accurate and precise parameter estimates.
    Journal of Pharmacokinetics and Pharmacodynamics 09/2008; 35(4):401-21. · 1.81 Impact Factor
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    ABSTRACT: Purpose To evaluate the likelihood-based methods for handling data below the quantification limit (BQL) using new features in NONMEM VI. Methods A two-compartment pharmacokinetic model with first-order absorption was chosen for investigation. Methods evaluated were: discarding BQL observations (M1), discarding BQL observations but adjusting the likelihood for the remaining data (M2), maximizing the likelihood for the data above the limit of quantification (LOQ) and treating BQL data as censored (M3), and like M3 but conditioning on the observation being greater than zero (M4). These four methods were compared using data simulated with a proportional error model. M2, M3, and M4 were also compared using data simulated from a positively truncated normal distribution. Successful terminations and bias and precision of parameter estimates were assessed. Results For the data simulated with a proportional error model, the overall performance was best for M3 followed by M2 and M1. M3 and M4 resulted in similar estimates in analyses without log transformation. For data simulated with the truncated normal distribution, M4 performed better than M3. Conclusions Analyses that maximized the likelihood of the data above the LOQ and treated BQL data as censored provided the most accurate and precise parameter estimates.
    Journal of Pharmacokinetics and Pharmacodynamics 01/2008; 37(3):305-308. · 1.81 Impact Factor