title: Quadratic inference functions in marginal models for longitudinal data.
abstract: The quadratic inference function (QIF) is a new statistical methodology developed for the estimation and inference in longitudinal data analysis using marginal models. This method is an alternative to the popular generalized estimating equations approach, and it has several useful properties such as robustness, a goodness-of-fit test and model selection. This paper presents an introductory review of the QIF, with a strong emphasis on its applications. In particular, a recently developed SAS MACRO QIF is illustrated in this paper to obtain numerical results.
authors.affiliation: Department of Biostatistics, UM School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA. pxsong@umich.edu
authors.forename: Peter X-K
authors.initials: PX
authors.lastname: Song
authors.forename: Zhichang
authors.initials: Z
authors.lastname: Jiang
authors.forename: Eunjoo
authors.initials: E
authors.lastname: Park
authors.forename: Annie
authors.initials: A
authors.lastname: Qu
mesh_terms: D000927:Anticonvulsants / Q000627:therapeutic use; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D012640:Seizures / Q000188:drug therapy / Q000517:prevention & control; D012984:Software; D005680:gamma-Aminobutyric Acid / Q000031:analogs & derivatives / Q000627:therapeutic use
chemical_list: D000927:Anticonvulsants; C017985:progabide; D005680:gamma-Aminobutyric Acid