title: Modeling nonlinear time-dependent treatment effects: an application of the generalized time-varying effect model (TVEM).
abstract: OBJECTIVE
  The goal of this article is to introduce to social and behavioral scientists the generalized time-varying effect model (TVEM), a semiparametric approach for investigating time-varying effects of a treatment. The method is best suited for data collected intensively over time (e.g., experience sampling or ecological momentary assessments) and addresses questions pertaining to effects of treatment changing dynamically with time. Thus, of interest is the description of timing, magnitude, and (nonlinear) patterns of the effect.
  
  
  METHODS
  Our presentation focuses on practical aspects of the model. A step-by-step demonstration is presented in the context of an empirical study designed to evaluate effects of surgical treatment on quality of life among early stage lung cancer patients during posthospitalization recovery (N = 59; 61% female, M age = 66.1 years). Frequency and level of distress associated with physical symptoms were assessed twice daily over a 2-week period, providing a total of 1,544 momentary assessments.
  
  
  RESULTS
  Traditional analyses (analysis of covariance [ANCOVA], repeated-measures ANCOVA, and multilevel modeling) yielded findings of no group differences. In contrast, generalized TVEM identified a pattern of the effect that varied in time and magnitude. Group differences manifested after Day 4.
  
  
  CONCLUSIONS
  Generalized TVEM is a flexible statistical approach that offers insight into the complexity of treatment effects and allows modeling of nonnormal outcomes. The practical demonstration, shared syntax, and availability of a free set of macros aim to encourage researchers to apply TVEM to complex data and stimulate important scientific discoveries.
authors.affiliation: Department of Counseling & Applied Educational Psychology, Bouvé College of Health Sciences, Northeastern University.
authors.forename: Mariya P
authors.initials: MP
authors.lastname: Shiyko
authors.affiliation: Department of Psychiatry & Behavioral Sciences, Memorial Sloan-Kettering Cancer Institute.
authors.forename: Jack
authors.initials: J
authors.lastname: Burkhalter
authors.affiliation: Department of Statistics, Pennsylvania State University.
authors.forename: Runze
authors.initials: R
authors.lastname: Li
authors.affiliation: Hackensack University Medical Center.
authors.forename: Bernard J
authors.initials: BJ
authors.lastname: Park
mesh_terms: D000368:Aged; D000704:Analysis of Variance; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008175:Lung Neoplasms / Q000473:pathology / Q000523:psychology* / Q000601:surgery*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009367:Neoplasm Staging; D011013:Pneumonectomy / Q000523:psychology; D011184:Postoperative Period; D011788:Quality of Life; D013997:Time Factors; D016896:Treatment Outcome