A scripted telephone call from a trained nurse to an older adult after discharge from the ED did not reduce ED or hospital return rates or death within 30 days. Clinicaltrials.gov identifier: NCT01893931z.

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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Interval-censored failure time data arise when the failure time of interest in a survival study is not exactly observed but known only to fall within some interval. One area that often produces such data is medical studies with periodic follow-ups, in which the medical condition of interest such as the onset of a disease is only known to occur between two adjacent examination times. An important special case of intervalcensored data is current status data which arise when each study subject is observed only once and the only information available is whether the failure event of interest has occurred or not by the observation time. The areas that often yield such data include tumorigenicity experiments and cross-sectional studies. Sometimes we refer to current status data as case I interval-censored data, and the general case as case II interval-censored data. The analysis of both case I and case II interval-censored data has recently attracted a great deal of attention and many procedures have been proposed for various issues related to it. However, there are still a number of problems that remain unsolved or lack approaches that are simpler, more efficient and apply to more general situations compared to the existing ones. This is especially the case for multivariate intervalcensored data which arise if there are multiple failure times of interest and all of them suffer intervalcensoring. This dissertation focuses on the statistical analysis for bivariate interval-censored data, including regression analysis, model selection and estimation of the association between failure times.

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Failure time data subject to various types of censoring commonly arise in epidemiological and biomedical studies. Motivated by an AIDS clinical trial, we consider regression analysis of failure time data that include exact and left‐, interval‐, and/or right‐censored observations, which are often referred to as partly interval‐censored failure time data. We study the effects of potentially time‐dependent covariates on partly interval‐censored failure time via a class of semiparametric transformation models that includes the widely used proportional hazards model and the proportional odds model as special cases. We propose an EM algorithm for the nonparametric maximum likelihood estimation and show that it unifies some existing approaches developed for traditional right‐censored data or purely interval‐censored data. In particular, the proposed method reduces to the partial likelihood approach in the case of right‐censored data under the proportional hazards model. We establish that the resulting estimator is consistent and asymptotically normal. In addition, we investigate the proposed method via simulation studies and apply it to the motivating AIDS clinical trial.