fixed effect包含清除率，病人年紀，病人性別及體重。

80 下列有關 population pharmacokinetics 分析的敘述，何者錯誤？ 
(A)可以 nonlinear mixed effect model（NONMEM）方式分析 
(B)可以 standard two-stage（STS）方式分析 
(C)目前較少使用於歐美各國的藥物臨床試驗中 
(D)可同時分析並預測藥物動力學與相關統計性質

58.統計分析是由眾多不同病人所匯集的大量樣本血漿藥品濃度數據，同時也考量不同病人與同一病 人的變異，來研究藥品在不同特定群體（疾病、性別、年齡…）藥動性質的差異，是屬於下面那一學科的研究範疇？ 
(A)基礎藥物動力學（basic pharmacokinetics） 
(B)臨床藥物動力學（clinical pharmacokinetics） 
(C)藥效學（pharmacodynamics） 
(D)族群藥物動力學（population pharmacokinetics）

Nonlinear Mixed-Effects Modeling of Population Pharmacokinetics Data

By Kristen Zannella, MathWorks

Data sets involving nonlinear, sparse grouped data are common in the health sciences, especially in drug trials_{(D)NONMEM可用於新藥開發及臨床試驗的數據處理}, where they are used to measure drug absorption, distribution, metabolism, and elimination. In this approach, patients are grouped using characteristics such as age, sex, weight, and smoking history. Given the expense of drug trials, however, it is not always possible to obtain sufficient patient data.

Nonlinear mixed-effects (NLME) modeling provides a good solution for modeling sparse datasets. These models account for both fixed effects (population parameters assumed to be constant each time data is collected)_{(B)Fixed effect包括清除率（clearance）以及年紀}and random effects (sample-dependent random variables). In modeling, random effects act like additional error terms, and their distributions and covariances must be specified._{(C)Random effect不包括體重及身高}Mixed-effects models provide a reasonable compromise between ignoring data groupings entirely, thereby losing valuable information, and fitting each group with a separate model, which requires significantly larger sample sizes.

https://www.mathworks.com/company/newsletters/articles/nonlinear-mixed-effects-modeling-of-population-pharmacokinetics-data.html

補充固定效應口訣：連體嬰（年齡 體重 清除率）