Abstract
In some situations, the failure time of interest is defined as the gap time between two related events and the observations on both event times can suffer either right or interval censoring. Such data are usually referred to as doubly censored data and frequently encountered in many clinical and observational studies. Additionally, there may also exist a cured subgroup in the whole population, which means that not every individual under study will experience the failure time of interest eventually. In this paper, we consider regression analysis of doubly censored data with a cured subgroup under a wide class of flexible transformation cure models. Specifically, we consider marginal likelihood estimation and develop a two-step approach by combining the multiple imputation and a new expectation-maximization (EM) algorithm for its implementation. The resulting estimators are shown to be consistent and asymptotically normal. The finite sample performance of the proposed method is investigated through simulation studies. The proposed method is also applied to a real dataset arising from an AIDS cohort study for illustration.
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Supported by the National Natural Science Foundation of China (Grant Nos. 11771431, 11690015, 11926341, 11901128 and 11601097), Key Laboratory of RCSDS, CAS (Grant Nos. 2008DP 173182) and Natural Science Foundation of Guangdong Province of China (Grant No. 2018A030310068)
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Cai, M., Xiao, L.Q. & Li, S.W. Regression Analysis of Doubly Censored Data with a Cured Subgroup under a Class of Promotion Time Cure Models. Acta. Math. Sin.-English Ser. 37, 835–853 (2021). https://doi.org/10.1007/s10114-021-0151-x
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DOI: https://doi.org/10.1007/s10114-021-0151-x