×

zbMATH — the first resource for mathematics

Improving estimation efficiency for semi-competing risks data with partially observed terminal event. (English) Zbl 1348.62129
Summary: Semi-competing risks data arise when two types of events, non-terminal and terminal, may be observed. When the terminal event occurs first, it censors the non-terminal event. Otherwise the terminal event is observable after the occurrence of the non-terminal event. In practice, it can be hard to ascertain all terminal event information after the non-terminal event. M. Yu and C. T. Yiannoutsos [Scand. J. Stat. 42, No. 1, 87–103 (2015; Zbl 1364.62267)] considered a setting when the terminal event is ascertained via double sampling from only a subset of patients who experienced the non-terminal event. They discussed estimation for marginal and conditional distributions under this double sampled semi-competing risk data framework. We propose a more efficient estimation method in the same setting by fully utilising the non-terminal event information. The efficiency gain can be substantial as observed in our simulation study.
MSC:
62G07 Density estimation
62N01 Censored data models
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] DOI: 10.1093/biomet/65.1.141 · Zbl 0394.92021 · doi:10.1093/biomet/65.1.141
[2] DOI: 10.1093/biomet/84.1.45 · Zbl 0883.62116 · doi:10.1093/biomet/84.1.45
[3] DOI: 10.1002/sim.2582 · doi:10.1002/sim.2582
[4] DOI: 10.1093/biomet/88.4.907 · Zbl 0986.62091 · doi:10.1093/biomet/88.4.907
[5] Fix E., Human Biology 23 pp 205– (1951)
[6] DOI: 10.1111/j.0006-341X.2001.00333.x · Zbl 1209.62220 · doi:10.1111/j.0006-341X.2001.00333.x
[7] DOI: 10.1111/j.1541-0420.2011.01633.x · Zbl 1241.62153 · doi:10.1111/j.1541-0420.2011.01633.x
[8] DOI: 10.1002/sim.6313 · doi:10.1002/sim.6313
[9] DOI: 10.1016/S0197-2456(02)00307-0 · doi:10.1016/S0197-2456(02)00307-0
[10] DOI: 10.1111/j.1541-0420.2007.00872.x · Zbl 1138.62062 · doi:10.1111/j.1541-0420.2007.00872.x
[11] DOI: 10.1093/biomet/80.3.573 · Zbl 0800.62700 · doi:10.1093/biomet/80.3.573
[12] Nelsen G., An Introduction to Copula, 2. ed. (2006) · Zbl 1152.62030
[13] DOI: 10.1080/01621459.1989.10478795 · doi:10.1080/01621459.1989.10478795
[14] DOI: 10.2307/2530374 · Zbl 0392.62088 · doi:10.2307/2530374
[15] Sverdrup E., Skand. Aktuarietietidskr 48 pp 184– (1965)
[16] van de Vaart A.W., Asymptotic Statistics (2000)
[17] DOI: 10.1007/978-1-4757-2545-2 · doi:10.1007/978-1-4757-2545-2
[18] DOI: 10.1111/1467-9868.00385 · Zbl 1064.62102 · doi:10.1111/1467-9868.00385
[19] DOI: 10.1111/j.1541-0420.2009.01340.x · Zbl 1203.62207 · doi:10.1111/j.1541-0420.2009.01340.x
[20] DOI: 10.1111/j.1541-0420.2009.01295.x · Zbl 1192.62123 · doi:10.1111/j.1541-0420.2009.01295.x
[21] DOI: 10.1111/sjos.12096 · Zbl 1364.62267 · doi:10.1111/sjos.12096
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.