Current status data occur in many fields including demographical, epidemiological, financial, medical, and sociological studies. We consider the regression analysis of current status data with latent variables. The proposed model consists of a factor analytic model for characterizing latent variables through their multiple surrogates and an additive hazard model for examining potential covariate effects on the hazards of interest in the presence of current status data. We develop a borrow-strength estimation procedure that incorporates the expectation–maximization algorithm and correlated estimating equations. The consistency and asymptotic normality of the proposed estimators are established. A simulation study is conducted to evaluate the finite sample performance of the proposed method. A real-life study on the chronic kidney disease of type 2 diabetic patients is presented.

当前状态数据出现在许多领域，包括人口统计学、流行病学、金融、医学和社会学研究。我们考虑具有潜在变量的当前状态数据的回归分析。所提出的模型包括一个因子分析模型，用于通过多个代理来表征潜在变量，以及一个加性风险模型，用于在存在当前状态数据的情况下检查对感兴趣的危害的潜在协变量影响。我们开发了一个借用强度估计程序，它结合了期望最大化算法和相关估计方程。建立了建议估计量的一致性和渐近正态性。进行模拟研究以评估所提出方法的有限样本性能。