title: Repeated measures semiparametric regression using targeted maximum likelihood methodology with application to transcription factor activity discovery.
abstract: In longitudinal and repeated measures data analysis, often the goal is to determine the effect of a treatment or aspect on a particular outcome (e.g., disease progression). We consider a semiparametric repeated measures regression model, where the parametric component models effect of the variable of interest and any modification by other covariates. The expectation of this parametric component over the other covariates is a measure of variable importance. Here, we present a targeted maximum likelihood estimator of the finite dimensional regression parameter, which is easily estimated using standard software for generalized estimating equations. The targeted maximum likelihood method provides double robust and locally efficient estimates of the variable importance parameters and inference based on the influence curve. We demonstrate these properties through simulation under correct and incorrect model specification, and apply our method in practice to estimating the activity of transcription factor (TF) over cell cycle in yeast. We specifically target the importance of SWI4, SWI6, MBP1, MCM1, ACE2, FKH2, NDD1, and SWI5. The semiparametric model allows us to determine the importance of a TF at specific time points by specifying time indicators as potential effect modifiers of the TF. Our results are promising, showing significant importance trends during the expected time periods. This methodology can also be used as a variable importance analysis tool to assess the effect of a large number of variables such as gene expressions or single nucleotide polymorphisms.
authors.affiliation: University of California, Berkeley, CA, USA. ctuglus@berkeley.edu
authors.forename: Catherine
authors.initials: C
authors.lastname: Tuglus
authors.forename: Mark J
authors.initials: MJ
authors.lastname: van der Laan
mesh_terms: D001699:Biometry / Q000379:methods*; D002453:Cell Cycle; D018797:Cell Cycle Proteins / Q000378:metabolism; D003198:Computer Simulation; D004268:DNA-Binding Proteins / Q000378:metabolism; D016013:Likelihood Functions; D015233:Models, Statistical; D012044:Regression Analysis; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D018797:Cell Cycle Proteins; D004268:DNA-Binding Proteins; C083209:MBP1 protein, S cerevisiae; C118820:NDD1 protein, S cerevisiae; C062214:SWI4 protein, S cerevisiae; C080218:SWI5 protein, S cerevisiae; C075111:SWI6 protein, S cerevisiae; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors