Opportunistic pharmacokinetic (PK) studies have sparse and imbalanced clinical measurement data, and the impact of sample time errors is an important concern when seeking accurate estimates of treatment response. We evaluated an approximate Bayesian model for individualized pharmacokinetics in the presence of time recording errors (TREs), considering both a short and long infusion dosing pattern. We found that the long infusion schedule generally had lower bias in estimates of the pharmacodynamic (PD) endpoint relative to the short infusion schedule. We investigated three different design strategies for their ability to mitigate the impact of TREs: (i) shifting blood draws taken during an active infusion to the post-infusion period, (ii) identifying the best next sample time by minimizing bias in the presence of TREs, and (iii) collecting additional information on a subset of patients based on estimate uncertainty or quadrature-estimated variance in the presence of TREs. Generally, the proposed strategies led to a decrease in bias of the PD estimate for the short infusion schedule, but had a negligible impact for the long infusion schedule. Dosing regimens with periods of high non-linearity may benefit from design modifications, while more stable concentration-time profiles are generally more robust to TREs with no design modifications.

翻译：机会性药代动力学研究存在稀疏且不均衡的临床测量数据，在寻求准确的治疗反应估计时，采样时间误差的影响至关重要。我们评估了一种在存在记录时间误差的情况下进行个体化药代动力学分析的近似贝叶斯模型，同时考虑了短时和长时输注给药模式。研究发现，与短时输注方案相比，长时输注方案在药效学终点估计中通常具有更低的偏倚。我们研究了三种不同的设计策略以缓解记录时间误差的影响：(i) 将输注期间采集的血样转移至输注后时段，(ii) 通过在存在记录时间误差的情况下最小化偏倚来确定最佳的下一次采样时间，以及(iii) 基于估计不确定性或存在记录时间误差时通过求积法估计的方差，收集部分患者的额外信息。总体而言，所提出的策略在短时输注方案中降低了药效学估计的偏倚，但对长时输注方案影响甚微。具有高非线性时段的给药方案可能受益于设计修改，而浓度-时间曲线相对稳定的方案通常对记录时间误差具有更强的鲁棒性，无需进行设计修改。