Longitudinal Data Analysis Using Bayesian-frequentist Hybrid Random Effects Model

Le Chen, Ao Yuan, Aiyi Liu, Guanjie Chen
Journal of Applied Statistics 2014, 41 (9): 2001-2010
The mixed random effect model is commonly used in longitudinal data analysis within either frequentist or Bayesian framework. Here we consider a case, we have prior knowledge on partial-parameters, while no such information on rest. Thus, we use the hybrid approach on the random-effects model with partial-parameters. The parameters are estimated via Bayesian procedure, and the rest of parameters by the frequentist maximum likelihood estimation (MLE), simultaneously on the same model. In practices, we often know partial prior information such as, covariates of age, gender, and etc. These information can be used, and get accurate estimations in mixed random-effects model. A series of simulation studies were performed to compare the results with the commonly used random-effects model with and without partial prior information. The results in hybrid estimation (HYB) and Maximum likelihood estimation (MLE) were very close each other. The estimated θ values in with partial prior information model (HYB) were more closer to true θ values, and shown less variances than without partial prior information in MLE. To compare with true θ values, the mean square of errors (MSE) are much less in HYB than in MLE. This advantage of HYB is very obvious in longitudinal data with small sample size. The methods of HYB and MLE are applied to a real longitudinal data for illustration.


You are not logged in. Sign Up or Log In to join the discussion.

Related Papers

Available on the App Store

Available on the Play Store
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"