title: Mixed effects logistic regression models for longitudinal binary response data with informative drop-out.
abstract: A shared parameter model with logistic link is presented for longitudinal binary response data to accommodate informative drop-out. The model consists of observed longitudinal and missing response components that share random effects parameters. To our knowledge, this is the first presentation of such a model for longitudinal binary response data. Comparisons are made to an approximate conditional logit model in terms of a clinical trial dataset and simulations. The naive mixed effects logit model that does not account for informative drop-out is also compared. The simulation-based differences among the models with respect to coverage of confidence intervals, bias, and mean squared error (MSE) depend on at least two factors: whether an effect is a between- or within-subject effect and the amount of between-subject variation as exhibited by variance components of the random effects distributions. When the shared parameter model holds, the approximate conditional model provides confidence intervals with good coverage for within-cluster factors but not for between-cluster factors. The converse is true for the naive model. Under a different drop-out mechanism, when the probability of drop-out is dependent only on the current unobserved observation, all three models behave similarly by providing between-subject confidence intervals with good coverage and comparable MSE and bias but poor within-subject confidence intervals, MSE, and bias. The naive model does more poorly with respect to the within-subject effects than do the shared parameter and approximate conditional models. The data analysis, which entails a comparison of two pain relievers and a placebo with respect to pain relief, conforms to the simulation results based on the shared parameter model but not on the simulation based on the outcome-driven drop-out process. This comparison between the data analysis and simulation results may provide evidence that the shared parameter model holds for the pain data.
authors.affiliation: Center for Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia 19104-6021, USA. ttenhave@cceb.upenn.edu
authors.forename: T R
authors.initials: TR
authors.lastname: Ten Have
authors.forename: A R
authors.initials: AR
authors.lastname: Kunselman
authors.forename: E P
authors.initials: EP
authors.lastname: Pulkstenis
authors.forename: J R
authors.initials: JR
authors.lastname: Landis
mesh_terms: D000700:Analgesics / Q000627:therapeutic use; D001699:Biometry; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D010146:Pain / Q000188:drug therapy; D010352:Patient Dropouts; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data
chemical_list: D000700:Analgesics