Mixed Effects Logistic Regression Models for Longitudinal Binary Response Data with Informative Drop-Out Export

@article{tenh:kuns:pulk:land:1998, 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.}, author = {Ten Have, Thomas R. and Kunselman, Allen R. and Pulkstenis, Erik P. and Landis, J. Richard}, citeulike-article-id = {4830943}, citeulike-linkout-0 = {http://dx.doi.org/10.2307/2534023}, citeulike-linkout-1 = {http://www.jstor.org/stable/2534023}, doi = {10.2307/2534023}, issn = {0006341X}, journal = {Biometrics}, keywords = {dropout, longitudinal}, number = {1}, pages = {367--383}, posted-at = {2009-06-12 23:05:14}, priority = {2}, publisher = {International Biometric Society}, title = {Mixed Effects Logistic Regression Models for Longitudinal Binary Response Data with Informative Drop-Out}, url = {http://dx.doi.org/10.2307/2534023}, volume = {54}, year = {1998} }

TY - JOUR ID - tenh:kuns:pulk:land:1998 L3 - citeulike-article-id:4830943 N2 - 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. IS - 1 JF - Biometrics SN - 0006341X EP - 383 TI - Mixed Effects Logistic Regression Models for Longitudinal Binary Response Data with Informative Drop-Out PB - International Biometric Society VL - 54 SP - 367 KW - dropout KW - longitudinal AU - Ten Have, Thomas AU - Kunselman, Allen AU - Pulkstenis, Erik AU - Landis, Richard PY - 1998/// UR - http://dx.doi.org/10.2307/2534023 ER -