A Stochastic Regression Model for General Trend Analysis of Longitudinal Continuous Data

作者：Chao Wei Hsiung^*; Chen Su Hua

来源：Biometrical Journal, 2009, 51(4): 571-587.

DOI：10.1002/bimj.200800254

• Local equilibrium distribution model
• Longitudinal data
• Markov process
• Ornstein-Uhlenbeck process
• Quasi-score functions

A predictive continuous time model is developed for continuous panel data to assess the effect of time-varying covariates on the general direction of the movement of a continuous response that fluctuates over time. This is accomplished by reparameterizing the infinitesimal mean of an Ornstein-Uhlenbeck processes in terms of its equilibrium mean and a drift parameter, which assesses the rate that the process reverts to its equilibrium mean. The equilibrium mean is modeled as a linear predictor of covariates. This model can be viewed as a continuous time first-order autoregressive regression model with time-varying lag effects of covariates and the response, which is more appropriate for unequally spaced panel data than its discrete time analog. Both maximum likelihood and quasi-likelihood approaches are considered for estimating the model parameters and their performances are compared through simulation studies. The simpler quasi-likelihood approach is suggested because it yields an estimator that is of high efficiency relative to the maximum likelihood estimator and it yields a variance estimator that is robust to the diffusion assumption of the model. To illustrate the proposed model, an application to diastolic blood pressure data from a follow-up study on cardiovascular diseases is presented. Missing observations are handled naturally with this model.

• 出版日期2009-8
• 单位东华大学