Abstract
One problem researchers face when analyzing survival data is how to handle the censoring distribution. For practical convenience, it is often assumed that the observation process generating the censoring is independent of the event time of interest. This assumption allows one to effectively ignore the censoring distribution during the analysis, but it is clearly not always realistic. Unfortunately, one cannot generally test for independent censoring without additional assumptions or information. Therefore, the researcher is faced with a choice between using methods designed for informative or non-informative censoring without knowing the true nature of the censoring. This uncertainty creates a situation where the reliability of estimation and testing procedures is unknown as the assumptions are potentially violated. Fortunately, Monte-Carlo simulation methods can be very useful for exploring these types of questions under many different conditions. This chapter uses extensive simulation studies in order to investigate the effectiveness and flexibility of two methods developed for regression analysis of informative case I and case II interval-censored data under both types of censoring. The results of these simulation studies can provide guidelines for deciding between models when facing a practical problem where one is unsure about the informativeness of the censoring distribution.
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Cook, T., Zhang, Z., Sun, J. (2017). Simulation Studies on the Effects of the Censoring Distribution Assumption in the Analysis of Interval-Censored Failure Time Data. In: Chen, DG., Chen, J. (eds) Monte-Carlo Simulation-Based Statistical Modeling . ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-3307-0_15
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DOI: https://doi.org/10.1007/978-981-10-3307-0_15
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