|Title||Parsimonious Covariate Selection for Interval Censored Data|
|Year of Publication||2020|
|Degree||Doctor of Philosophy|
|University||State University of New York at Albany|
|Keywords||Parsimonious covariate selection, Statistics|
Interval censored outcomes widely arise in many clinical trials and observational studies. In many cases, subjects are only followed-up periodically. As a result, the event of interest is known only to occur within a certain interval. We provided a method to select the parsimonious set of covariates associated with the interval censored outcome. First, the iterative sure independence screening (ISIS) method was applied to all interval censored time points across subjects to simultaneously select a set of potentially important covariates; then multiple testing approaches were used to improve the selection accuracy through refining the selection criteria, i.e. determining a refined common cutoff value. We compared the improvement of selection accuracy by using both familywise error rate (FWER)and generalized FWER (gFWER) methods. Our method shows good performance in simultaneously in selecting non-zero effects and deselecting zero-effects, respectively.