Disease progression is often monitored by intermittent follow-up visits in longitudinal cohort studies, resulting in time-to-event outcomes subject to interval censoring. Furthermore, the timing and frequency of clinical visits are often found related to a person's history of disease-related variables such as disease progression and treatment, thus producing outcome-dependent observation times. Standard statistical methods for interval-censored data analysis reply on the assumption that observation times are independent of the time-to-event outcome. Otherwise, selection bias will be induced. In this presentation, I will introduce a so-called inverse-intensity-of-visit (IIV) weighting approach which models observation times as a recurrent event process and incorporates the estimated visit intensities in the model of time-to-event outcome. Parametric, non-parametric and semi-parametric methods are specifically developed based on that. Simulations are conducted to examine the finite sample performance of the proposed estimators, compared with standard analysis methods. A dataset from the Toronto Psoriatic Arthritis (PsA) Cohort Study is used to illustrate the proposed methodologies.