The Log-Normal zero-inflated cure regression model for labor time in an African obstetric population

Hayala Cristina Cavenague de Souza, Francisco Louzada, Mauro Ribeiro de Oliveira, Bukola Fawole, Adesina Akintan, Lawal Oyeneyin, Wilfred Sanni and Gleici da Silva Castro Perdoná

Journal of Applied Statistics, 2022, vol. 49, issue 9, 2416-2429

Abstract: In obstetrics and gynecology, knowledge about how women's features are associated with childbirth is important. This leads to establishing guidelines and can help managers to describe the dynamics of pregnant women's hospital stays. Then, time is a variable of great importance and can be described by survival models. An issue that should be considered in the modeling 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. Additionally, another proportion of women's time may be censored due to some intervention. The aim of this paper was to present the Log-Normal zero-inflated cure regression model and to evaluate likelihood-based parameter estimation by a simulation study. In general, the inference procedures showed a better performance for larger samples and low proportions of zero inflation and cure. To exemplify how this model can be an important tool for investigating the course of the childbirth process, we considered the Better Outcomes in Labor Difficulty project dataset and showed that parity and educational level are associated with the main outcomes. We acknowledge the World Health Organization for granting us permission to use the dataset.

Date: 2022
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DOI: 10.1080/02664763.2021.1896684

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