Dr. Peter Adamic

Assistant Professor

Department of Mathematics and Computer Science

Laurentian University

* Title: *" Nonparametric Competing-Risks Modeling"

* Abstract:*

In this seminar, Self-Consistent (SC) Expectation-Maximization (EM) algorithms will be examined that

nonparametrically estimate the associated single risk survival functions in a competing risks context.

The algorithms are a generalization of the univariate algorithms of Efron (1967) and Turnbull (1974, 1976),

and are carried out in the presence of both double and interval-censored data. Unlike any previous nonparametric

models proposed in the literature to date, the algorithms will explicitly allow for the possibility of masked

modes of failure, where failure is known to occur due to a subset from the set of all possible causes. A second

major step in the model building process is subsequently pursued. Many of the modeling difficulties associated

with censoring are remedied by utilizing a modified SC/EM algorithm that incorporates kernel smoothing at each

iteration of the generalized algorithms. This modification is an extension of similar work done by Li, Watkins,

and Yu (1997), and Braun, Duchesne, and Stafford (2005). The talk will conclude with a brief discussion of the

usefulness of the proposed algorithms within the current state of competing risks scholarship - as well as

exploring areas where derivative work would prove to be fruitful.