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Title: Predictive modelling of assisted conception data with embryo-level covariates : statistical issues and application
Author: Stylianou, Christos
ISNI:       0000 0004 2718 6167
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2011
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Statistical modelling of data from the embryo transfer process of In-Vitro Fertilization (IVF) treatments is motivated by the need to perform statistical inference for potential factors and to develop predictive models for these treatments. The biggest issue arising when modelling these treatments is that a number of embryos are transferred but unless all of the embryos get implanted or fail to implant then it is not possible to identify which of the embryos implanted. Little work has been done to address this partial observability of the outcome as it arises in this context. We adopt an Embryo-Uterus (EU) framework where a patient response has distinct uterine and embryo components. This framework is used to construct statistical models, expand them to allow for clustering effects and develop a package that will enable the fitting and prediction of these models in STATA. The capabilities of this package are demonstrated in two real datasets, aimed in investigating the effect of a new embryo prognostic variable and the effect of patient clustering in these treatments. In a simulation study EU models are shown to be capable of identifying a patient covariate either as a predictor of uterine receptivity or embryo viability. However a simulation case study shows that a considerable amount of information about the embryo covariate is lost due to the partial observability of the outcome. Further simulation work evaluating the performance of a number of proposed alternatives to the EU model shows that these alternatives are either biased or conservative. The partially observed cycles are finally considered as a missing data problem and two novel modelling approaches are developed which are able to handle the structure of these treatments. These novel models, based on multiple imputation and probability weighting, are compared to the EU model using simulation in terms of predictive accuracy and are found to have similar predictive accuracy to the EU model.
Supervisor: Roberts, Stephen; Pickles, Andrew Sponsor: Medical Research Council (MRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: IVF ; EU models ; In-vitro fertilization ; statistical modelling