Performance comparison of first-order conditional estimation with interaction and Bayesian estimation methods for estimating the population parameters and its distribution from data sets with a low number of subjects
| Autor: | Kyung Im Kim, Jae-Yeon Lee, Hyun-moon Back, Kwang-il Kwon, Hwi-yeol Yun, Jung-woo Chae, Byungjeong Song, Nayoung Han, Sudeep Pradhan |
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| Rok vydání: | 2017 |
| Předmět: |
Mean squared error
Epidemiology Population Bayesian probability Health Informatics Few subjects 030226 pharmacology & pharmacy 03 medical and health sciences Bayes' theorem symbols.namesake First-order conditional estimation with interaction 0302 clinical medicine Statistics Expectation–maximization algorithm Humans education Markov chain Monte Carlo Bayesian NONMEM Mathematics Demography lcsh:R5-920 education.field_of_study Bayes estimator Stochastic Processes Markov chain Monte Carlo Bayes Theorem Estimation methods Random effects model Markov Chains 030220 oncology & carcinogenesis Data Interpretation Statistical symbols lcsh:Medicine (General) Monte Carlo Method Algorithms Research Article |
| Zdroj: | BMC Medical Research Methodology BMC Medical Research Methodology, Vol 17, Iss 1, Pp 1-9 (2017) |
| ISSN: | 1471-2288 |
| Popis: | Background Exploratory preclinical, as well as clinical trials, may involve a small number of patients, making it difficult to calculate and analyze the pharmacokinetic (PK) parameters, especially if the PK parameters show very high inter-individual variability (IIV). In this study, the performance of a classical first-order conditional estimation with interaction (FOCE-I) and expectation maximization (EM)-based Markov chain Monte Carlo Bayesian (BAYES) estimation methods were compared for estimating the population parameters and its distribution from data sets having a low number of subjects. Methods In this study, 100 data sets were simulated with eight sampling points for each subject and with six different levels of IIV (5%, 10%, 20%, 30%, 50%, and 80%) in their PK parameter distribution. A stochastic simulation and estimation (SSE) study was performed to simultaneously simulate data sets and estimate the parameters using four different methods: FOCE-I only, BAYES(C) (FOCE-I and BAYES composite method), BAYES(F) (BAYES with all true initial parameters and fixed ω 2), and BAYES only. Relative root mean squared error (rRMSE) and relative estimation error (REE) were used to analyze the differences between true and estimated values. A case study was performed with a clinical data of theophylline available in NONMEM distribution media. NONMEM software assisted by Pirana, PsN, and Xpose was used to estimate population PK parameters, and R program was used to analyze and plot the results. Results The rRMSE and REE values of all parameter (fixed effect and random effect) estimates showed that all four methods performed equally at the lower IIV levels, while the FOCE-I method performed better than other EM-based methods at higher IIV levels (greater than 30%). In general, estimates of random-effect parameters showed significant bias and imprecision, irrespective of the estimation method used and the level of IIV. Similar performance of the estimation methods was observed with theophylline dataset. Conclusions The classical FOCE-I method appeared to estimate the PK parameters more reliably than the BAYES method when using a simple model and data containing only a few subjects. EM-based estimation methods can be considered for adapting to the specific needs of a modeling project at later steps of modeling. Electronic supplementary material The online version of this article (10.1186/s12874-017-0427-0) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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