Research interests
Survival analysis with application to cancer epidemiology and HIV/AIDS research Multiple event data; Truncation and censoring; Interval and prevalent sampling
Methodologies Nonparametric; Semiparametrc; Likelihood; Estimating equation; Copula
Thesis work
Statistical methods for bivariate
survival data with interval sampling In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. We consider a sampling scheme where the first failure event (i.e., cancer-onset) is identified within a calendar time interval, the time-origin (i.e., birth) can be retrospectively confirmed, and the occurrence of the second event (i.e., death) is observed subject to right censoring. This type of sampling is referred to as ``interval sampling'' and commonly used in collection of registry or surveillance data of a disease. To analyze the bivariate survival data of this kind, it is important to recognize the presence of bias arising due to interval sampling. We develop nonparametric and semiparametric methods to analyze bivariate survival data with interval sampling under stationary and semi-stationary conditions. The related paper is submitted to Biometrics.
Collaborative projects
NIH project: Defining the Clinical Significance of HbA1c Prior to oneset of Diabetes [RA for the work conducted by Dr.Elizabeth Selvin in Welch center for Prevention Epidemiology and Clinical Research, JHMI]
NIH project: Epidemiologic study of extramedical analgesic use [RA for the work conducted by Dr.Silvia Martins in Department of Mental Health, JHSPH]
[RA for the work conducted by Dr. Ron Gray in Department of Family, Poplation and Reproductive Health, JHSPH]
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