Use of empirical likelihood to calibrate auxiliary information in isotonic

 
   Lead Investigator:    Baojiang Chen
   Institution      :    UW
   E-Mail           :    baojiang.chen@unmc.edu
   Proposal ID      :    246


   Publications:


1  Chen B, Zhou XH , Chan KC
   Pseudo-empirical Likelihood-Based Method Using Calibration for Longitudinal Data with Drop-Out
   Journal Article  J Royal Stat Soceity Series C,64:157-174,2015,PMC4288856

   PUBLISHED



2  Chen B , Qin J
   Use of empirical likelihood to calibrate auxiliary information in partly linear monotone regression models
   Journal Article  Stat Med,33:1713-1722,2014,PMC3981901

   PUBLISHED





   Proposal Description:
   In statistical analysis a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depends on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood based method for isotonic regression model to incorporate the auxiliary information. Since the monotonicity constraints are still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates. We apply this method to a dementia study where the response of interest is the MMSE score, and the covariate of interest is the depressions status (yes/no). As is widely known, dementia is a progressive disease, and it gets worse over age. To eliminate the age effect, we must accommodate age into the model.