Binary Time Series Modeling With Application to Adhesion Frequency Experiments

Abstract

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 can often be violated in practice. Motivated by the analysis of repeated adhesion tests, a binary time series model incorporating random effects is developed in this paper. 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 via 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.