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A latent class based imputation method under Bayesian quantile regression framework using asymmetric Laplace distribution for longitudinal medication usage data with intermittent missing values.
Journal of Biopharmaceutical Statistics ( IF 1.2 ) Pub Date : 2019-11-15 , DOI: 10.1080/10543406.2019.1684306
Minjae Lee 1 , Mohammad H Rahbar 1, 2 , Lianne S Gensler 3 , Matthew Brown 4 , Michael Weisman 5 , John D Reveille 6
Affiliation  

Evaluating the association between diseases and the longitudinal pattern of pharmacological therapy has become increasingly important. However, in many longitudinal studies, self-reported medication usage data collected at patients' follow-up visits could be missing for various reasons. These pieces of missing or inaccurate/untenable information complicate determining the trajectory of medication use and its complete effects for patients. Although longitudinal models can deal with specific types of missing data, inappropriate handling of this issue can lead to a biased estimation of regression parameters especially when missing data mechanisms are complex and depend upon multiple sources of variation. We propose a latent class-based multiple imputation (MI) approach using a Bayesian quantile regression (BQR) that incorporates cluster of unobserved heterogeneity for medication usage data with intermittent missing values. Findings from our simulation study indicate that the proposed method performs better than traditional MI methods under certain scenarios of data distribution. We also demonstrate applications of the proposed method to data from the Prospective Study of Outcomes in Ankylosing Spondylitis (AS) cohort when assessing an association between longitudinal nonsteroidal anti-inflammatory drugs (NSAIDs) usage and radiographic damage in AS, while the longitudinal NSAID index data are intermittently missing.

中文翻译:

贝叶斯分位数回归框架下的基于潜在类的插补方法,使用具有不连续缺失值的纵向药物使用数据的非对称拉普拉斯分布。

评估疾病与药物治疗纵向模式之间的关联变得越来越重要。但是,在许多纵向研究中,由于各种原因,可能会丢失患者随访中收集到的自我报告的药物使用数据。这些缺失或不正确/无法理解的信息使确定药物使用的轨迹及其对患者的完全效果变得复杂。尽管纵向模型可以处理丢失数据的特定类型,但是对此问题的不适当处理可能导致回归参数的估计有偏差,尤其是在丢失数据机制复杂且依赖于多种变化源的情况下。我们提出了一种使用贝叶斯分位数回归(BQR)的基于潜在类的多重插补(MI)方法,该方法结合了具有间歇性缺失值的药物使用数据的未观察到的异质性群集。我们的仿真研究结果表明,在某些数据分布情况下,该方法的性能优于传统的MI方法。当评估纵向非甾体抗炎药(NSAID)的使用与AS的放射学损害之间的关联时,我们还证明了该方法在前瞻性脊柱炎(AS)队列结果研究中的数据中的应用,而纵向NSAID指数数据间歇地丢失。我们的仿真研究结果表明,在某些数据分布情况下,该方法的性能优于传统的MI方法。当评估纵向非甾体抗炎药(NSAID)的使用与AS的放射学损害之间的关联时,我们还证明了该方法在前瞻性脊柱炎(AS)队列结果研究中的数据中的应用,而纵向NSAID指数数据间歇地丢失。我们的仿真研究结果表明,在某些数据分布情况下,该方法的性能优于传统的MI方法。当评估纵向非甾体抗炎药(NSAID)的使用与AS的放射学损害之间的关联时,我们还证明了该方法在前瞻性脊柱炎(AS)队列结果研究中的数据中的应用,而纵向NSAID指数数据间歇地丢失。
更新日期:2019-11-01
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