Binary Time Series Modeling With Application to Adhesion Frequency Experiments

• 成果类型: Article
• 署名作者: Hung, Ying; Zarnitsyna, Veronika; Zhang, Yan; Zhu, Cheng; Wu, C. F. Jeff
• 署名单位: Rutgers University System; Rutgers University New Brunswick; University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000000508
• 发表日期: 2008
• 页码: 1248-1259
• 关键词: linear mixed models; partial likelihood; regression-models; cell-adhesion; diagnostics; inference

摘要

Repeated adhesion frequency assay is the only published method for measuring the kinetic rates of cell adhesion. Cell adhesion plays an important role in many physiological and pathological processes. Traditional analysis of adhesion frequency experiments assumes that the adhesion test cycles are independent Bernoulli trials. This assumption often can be violated in practice. Motivated by the analysis of repeated adhesion tests, a binary time series model incorporating random effects is developed. A goodness-of-fit statistic is introduced to assess the adequacy of distribution assumptions on the dependent binary data with random effects. The asymptotic distribution of the goodness-of-fit statistic is derived, and its finite-sample performance is examined through a simulation study. Application of the proposed methodology to real data from a T-cell experiment reveals some interesting information, including the dependency between repeated adhesion tests.