Cross-sectional length-biased data arise from questions on the at-risk time for an event of interest from those who are at risk but have yet to experience the event. For example, in the National Survey on Family Growth (NSFG) women, who were currently attempting to become pregnant, were asked how long they had been attempting pregnancy. Cross-sectional survival analysis methods use the observed at-risk times to make inference on the distribution of the unobserved time-to-failure. However, methodological gaps in these methods remain such as how to handle semicompeting risks. For example, if the women attempting pregnancy had undergone fertility treatment during their current pregnancy attempt. In this paper we develop statistical methods that extend cross-sectional survival analysis methods to incorporate semicompeting risks. They can be used to estimate the distribution of the length of natural pregnancy attempts (i.e., without fertility treatment) while correctly accounting for women that sought fertility treatment prior to being sampled using cross-sectional data. We demonstrate our approach based on simulated data and an analysis of data from the NSFG. The proposed method results in separate survival curves for time-to-natural-pregnancy, time-to-fertility treatment and time-to-pregnancy after fertility treatment.
This research was supported through the Eunice Kennedy Shriver National Institute of Child Health and Human Development grant number 1R03HD097287-01.
The authors would like to thank the Editor, Associate Editor and two reviewers for helpful comments that improved the manuscript.
"Length-biased semicompeting risks models for cross-sectional data: An application to current duration of pregnancy attempt data." Ann. Appl. Stat. 15 (2) 1054 - 1067, June 2021. https://doi.org/10.1214/20-AOAS1428