title: Regression modeling of longitudinal data with outcome-dependent observation times: extensions and comparative evaluation.
abstract: Conventional longitudinal data analysis methods assume that outcomes are independent of the data-collection schedule. However, the independence assumption may be violated, for example, when a specific treatment necessitates a different follow-up schedule than the control arm or when adverse events trigger additional physician visits in between prescheduled follow-ups. Dependence between outcomes and observation times may introduce bias when estimating the marginal association of covariates on outcomes using a standard longitudinal regression model. We formulate a framework of outcome-observation dependence mechanisms to describe conditional independence given observed observation-time process covariates or shared latent variables. We compare four recently developed semi-parametric methods that accommodate one of these mechanisms. To allow greater flexibility, we extend these methods to accommodate a combination of mechanisms. In simulation studies, we show how incorrectly specifying the outcome-observation dependence may yield biased estimates of covariate-outcome associations and how our proposed extensions can accommodate a greater number of dependence mechanisms. We illustrate the implications of different modeling strategies in an application to bladder cancer data. In longitudinal studies with potentially outcome-dependent observation times, we recommend that analysts carefully explore the conditional independence mechanism between the outcome and observation-time processes to ensure valid inference regarding covariate-outcome associations.
authors: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, U.S.A.
authors: Kay See
authors: KS
authors: Tan
authors: Benjamin
authors: B
authors: French
authors: Andrea B
authors: AB
authors: Troxel
mesh_terms: D018906:Antineoplastic Agents, Alkylating / Q000627:therapeutic use; D015982:Bias; D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis; D013852:Thiotepa / Q000627:therapeutic use; D016896:Treatment Outcome; D001749:Urinary Bladder Neoplasms / Q000188:drug therapy
chemical_list: D018906:Antineoplastic Agents, Alkylating; D013852:Thiotepa
keywords: informative observation times; joint models; observation-time process; outcome process; outcome-dependent follow-up; semi-parametric regression