Browsing by Subject "interval censoring"

Now showing 1 - 3 of 3
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    Distribution‐free estimation of local growth rates around interval censored anchoring events
    (Wiley, 2019) Chu, Chenghao; Zhang, Ying; Tu, Wanzhu; Biostatistics, School of Public Health
    Biological processes are usually defined on timelines that are anchored by specific events. For example, cancer growth is typically measured by the change in tumor size from the time of oncogenesis. In the absence of such anchoring events, longitudinal assessments of the outcome lose their temporal reference. In this paper, we considered the estimation of local change rates in the outcomes when the anchoring events are interval‐censored. Viewing the subject‐specific anchoring event times as random variables from an unspecified distribution, we proposed a distribution‐free estimation method for the local growth rates around the unobserved anchoring events. We expressed the rate parameters as stochastic functionals of the anchoring time distribution and showed that under mild regularity conditions, consistent and asymptotically normal estimates of the rate parameters could be achieved, with a biom13015-gra-0001 convergence rate. We conducted a carefully designed simulation study to evaluate the finite sample performance of the method. To motivate and illustrate the use of the proposed method, we estimated the skeletal growth rates of male and female adolescents, before and after the unobserved pubertal growth spurt (PGS) times.
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    Semiparametric Regression Under Left-Truncated and Interval-Censored Competing Risks Data and Missing Cause of Failure
    (2020-04) Park, Jun; Bakoyannis, Giorgos; Yiannoutsos, Constantin T.; Zhang, Ying; Gao, Sujuan; Song, Yiqing
    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.
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    Stochastic functional estimates in longitudinal models with interval‐censored anchoring events
    (Wiley, 2020-09) Chu, Chenghao; Zhang, Ying; Tu, Wanzhu; Biostatistics, School of Public Health
    Timelines of longitudinal studies are often anchored by specific events. In the absence of the fully observed anchoring event times, the study timeline becomes undefined, and the traditional longitudinal analysis loses its temporal reference. In this paper, we considered an analytical situation where the anchoring events are interval censored. We demonstrated that by expressing the regression parameter estimators as stochastic functionals of a plug‐in estimate of the unknown anchoring event time distribution, the standard longitudinal models could be extended to accommodate the situation of less well‐defined timelines. We showed that for a broad class of longitudinal models, the functional parameter estimates are consistent and asymptotically normally distributed with a 𝑛⎯⎯√ convergence rate under mild regularity conditions. Applying the developed theory to linear mixed‐effects models, we further proposed a hybrid computational procedure that combines the strengths of the Fisher's scoring method and the expectation‐expectation (EM) algorithm for model parameter estimation. We conducted a simulation study to validate the asymptotic properties and to assess the finite sample performance of the proposed method. A real data example was used to illustrate the proposed method. The method fills in a gap in the existing longitudinal analysis methodology for data with less well‐defined timelines.