A Multiple Imputation Workflow for Handling Missing Covariate Data in Pharmacometrics Modeling.

IF 3.1 3区 医学 Q2 PHARMACOLOGY & PHARMACY
My-Luong Vuong, Geert Verbeke, Erwin Dreesen
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Abstract

Covariate missingness is a prevalent issue in pharmacometrics modeling. Incorrect handling of missing covariates can lead to biased parameter estimates, adversely affecting clinical practice and drug development dosing decisions. Single imputation is usually favored by pharmacometricians for its simplicity, but it ignores the uncertainty about imputed values, potentially leading to biased estimates and standard errors. Multiple imputation, in contrast, generates multiple plausible values from a predictive distribution, addressing this uncertainty and thus is a preferable approach over single imputation to handle covariate missingness. Yet, its application in pharmacometrics remains limited due to perceived complexity. To address this, we developed a multiple imputation workflow specifically tailored for pharmacometricians, encouraging wider adoption of this more reliable method in pharmacometrics modeling. We compared single imputation and multiple imputation in estimating covariate effects using a publicly available dataset on warfarin pharmacokinetics in healthy volunteers. A one-compartment population pharmacokinetic model with baseline body weight as the only covariate was used to describe the warfarin pharmacokinetics. We simulated five scenarios in which 6.25%, 12.5%, 25%, 50%, and 75% of the subjects had their body weight missing under a missing at random mechanism conditioned on age and sex. We confirm that multiple imputation better reflects uncertainty estimates than single imputation, regardless of the degree of missingness. This confirms multiple imputation as a superior alternative to single imputation for handling missing covariate data in pharmacometrics.

在药物计量学建模中处理缺失协变量数据的多重输入工作流。
协变量缺失是药物计量学建模中一个普遍存在的问题。对缺失协变量的错误处理可能导致参数估计有偏差,对临床实践和药物开发剂量决策产生不利影响。由于其简单性,单一估算法通常受到药物计量学家的青睐,但它忽略了估算值的不确定性,可能导致有偏差的估计和标准误差。相反,多重插值从预测分布中产生多个可信值,解决了这种不确定性,因此是处理协变量缺失的最佳方法。然而,由于其复杂性,其在药物计量学中的应用仍然有限。为了解决这个问题,我们开发了一个专门为药物计量学家量身定制的多重输入工作流程,鼓励在药物计量建模中更广泛地采用这种更可靠的方法。我们使用公开的华法林在健康志愿者体内的药代动力学数据集,比较了单次代入和多次代入对协变量效应的估计。以基线体重为唯一协变量的单室人群药代动力学模型被用来描述华法林的药代动力学。我们模拟了五种情况,分别是6.25%、12.5%、25%、50%和75%的受试者在年龄和性别随机缺失机制下体重缺失。我们证实,无论缺失程度如何,多重代入比单一代入更能反映不确定性估计。这证实了在处理药物计量学中缺失的协变量数据时,多重输入是比单一输入更好的选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.00
自引率
11.40%
发文量
146
审稿时长
8 weeks
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