Bokor Abigail J, Holford Nick, Hannam Jacqueline A
Department of Pharmacology and Clinical Pharmacology, The University of Auckland, 85 Park Road, Grafton, Auckland, 1023, New Zealand.
J Pharmacokinet Pharmacodyn. 2025 Mar 18;52(2):20. doi: 10.1007/s10928-025-09965-8.
The time course of biomarkers (e.g., acute phase proteins) are typically described using days relative to events of interest, such as surgery or birth, without specifying the sample time. This limits their use as they may change rapidly during a single day. We investigated strategies to impute missing clock times, using procalcitonin for population modelling as the motivating example. 1275 procalcitonin concentrations from 282 neonates were available with dates but not sample times (Scenario 0). Missing clock times were imputed using a random uniform distribution under three scenarios: (1) minimum sampling intervals (8-12 h); (2) procalcitonin concentrations increase for postnatal days 0-1 then decrease; (3) standard sampling practice at the study hospital. Unique datasets (n = 100) were created with scenario-specific imputed clock times. Procalcitonin was modelled for each scenario using the same non-linear mixed effects model using NONMEM. Scenarios were evaluated by the NONMEM objective function value compared to Scenario 0 (∆OFV) and with visual predictive checks. Scenario 3, based on standard sampling practice at the study hospital, was the best imputation procedure with an improved objective function value compared to Scenario 0 (∆OFV: -62.6). Scenario 3 showed a shorter lag time between the birth event and the procalcitonin concentration increase (average: 12.0 h, 95% interval: 9.7 to 14.3 h) compared to other scenarios (averages: 15.3 to 18.7 h). A methodology for selecting imputation strategies for clock times was developed. This may be applied to other problems where clock times are missing.
生物标志物（如急性期蛋白）的时间进程通常是相对于感兴趣的事件（如手术或出生）以天数来描述的，而未指定采样时间。这限制了它们的用途，因为它们可能在一天内迅速变化。我们以降钙素原用于人群建模为例，研究了估算缺失时钟时间的策略。有来自282名新生儿的1275个降钙素原浓度数据，有日期但没有采样时间（情景0）。在三种情景下使用随机均匀分布估算缺失的时钟时间：（1）最小采样间隔（8 - 12小时）；（2）降钙素原浓度在出生后第0 - 1天升高然后降低；（3）研究医院的标准采样做法。使用特定情景的估算时钟时间创建了独特的数据集（n = 100）。使用NONMEM对每种情景下的降钙素原进行建模，采用相同的非线性混合效应模型。通过与情景0（∆OFV）相比的NONMEM目标函数值以及视觉预测检查来评估情景。基于研究医院标准采样做法的情景3是最佳的估算程序，与情景0相比目标函数值有所改善（∆OFV：-62.6）。与其他情景（平均值：15.3至18.7小时）相比，情景3显示出生事件与降钙素原浓度升高之间的滞后时间更短（平均值：12.0小时，95%区间：9.7至14.3小时）。开发了一种选择时钟时间估算策略的方法。这可能适用于其他时钟时间缺失的问题。
