title: Making inferences about projected completors in longitudinal studies.
abstract: In this article, we present methodology for making inferences about projected completors in the presence of attrition. The approach is motivated by a clinical trial that investigates a treatment for disability among individuals who sustain severe head injuries. Although most studies attempt to make inferences about the entire study population, our application poses important scientific questions targeting individuals who are likely to complete the study or to remain on protocol for a specified time period. We propose using measures of each individual's dropout inclination to identify projected completors and then building a stratified response model based on projected completion status. We present several prediction measures along with procedures for evaluating accuracy with respect to observed dropout. Estimation of model parameters proceeds using maximum likelihood and restricted maximum likelihood methods. We illustrate the utility of our proposed analysis by using the motivating disability data example.
authors.affiliation: Department of Biostatistics, Emory University, Atlanta, GA 30322, USA. dbowma3@sph.emory.edu
authors.forename: F DuBois
authors.initials: FD
authors.lastname: Bowman
authors.forename: Paul W
authors.initials: PW
authors.lastname: Stewart
authors.forename: Pranab K
authors.initials: PK
authors.lastname: Sen
authors.forename: Ronald W
authors.initials: RW
authors.lastname: Helms
mesh_terms: D000465:Algorithms; D000818:Animals; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D000972:Antineoplastic Agents, Phytogenic / Q000627:therapeutic use; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use; D001499:Bayes Theorem; D001932:Brain Neoplasms / Q000188:drug therapy; D002166:Camptothecin / Q000627:therapeutic use; D003606:Dacarbazine / Q000031:analogs & derivatives* / Q000627:therapeutic use; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008390:Markov Chains; D051379:Mice; D015233:Models, Statistical; D009010:Monte Carlo Method; D009368:Neoplasm Transplantation / Q000502:physiology*; D009447:Neuroblastoma / Q000188:drug therapy; D011237:Predictive Value of Tests; D000077204:Temozolomide; D014183:Transplantation, Heterologous
chemical_list: D000970:Antineoplastic Agents; D000972:Antineoplastic Agents, Phytogenic; D003606:Dacarbazine; D002166:Camptothecin; D000077204:Temozolomide