Length-biased semicompeting risks models for cross-sectional data: an application to current duration of pregnancy attempt data

DOI10.1214/20-AOAS1428zbMath1477.62316OpenAlexW3182780649MaRDI QIDQ2245182

Jiajia Zhang, Marie Thoma, Alexander C. McLain, Siyuan Guo

Publication date: 15 November 2021

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/20-aoas1428

zbMATH Keywords

cross-sectional datainfertilitysemicompeting riskslength-biased survival

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20) Reliability and life testing (62N05)

Uses Software

• R
• cmprsk

Cites Work

• Estimation in a competing risks proportional hazards model under length-biased sampling with censoring
• Prognostic score matching methods for estimating the average effect of a non-reversible binary time-dependent treatment on the survival function
• On semi-competing risks data
• The Current Duration Approach to Estimating Time to Pregnancy
• Estimation from cross-sectional samples under bias and dependence
• The competing risks illness–death model under cross-sectional sampling
• Causal estimation using semiparametric transformation models under prevalent sampling
• Covariate Bias Induced by Length-Biased Sampling of Failure Times
• Nonparametric estimation of the probability of illness in the illness-death model under cross-sectional sampling
• Multiplicative censoring, renewal processes, deconvolution and decreasing density: Nonparametric estimation
• A nonidentifiability aspect of the problem of competing risks.
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data
• Age-Specific Incidence and Prevalence: A Statistical Perspective
• Estimating Optimal Dynamic Treatment Regimes With Survival Outcomes
• Design and analysis of time-to-pregnancy
• Estimating time to pregnancy from current durations in a cross-sectional sample
• Survival analysis under cross-sectional sampling: length bias and multiplicative censoring

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