The restricted mean survival time (RMST) is often of direct interest in clinical studies involving censored survival outcomes. It describes the area under the survival curve from time zero to a specified time point. When data are subject to length-biased sampling, as is frequently encountered in observational cohort studies, existing methods cannot estimate the RMST for various restriction times through a single model. In this article, we model the RMST as a continuous function of the restriction time under the setting of length-biased sampling. Two approaches based on estimating equations are proposed to estimate the time-varying effects of covariates. Finally, we establish the asymptotic properties for the proposed estimators. Simulation studies are performed to demonstrate the finite sample performance. Two real-data examples are analyzed by our procedures.
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http://dx.doi.org/10.1177/09622802241267812 | DOI Listing |
J R Stat Soc Ser C Appl Stat
November 2024
Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, USA.
The motivation for this paper is to determine factors associated with time-to-fertility treatment (TTFT) among women currently attempting pregnancy in a cross-sectional sample. Challenges arise due to dependence between time-to-pregnancy (TTP) and TTFT. We propose appending a marginal accelerated failure time model to identify risk factors of TTFT with a model for TTP where fertility treatment is included as a time-varying treatment to account for their dependence.
View Article and Find Full Text PDFJ Hosp Infect
October 2024
Department of Public Health and Paediatrics, University of Turin, Turin, Italy.
Stat Methods Med Res
September 2024
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
The restricted mean survival time (RMST) is often of direct interest in clinical studies involving censored survival outcomes. It describes the area under the survival curve from time zero to a specified time point. When data are subject to length-biased sampling, as is frequently encountered in observational cohort studies, existing methods cannot estimate the RMST for various restriction times through a single model.
View Article and Find Full Text PDFMath Biosci Eng
November 2023
Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt.
Recent innovations have focused on the creation of new families that extend well-known distributions while providing a huge amount of practical flexibility for data modeling. Weighted distributions offer an effective approach for addressing model building and data interpretation problems. The main objective of this work is to provide a novel family based on a weighted generator called the length-biased truncated Lomax-generated (LBTLo-G) family.
View Article and Find Full Text PDFSci Rep
July 2023
Faculty of Science and Humanities, School of Postgraduate Studies and Research (SPGSR), Amoud University, Borama, 25263, Somalia.
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