Weighted biomarker variability in joint analysis of longitudinal and time-to-event data-Reference-Cited by-同舟云学术

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Weighted biomarker variability in joint analysis of longitudinal and time-to-event data

Published:2024-09-01 Issue:3 Volume:18 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Wang Chunyu¹,Shen Jiaming²,Charalambous Christiana²,Pan Jianxin³

Affiliation:

1. MRC Biostatistics Unit, University of Cambridge

2. Department of Mathematics, The University of Manchester

3. Research Center for Mathematics, Beijing Normal University and Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College

Publisher

Institute of Mathematical Statistics

Reference30 articles.

1. Barrett, J. K., Huille, R., Parker, R., Yano, Y. and Griswold, M. (2019). Estimating the association between blood pressure variability and cardiovascular disease: An application using the ARIC study. Stat. Med. 38 1855–1868.

2. HAUPTMANN, M., WELLMANN, J., LUBIN, J. H., ROSENBERG, P. S. and KREIENBROCK, L. (2000). Analysis of exposure-time–response relationships using a spline weight function. Biometrics 56 1105–1108.

3. SYLVESTRE, M.-P. and ABRAHAMOWICZ, M. (2009). Flexible modeling of the cumulative effects of time-dependent exposures on the hazard. Stat. Med. 28 3437–3453.

4. ZENG, D. and CAI, J. (2005). Asymptotic results for maximum likelihood estimators in joint analysis of repeated measurements and survival time. Ann. Statist. 33 2132–2163.

5. RIZOPOULOS, D. (2012). Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. CRC Press, Boca Raton.

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