Bycott P, Taylor J
Department of Biometrics, Parke-Davis Pharmaceutical Research, Ann Arbor, MI 48105, USA.
Stat Med. 1998 Sep 30;17(18):2061-77. doi: 10.1002/(sici)1097-0258(19980930)17:18<2061::aid-sim896>3.0.co;2-o.
The use of CD4+ T-lymphocyte counts as a covariate presents some unique challenges in survivorship analyses due to the variability of this marker. If one does not account for the measurement error component of this variability in some manner, the estimate of the relative risk parameter in a time-dependent Cox model is biased towards zero, and coverage levels of confidence intervals may be seriously incorrect. We use a two-stage approach to reduce the variability in the observed CD4 counts in order to obtain a more accurate estimate of the relative risk parameter and more valid summary statistics. In the first stage, population based smoothing methods derived from a random-effects model plus a stochastic process or individual based smoothing methods are used to replace the observed longitudinal CD4 counts with less variable imputes at each failure time. In the second stage, we use the imputes in a time-dependent Cox model to estimate the risk parameter and its associated summary statistics. We compare the smoothing methods in simulation studies and find that the use of these smoothing methods results in a substantial reduction in bias for the true risk parameter estimate, better efficiency, and more accurate coverage rates in confidence intervals. We apply our two-stage smoothing methods to the marker CD4 in the ACTG-019 clinical trial part B.
在生存分析中，将CD4 + T淋巴细胞计数用作协变量会带来一些独特的挑战，因为该标志物存在变异性。如果不以某种方式考虑这种变异性的测量误差成分，那么在时间相依Cox模型中相对风险参数的估计会偏向于零，并且置信区间的覆盖水平可能会严重不准确。我们采用两阶段方法来减少观察到的CD4计数中的变异性，以便更准确地估计相对风险参数并获得更有效的汇总统计量。在第一阶段，基于群体的平滑方法（源自随机效应模型加随机过程）或基于个体的平滑方法用于在每个失败时间用变异性较小的插补值替换观察到的纵向CD4计数。在第二阶段，我们在时间相依Cox模型中使用插补值来估计风险参数及其相关的汇总统计量。我们在模拟研究中比较了平滑方法，发现使用这些平滑方法可大幅减少真实风险参数估计的偏差，提高效率，并使置信区间的覆盖率更准确。我们将两阶段平滑方法应用于ACTG - 019临床试验B部分中的标志物CD4。
