Nasserinejad Kazem, van Rosmalen Joost, de Kort Wim, Rizopoulos Dimitris, Lesaffre Emmanuel
Department of Biostatistics, Erasmus MC, Rotterdam, the Netherlands.
Department of Donor Studies, Sanquin Research, Amsterdam, the Netherlands.
Stat Med. 2016 Feb 20;35(4):581-94. doi: 10.1002/sim.6759. Epub 2015 Oct 14.
Blood donors experience a temporary reduction in their hemoglobin (Hb) value after donation. At each visit, the Hb value is measured, and a too low Hb value leads to a deferral for donation. Because of the recovery process after each donation as well as state dependence and unobserved heterogeneity, longitudinal data of Hb values of blood donors provide unique statistical challenges. To estimate the shape and duration of the recovery process and to predict future Hb values, we employed three models for the Hb value: (i) a mixed-effects models; (ii) a latent-class mixed-effects model; and (iii) a latent-class mixed-effects transition model. In each model, a flexible function was used to model the recovery process after donation. The latent classes identify groups of donors with fast or slow recovery times and donors whose recovery time increases with the number of donations. The transition effect accounts for possible state dependence in the observed data. All models were estimated in a Bayesian way, using data of new entrant donors from the Donor InSight study. Informative priors were used for parameters of the recovery process that were not identified using the observed data, based on results from the clinical literature. The results show that the latent-class mixed-effects transition model fits the data best, which illustrates the importance of modeling state dependence, unobserved heterogeneity, and the recovery process after donation. The estimated recovery time is much longer than the current minimum interval between donations, suggesting that an increase of this interval may be warranted.
献血者在献血后血红蛋白（Hb）值会暂时降低。每次献血时都会测量Hb值，Hb值过低会导致延期献血。由于每次献血后的恢复过程以及状态依赖性和未观察到的异质性，献血者Hb值的纵向数据带来了独特的统计挑战。为了估计恢复过程的形状和持续时间并预测未来的Hb值，我们采用了三种Hb值模型：（i）混合效应模型；（ii）潜在类别混合效应模型；（iii）潜在类别混合效应转换模型。在每个模型中，使用了一个灵活的函数来模拟献血后的恢复过程。潜在类别识别出恢复时间快或慢的献血者群体以及恢复时间随献血次数增加的献血者群体。转换效应考虑了观测数据中可能存在的状态依赖性。所有模型均采用贝叶斯方法进行估计，使用来自Donor InSight研究的新加入献血者的数据。基于临床文献的结果，对未通过观测数据识别的恢复过程参数使用了信息先验。结果表明，潜在类别混合效应转换模型对数据的拟合效果最佳，这说明了对状态依赖性、未观察到的异质性以及献血后恢复过程进行建模的重要性。估计的恢复时间比目前规定的最短献血间隔长得多，这表明可能有必要延长这一间隔。
