Statistical Inferences on Incomplete Time-to-Event Data with Event-Free Fractions

• Main Authors: Yuh-Chyuan Tsay, 蔡育銓
• Other Authors: Chen-Hsin Chen
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/01771175321877224574

博士 === 國立臺灣大學 === 農藝學研究所生物統計組 === 101 === Survival analysis is concerned with time-to-event data, such as time to death, time to relapse of a disease, and age at onset of a disorder. Typically, a set of time-to-event data can not be completely observed. Arising from various schemes of study design and data sampling, it may produce censored and/or truncated data. In a longitudinal follow-up study, general interval censored data are often collected. Moreover, in a longitudinal follow-up study of a healthy cohort, left truncated and interval censored (LTIC) data are frequently encountered. In traditional survival analysis, an underlying assumption is that all the study subjects are susceptible to contracting or relapsing into the disease of interest (Cox and Oakes, 1984; Kalbfleisch and Prentice, 2002). However, owing to various genetic and environmental etiologies, some study subjects may not be susceptible to the disease of interest. Moreover, due to recent progress in medical diagnostic technology strategy, many patients who could not previously be adequately treated can now be appropriately diagnosed and cured. Hence, statisticalmethods in event history analysis have considered incorporating event-free fractions such as probabilities of nonsusceptibility or cure (Miller, 1981). Recently, parametric and semiparametric regression models with the mixture survival distribution have been extensively studied for right censored data (Farewell, 1982, 1986; Kuk and Chen, 1992; Yamaguchi, 1992; Peng et al., 1998; Peng and Dear, 2000; Sy and Taylor, 2000; Li and Taylor, 2002; Lu and Ying, 2004). For LTIC data in considering event-free fraction, Chen et al. (2013) recently proposed logistic-AFT location-scale mixture regression models with nonsusceptibility for left-truncated and general interval-censored data. To the best of our knowledge, however, no nonparametric estimation has been discussed in the literature which considers both the event-free fraction and event time distribution simultaneously for LTIC data, and very few two-sample rank test statistics has been proposed for right censored data with event-free fraction. Therefore, incorporating the event-free fraction(s) with the event time distribution(s) simultaneously, we develop (i) a one-sample nonparametric estimation for LTIC data in Chapter 2 and (ii) two-sample rank tests for right censored data in Chapter 3, respectively. Besides, effects of covariates are also important for biomedical studies, and usually assessed by regression models. Therefore, to facilitate the analysis procedures, we have developed the statistical software system “EHA-RiskFree” in Chapter 4 on the methodological foundation of Chen et al. (2013) with a web-based user-friendly interface.