Sun Zhuoxin, Rosen Ori, Sampson Allan R
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Biometrics. 2007 Sep;63(3):901-9. doi: 10.1111/j.1541-0420.2007.00762.x.
A novel mixture model is presented for repeated measurements in which correlation among repeated observations on the same subject is induced via correlated unobservable component indicators. The mixture components in our model are linear regressions, and the mixing proportions are logits with random effects. Inference is facilitated by sampling from the posterior distribution of the parameters via Markov chain Monte Carlo methods. The model is applied to a neuronal postmortem brain tissue study to examine the differences in neuron volumes between schizophrenic and control subjects.
提出了一种用于重复测量的新型混合模型，其中通过相关的不可观测成分指标诱导同一受试者重复观测之间的相关性。我们模型中的混合成分是线性回归，混合比例是具有随机效应的对数几率。通过马尔可夫链蒙特卡罗方法从参数的后验分布中抽样，便于进行推断。该模型应用于一项神经元死后脑组织研究，以检查精神分裂症患者和对照受试者之间神经元体积的差异。
