Modelagem conjunta de dados longitudinais e de sobrevivência para avaliação de desfechos clínicos do parto

Abstract

As most pregnancy-related deaths and morbidities are clustered around the time of child birth, the quality of care during this period is crucial for mothers and their babies. To monitor the women at this stage, the partograph has been the central tool used in recent decades and, motivated by its simplicity, is frequently used in low-and middle-income countries. However, its use is highly questioned due to lack of evidence to justify a contribution to labor. To improve the quality of labor in these circumstances, the BOLD project has been developed in order to reduce the occurrence of pregnancy-related problems and in order to develop a modern tool, called SELMA, which is projected as an alternative to partograph. Aiming to associate fixed and dynamic characteristics evaluated in the delivery and to identify which elements can be used as triggers for performing an intervention, and thus preventing a bad outcome, this thesis proposes the use of survival models with time dependent covariates. Initially, we consider the joint modeling of survival and longitudinal data using flexible parametric hazard functions. In this sense, we propose the use of five generalizations of Weibull distribution, the Nagakami model and an inedited framework to discriminate usual parametric models via the generalized Gamma distribution, performing an extensive simulation study to evaluate the maximum likelihood estimations and the proposed discrimination criteria. Indeed, by its own nature, the birth leads us to a context of multiple events, referring to the use of multi-state models. These are models for a stochastic process which at any time occupies one of a few possible states. In general, they are the most common models to describe the development of longitudinal failure time data and are often used in medical applications. Considering this context, we proposed the inclusion of a time dependent covariate in the multi-state model using a modified version of the input data, which gave us satisfactory results similar to those expected in clinical practice.