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 (1995), we propose a class of inverse intensity of visit process weighted estimators in marginal regression models for longitudinal outcomes 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 behavior via 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 past three months) and other auxiliary factors were recorded at follow-up times that are not fixed by design. This work with Haiqun Lin and Robert Rosenheck from Yale University.