Postpartum Screening for Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus: A Cost-Effectiveness Analysis in Singapore.

Objectives: In Singapore, diabetes imposes a huge population health and economic burden. Despite that, there is paucity of evidence on the health economics of screening programs for type 2 diabetes, especially in the context of screening after gestational diabetes (GDM). The objective of this study is to assess cost-effectiveness of universal lifelong screening for type 2 diabetes after GDM, which is supported by current guidelines, compared with elective screening where 54% of mothers with GDM undertake one-off screening.

The multivariate Bernoulli detector: change point estimation in discrete survival analysis.

Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival analysis methods with a single risk suffers from biased estimation. Therefore, we propose the multivariate Bernoulli detector for competing risks with discrete times involving a multivariate change point model on the cause-specific baseline hazards.

Joint modelling of mental health markers through pregnancy: a Bayesian semi-parametric approach.

Maternal depression and anxiety through pregnancy have lasting societal impacts. It is thus crucial to understand the trajectories of its progression from preconception to postnatal period, and the risk factors associated with it. Within the Bayesian framework, we propose to jointly model seven outcomes, of which two are physiological and five non-physiological indicators of maternal depression and anxiety over time.

Mutual information: Measuring nonlinear dependence in longitudinal epidemiological data.

Given a large clinical database of longitudinal patient information including many covariates, it is computationally prohibitive to consider all types of interdependence between patient variables of interest. This challenge motivates the use of mutual information (MI), a statistical summary of data interdependence with appealing properties that make it a suitable alternative or addition to correlation for identifying relationships in data. MI: (i) captures all types of dependence, both linear and nonlinear, (ii) is zero only when random variables are independent, (iii) serves as a measure of relationship strength (similar to but more general than R2), and (iv) is interpreted the same way for numerical and categorical data.

Bayesian clustering of multiple zero-inflated outcomes.

Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes.

Bayesian inference on the number of recurrent events: A joint model of recurrence and survival.

The number of recurrent events before a terminating event is often of interest. For instance, death terminates an individual's process of rehospitalizations and the number of rehospitalizations is an important indicator of economic cost. We propose a model in which the number of recurrences before termination is a random variable of interest, enabling inference and prediction on it.

Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore.

Background: Precision medicine (PM) programs typically use broad consent. This approach requires maintenance of the social license and public trust. The ultimate success of PM programs will thus likely be contingent upon understanding public expectations about data sharing and establishing appropriate governance structures.

The Search for Optimal Oxygen Saturation Targets in Critically Ill Patients: Observational Data From Large ICU Databases.

Background: Although low oxygen saturations are generally regarded as deleterious, recent studies in ICU patients have shown that a liberal oxygen strategy increases mortality. However, the optimal oxygen saturation target remains unclear. The goal of this study was to determine the optimal range by using real-world data.

Extrema-weighted feature extraction for functional data.

Motivation: Although there is a rich literature on methods for assessing the impact of functional predictors, the focus has been on approaches for dimension reduction that do not suit certain applications. Examples of standard approaches include functional linear models, functional principal components regression and cluster-based approaches, such as latent trajectory analysis. This article is motivated by applications in which the dynamics in a predictor, across times when the value is relatively extreme, are particularly informative about the response.

Effect of A1C and Glucose on Postoperative Mortality in Noncardiac and Cardiac Surgeries.

Objective: Hemoglobin A (A1C) is used in assessment of patients for elective surgeries because hyperglycemia increases risk of adverse events. However, the interplay of A1C, glucose, and surgical outcomes remains unclarified, with often only two of these three factors considered simultaneously. We assessed the association of preoperative A1C with perioperative glucose control and their relationship with 30-day mortality.