MODELING THE RELATIONSHIP OF SURVIVAL TO LONGITUDINAL DATA MEASURED WITH ERROR - APPLICATIONS TO SURVIVAL AND CD4 COUNTS IN PATIENTS WITH AIDS
MODELING THE RELATIONSHIP OF SURVIVAL TO LONGITUDINAL DATA MEASURED WITH ERROR - APPLICATIONS TO SURVIVAL AND CD4 COUNTS IN PATIENTS WITH AIDS
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DOI：
10.2307/2291126
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发表时间：
1995-03-01
影响因子：
3.7
通讯作者：
WULFSOHN, MS
中科院分区：
文献类型：
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作者：
TSIATIS, AA;DEGRUTTOLA, V;WULFSOHN, MS
在获得性免疫缺陷综合征(AIDS)临床试验中，在评估新的治疗方法时，一个受到极大关注的问题是寻找一个良好的临床进展替代标记物。这种标记物的确定可能有助于在较短的时间内评估新疗法的疗效。由于观察到CD4淋巴细胞计数与临床结果的相关性，已建议将其作为人类免疫病毒(HIV)试验的潜在标记物。但是，为了评估作为潜在替代标记物的CD4计数的作用，我们必须更好地了解临床结果与个体随着时间推移的CD4计数历史的关系。采用COX比例风险回归模型，研究作为时间依赖协变量的CD4计数与生存率的关系。由于CD4计数只是周期性地测量，并且具有很大的测量误差和生物变化，因此通过最大化偏似然来估计COX模型中的参数的标准方法不再适用。相反，我们提出了一个分两个阶段的方法。在第一阶段中，使用重复测量随机分量模型对纵向的CD4计数数据进行建模。在第二阶段中，推导了当数据被假定为这种形式时估计COX模型中的参数的方法。我们还考虑了解决丢失数据模式的方法。这些方法被应用于艾滋病患者随机临床试验中的CD4数据，其中一半患者被随机接受齐多夫定(ZDV)治疗，另一半患者被随机接受安慰剂治疗。虽然已经证实了CD_4计数与存活率之间有很强的相关性，但我们也表明CD_4计数可能不能作为评估这类患者治疗的有效替代标记物。
A question that has received a great deal of attention in evaluating new treatments in acquired immune deficiency syndrome (AIDS) clinical trials is that of finding a good surrogate marker for clinical progression. The identification of such a marker may be useful in assessing the efficacy of new therapies in a shorter period. The number of CD4-lymphocyte counts has been proposed as such a potential marker for human immune virus (HIV) trials because of its observed correlation with clinical outcome. But to evaluate the role of CD4 counts as a potential surrogate marker, we must better understand the relationship of clinical outcome to an individual's CD4 count history over time. The Cox proportional hazards regression model is used to study the relationship between CD4 counts as a time-dependent covariate and survival. Because the CD4 counts are measured only periodically and with substantial measurement error and biological variation, standard methods for estimating the parameters in the Cox model by maximizing the partial likelihood are no longer appropriate. Instead, we propose a two-stage approach. In the first stage the longitudinal CD4 count data are modeled using a repeated measures random components model. In the second stage methods for estimating the parameters in a Cox model when the data are assumed to be of this form are derived. We also considered methods to account for missing data patterns. These methods are applied to CD4 data from a randomized clinical trial of AIDS patients where half of the patients were randomized to receive Zidovudine (ZDV) and the other half were randomized to receive a placebo. Although a strong correlation between CD4 counts and survival is demonstrated, we also show that CD4 counts may not serve as a useful surrogate marker for assessing treatments for this population of patients.