Quantile regression methods for longitudinal data with drop-outs: Application to CD4 cell counts of patients infected with the human immunodeficiency virus
Quantile regression methods for longitudinal data with drop-outs: Application to CD4 cell counts of patients infected with the human immunodeficiency virus
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DOI：
10.1111/1467-9876.00084
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发表时间：
1997-01-01
影响因子：
1.6
通讯作者：
Zhao, LP
中科院分区：
文献类型：
--
作者：
Lipsitz, SR;Fitzmaurice, GM;Zhao, LP
感染人类免疫缺陷病毒（HIV）的患者通常会出现CD4细胞计数（某些白色血细胞的计数）下降。我们描述了使用分位数回归方法来分析纵向数据的CD4细胞计数从1300例患者参加了临床试验，比较两种治疗方法：齐多夫定和去羟肌苷。确定短期治疗期间CD4细胞计数的任何治疗差异具有科学意义。然而，CD4数据的分析因脱落而变得复杂：基线时CD4细胞计数较低的患者似乎更有可能在以后的测量中脱落。出于这个例子，我们描述了使用“加权”估计方程的分位数回归模型的纵向数据与辍学。特别是，传统的估计方程的分位数回归参数的权重与辍学的概率成反比。这种方法要求生成缺失数据的过程是可估计的，但除了分位数回归模型所施加的假设之外，不对响应的分布做出任何假设。这种方法产生一致的估计的分位数回归参数的辍学模型已被正确指定。所提出的方法适用于CD4细胞计数数据和结果进行了比较，从“未加权”分析。这些结果表明，不考虑辍学情况的分析可能会产生误导。
Patients infected with the human immunodeficiency virus (HIV) generally experience a decline in their CD4 cell count (a count of certain white blood cells). We describe the use of quantile regression methods to analyse longitudinal data on CD4 cell counts from 1300 patients who participated in clinical trials that compared two therapeutic treatments: zidovudine and didanosine. It is of scientific interest to determine any treatment differences in the CD4 cell counts over a short treatment period. However, the analysis of the CD4 data is complicated by drop-outs: patients with lower CD4 cell counts at the base-line appear more likely to drop out at later measurement occasions. Motivated by this example, we describe the use of 'weighted' estimating equations in quantile regression models for longitudinal data with drop-outs. In particular, the conventional estimating equations for the quantile regression parameters are weighted inversely proportionally to the probability of drop-out. This approach requires the process generating the missing data to be estimable but makes no assumptions about the distribution of the responses other than those imposed by the quantile regression model. This method yields consistent estimates of the quantile regression parameters provided that the model for drop-out has been correctly specified. The methodology proposed is applied to the CD4 cell count data and the results are compared with those obtained from an 'unweighted' analysis. These results demonstrate how an analysis that fails to account for drop-outs can mislead.