A joint analysis proposal of nonlinear longitudinal and time-to-event right-, interval-censored data for modeling pregnancy miscarriage
Abstract
Pregnancy in-vitro fertilization (IVF) cases are associated with adverse first-trimester outcomes in comparison to spontaneously achieved pregnancies. Human chorionic gonadotrophin ? subunit (?-HCG) is a well-known biomarker for the diagnosis and monitoring of pregnancy after IVF. Low levels of ?-HCG during this period are related to miscarriage, ectopic pregnancy, and IVF procedure failures. Longitudinal profiles of ?-HCG can be used to distinguish between normal and abnormal pregnancies and to assist and guide the clinician in better management and monitoring of post-IVF pregnancies. Therefore, assessing the association between longitudinally measured ?-HCG serum concentration and time to early miscarriage is of crucial interest to clinicians. A common joint modeling approach is to use the longitudinal ?-HCG trajectory to determine the risk of miscarriage. This work was motivated by a follow-up study with normal and abnormal pregnancies where ?-HCG serum concentrations were measured in 173 young women during a gestational age of 986 days in Santiago, Chile. Some women experienced a miscarriage event, and their exact event times were unknown, so we have interval-censored data, with the event occurring between the last time of the observed measurement and ten days later. However, for those women belonging to the normal pregnancy group; that is, carrying a pregnancy to a full-term event, right censoring data are observed. Estimation procedures are based on the Stochastic Approximation of the ExpectationMaximization (SAEM) algorithm. © 2024
Más información
| Título según SCOPUS: | A joint analysis proposal of nonlinear longitudinal and time-to-event right-, interval-censored data for modeling pregnancy miscarriage |
| Título de la Revista: | Computers in Biology and Medicine |
| Volumen: | 182 |
| Editorial: | Elsevier Ltd. |
| Fecha de publicación: | 2024 |
| Idioma: | English |
| DOI: |
10.1016/j.compbiomed.2024.109186 |
| Notas: | SCOPUS |