23º SINAPE - Simpósio Nacional de Probabilidade e Estatística

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Although survival models are widely used in medical research, in obstetrics and gynecology these models are not used very often, particularly when it comes to labor and childbirth. Intrapartum care practices are usually based on the study of time, such as the identification of delays and the decision to accelerate labor. Therefore, it is important to establish criteria regarding the duration of labor, which means knowing the progression of labor so as to be able, if necessary, to intervene and favor a healthy outcome. To establish integrated guidelines, a study of the associated factors should be carried out with the knowledge of how the factors occur and change over time. Then, the variable time until childbirth is of great importance and can be described by survival models. An issue that should be considered in the modelling of these studies is the inclusion of women for whom the duration of labor cannot be observed due to fetal death, generating a proportion of times equal to zero. In addition, we consider that another proportion of women's time may be censored due to some intervention. In this context, the objective of this paper is to consider the Log-Normal Zero-Inflated Cure-Rate regression model in the context of labor time and to evaluate likelihood-based estimation procedures for the parameters by a simulation study and then apply to a real dataset. In general, the inference procedures showed a better performance for larger samples and low proportions of zero inflation and cure rate. To exemplify how this model can be an important tool for investigating the course of the childbirth process, we considered the Better Outcomes in Labour Difficulty project dataset and showed that parity and educational level are associated with the main outcomes. We acknowledge World Health Organization for granting us permission to use the dataset.


Childbirth, duration of labor, cure-rate models, survival analysis, zero-inflation


Análise de Sobrevivência


Hayala Cristina Cavenague de Souza, Francisco Louzada, Mauro Ribeiro de Oliveira, Gleici da Silva Castro Perdoná