Analysis of longitudinal data with irregular, outcome-dependent follow-up

@article{citeulike:2974562, abstract = {Summary. A frequent problem in longitudinal studies is that subjects may miss scheduled visits or be assessed at self-selected points in time. As a result, observed outcome data may be highly unbalanced and the availability of the data may be directly related to the outcome measure and/or some auxiliary factors that are associated with the outcome. If the follow-up visit and outcome processes are correlated, then marginal regression analyses will produce biased estimates. Building on the work of Robins, Rotnitzky and Zhao, we propose a class of inverse intensity-of-visit process-weighted estimators in marginal regression models for longitudinal responses that may be observed in continuous time. This allows us to handle arbitrary patterns of missing data as embedded in a subject's visit process. We derive the large sample distribution for our inverse visit-intensity-weighted estimators and investigate their finite sample behaviour by simulation. Our approach is illustrated with a data set from a health services research study in which homeless people with mental illness were randomized to three different treatments and measures of homelessness (as percentage days homeless in the past 3 months) and other auxiliary factors were recorded at follow-up times that are not fixed by design.}, address = {Yale University, New Haven, USA; Johns Hopkins Bloomberg School of Public Health, Baltimore, USA; Veterans Affairs Northeast Program Evaluation Center and Yale University, West Haven, USA}, author = {Lin, Haiqun and Scharfstein, Daniel O. and Rosenheck, Robert A. }, citeulike-article-id = {2974562}, doi = {10.1111/j.1467-9868.2004.b5543.x}, journal = {Journal of the Royal Statistical Society Series B}, keywords = {attrition, longitudinal}, number = {3}, pages = {791--813}, posted-at = {2008-07-09 09:20:59}, priority = {2}, title = {Analysis of longitudinal data with irregular, outcome-dependent follow-up}, url = {http://dx.doi.org/10.1111/j.1467-9868.2004.b5543.x}, volume = {66}, year = {2004} }

TY - JOUR ID - citeulike:2974562 L3 - citeulike-article-id:2974562 TI - Analysis of longitudinal data with irregular, outcome-dependent follow-up JF - Journal of the Royal Statistical Society Series B VL - 66 IS - 3 SP - 791 EP - 813 AD - Yale University, New Haven, USA; Johns Hopkins Bloomberg School of Public Health, Baltimore, USA; Veterans Affairs Northeast Program Evaluation Center and Yale University, West Haven, USA N2 - Summary. A frequent problem in longitudinal studies is that subjects may miss scheduled visits or be assessed at self-selected points in time. As a result, observed outcome data may be highly unbalanced and the availability of the data may be directly related to the outcome measure and/or some auxiliary factors that are associated with the outcome. If the follow-up visit and outcome processes are correlated, then marginal regression analyses will produce biased estimates. Building on the work of Robins, Rotnitzky and Zhao, we propose a class of inverse intensity-of-visit process-weighted estimators in marginal regression models for longitudinal responses that may be observed in continuous time. This allows us to handle arbitrary patterns of missing data as embedded in a subject's visit process. We derive the large sample distribution for our inverse visit-intensity-weighted estimators and investigate their finite sample behaviour by simulation. Our approach is illustrated with a data set from a health services research study in which homeless people with mental illness were randomized to three different treatments and measures of homelessness (as percentage days homeless in the past 3 months) and other auxiliary factors were recorded at follow-up times that are not fixed by design. KW - attrition KW - longitudinal AU - Lin, Haiqun AU - Scharfstein, Daniel AU - Rosenheck, Robert PY - 2004/// UR - http://dx.doi.org/10.1111/j.1467-9868.2004.b5543.x ER -