Division of Clinical and Translational Sciences, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
BMC Med Res Methodol. 2018 Jan 11;18(1):8. doi: 10.1186/s12874-017-0463-9.
In patient-based studies, biomarker data are often subject to left censoring due to the detection limits, or to incomplete sample or data collection. In the context of longitudinal regression analysis, inappropriate handling of these issues could lead to biased parameter estimates. We developed a specific multiple imputation (MI) strategy based on weighted censored quantile regression (CQR) that not only accounts for censoring, but also missing data at early visits when longitudinal biomarker data are modeled as a covariate.
We assessed through simulation studies the performances of developed imputation approach by considering various scenarios of covariance structures of longitudinal data and levels of censoring. We also illustrated the application of the proposed method to the Prospective Study of Outcomes in Ankylosing spondylitis (AS) (PSOAS) data to address the issues of censored or missing C-reactive protein (CRP) level at early visits for a group of patients.
Our findings from simulation studies indicated that the proposed method performs better than other MI methods by having a higher relative efficiency. We also found that our approach is not sensitive to the choice of covariance structure as compared to other methods that assume normality of biomarker data. The analysis results of PSOAS data from the imputed CRP levels based on our method suggested that higher CRP is significantly associated with radiographic damage, while those from other methods did not result in a significant association.
The MI based on weighted CQR offers a more valid statistical approach to evaluate a biomarker of disease in the presence of both issues with censoring and missing data in early visits.
在基于患者的研究中，由于检测限或不完全的样本或数据收集，生物标志物数据经常受到左截断。在纵向回归分析的背景下，不恰当地处理这些问题可能会导致参数估计有偏差。我们开发了一种基于加权截断分位数回归（CQR）的特定多重插补（MI）策略，该策略不仅考虑了截断，还考虑了当纵向生物标志物数据被建模为协变量时早期访问中的缺失数据。
我们通过考虑各种协方差结构和截断水平的纵向数据场景，通过模拟研究评估了所开发的插补方法的性能。我们还说明了拟议方法在强直性脊柱炎（AS）前瞻性结局研究（PSOAS）数据中的应用，以解决一组患者在早期访问中 CRP 水平截断或缺失的问题。
我们的模拟研究结果表明，与其他 MI 方法相比，该方法具有更高的相对效率，表现更好。与其他假设生物标志物数据正态性的方法相比，我们还发现我们的方法对协方差结构的选择不敏感。基于我们的方法从插补 CRP 水平得出的 PSOAS 数据的分析结果表明，较高的 CRP 与放射学损伤显著相关，而其他方法的结果则没有显著相关性。
基于加权 CQR 的 MI 为评估存在截断和早期缺失数据的疾病生物标志物提供了一种更有效的统计方法。
