Analysis of Longitudinal Data with Irregular, Informative Follow- up

A frequent problem in longitudinal studies is that subjects may miss some scheduled visits or be assessed at self-selected points in time. As a result, observed outcome data may be highly unbalanced. Also, the availability of the data may be directly related to the outcome measure or some auxiliary factors that are related to the outcomes. This situation can be viewed as informative follow-up, as well as the one of informative intermittent missing data. Analysis without accounting for informative follow-up will produce biased estimates. Building on the work of Robins, Rotnitzky and Zhao (1995), we propose a class of inverse intensity of visit process weighted estimators in marginal regression models for longitudinal responses that may be observed in a continuous-time fashion. 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 the finite sample behavior of our estimators with simulations. Our approach is also 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 and other auxiliary factors were recorded at the follow-up times that are not fixed by design.