Summary: | Indiana University-Purdue University Indianapolis (IUPUI) === Observational studies and clinical trials with time-to-event data frequently
involve multiple event types, known as competing risks. The cumulative incidence
function (CIF) is a particularly useful parameter as it explicitly quantifies clinical
prognosis. Common issues in competing risks data analysis on the CIF include interval
censoring, missing event types, and left truncation. Interval censoring occurs when
the event time is not observed but is only known to lie between two observation
times, such as clinic visits. Left truncation, also known as delayed entry, is the
phenomenon where certain participants enter the study after the onset of disease
under study. These individuals with an event prior to their potential study entry
time are not included in the analysis and this can induce selection bias. In order to
address unmet needs in appropriate methods and software for competing risks data
analysis, this thesis focuses the following development of application and methods.
First, we develop a convenient and
exible tool, the R package intccr, that performs
semiparametric regression analysis on the CIF for interval-censored competing risks
data. Second, we adopt the augmented inverse probability weighting method to deal
with both interval censoring and missing event types. We show that the resulting
estimates are consistent and double robust. We illustrate this method using data from
the East-African International Epidemiology Databases to Evaluate AIDS (IeDEA EA) where a significant portion of the event types is missing. Last, we develop an
estimation method for semiparametric analysis on the CIF for competing risks data
subject to both interval censoring and left truncation. This method is applied to the
Indianapolis-Ibadan Dementia Project to identify prognostic factors of dementia in
elder adults. Overall, the methods developed here are incorporated in the R package
intccr. === 2021-05-06
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