Authors

• T. Baghfalaki
• M. Ganjali
• M. Khounsiavash

Abstract

In this paper, multivariate skew-normal distribution is em- ployed for analyzing an outcome based dropout model for repeated mea- surements with non-random dropout in skew regression data sets. A probit regression is considered as the conditional probability of an ob- servation to be missing given outcomes. A simulation study of using the proposed methodology and comparing it with a semi-parametric method, GEE, is provided. The standardized bias is used for compari- son of different approaches. Furthermore, for investigation of efficiency of the methodology two applications are analyzed where observed infor- mation matrix is used to find the variances of the parameter estimates. In one of the applications a sensitivity analysis is also performed to in- vestigate the change on the response model’s parameter estimates due to perturbation of drop-out model’s parameter of interest.

Keywords

• Dropout
• Generalized estimating equations (GEE)
• longitu- dinal data
• observed information matrix
• selection model
• Skew-Normal distribution