Prediction of Pregnancy: A Joint Model for Longitudinal and Binary Data
Prediction of Pregnancy: A Joint Model for Longitudinal and Binary Data
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DOI：
10.1214/09-ba419
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发表时间：
2009-01-01
影响因子：
4.4
通讯作者：
van Den Heuvel, Marianne J.
中科院分区：
文献类型：
--
作者：
Horrocks, Julie;van Den Heuvel, Marianne J.
我们考虑了在接受不孕症治疗的女性人群中，基于对某些血淋巴细胞粘附性的纵向测量，预测成功怀孕的问题。这项分析的目标是为每名女性提供估计的怀孕概率。我们讨论了现有的各种方法，包括多重t检验、混合模型、判别分析和两阶段模型。我们使用了Wang等人开发的联合模型。(2000)，由纵向数据的线性混合效应模型和主要终点的广义线性模型(GLM)组成(这里是成功怀孕的二元指标)。关节纵向/GLM模型类似于流行的纵向和生存数据的关节模型。我们使用贝叶斯方法估计参数。
We consider the problem of predicting the achievement of successful pregnancy, in a population of women undergoing treatment for infertility, based on longitudinal measurements of adhesiveness of certain blood lymphocytes. A goal of the analysis is to provide, for each woman, an estimated probability of becoming pregnant. We discuss various existing approaches, including multiple t-tests, mixed models, discriminant analysis and two-stage models. We use a joint model developed by Wang et al. (2000), consisting of a linear mixed effects model for the longitudinal data and a generalized linear model (glm) for the primary end point, (here a binary indicator of successful pregnancy). The joint longitudinal/glm model is analogous to the popular joint models for longitudinal and survival data. We estimate the parameters using Bayesian methodology.