Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation

TL;DR

This paper explores multivariate prediction models for sequential IVF outcomes, comparing joint modeling to separate models, and finds no significant advantage in joint approaches for the data analyzed.

Contribution

It develops methods for multivariate prediction of mixed, multilevel, sequential IVF outcomes and evaluates their utility in real clinical data.

Findings

Joint modeling showed no clear benefit over separate models.

Sequential responses can be predicted dynamically using previous outcomes.

Multivariate approaches are feasible but may not always outperform simpler methods.

Abstract

In vitro fertilization (IVF) comprises a sequence of interventions concerned with the creation and culture of embryos which are then transferred to the patient's uterus. While the clinically important endpoint is birth, the responses to each stage of treatment contain additional information about the reasons for success or failure. As such, the ability to predict not only the overall outcome of the cycle, but also the stage-specific responses, can be useful. This could be done by developing separate models for each response variable, but recent work has suggested that it may be advantageous to use a multivariate approach to model all outcomes simultaneously. Here, joint analysis of the sequential responses is complicated by mixed outcome types defined at two levels (patient and embryo). A further consideration is whether and how to incorporate information about the response at each stage in models for subsequent stages. We develop a case study using routinely collected data from a large reproductive medicine unit in order to investigate the feasibility and potential utility of multivariate prediction in IVF. We consider two possible scenarios. In the first, stage-specific responses are to be predicted prior to treatment commencement. In the second, responses are predicted dynamically, using the outcomes of previous stages as predictors. In both scenarios, we fail to observe benefits of joint modelling approaches compared to fitting separate regression models for each response variable.