Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a supervised weighted overfitted latent class analysis.

Poor diet quality is a key modifiable risk factor for hypertension and disproportionately impacts low-income women. Analyzing diet-driven hypertensive outcomes in this demographic is challenging due to the complexity of dietary data and selection bias when the data come from surveys, a main data source for understanding diet-disease relationships in understudied populations. Supervised Bayesian model-based clustering methods summarize dietary data into latent patterns that holistically capture relationships among foods and a known health outcome but do not sufficiently account for complex survey design.

Collaborative learning framework for online stakeholder engagement.

Background: Public and stakeholder engagement can improve the quality of both research and policy decision making. However, such engagement poses significant methodological challenges in terms of collecting and analysing input from large, diverse groups.

Objective: To explain how online approaches can facilitate iterative stakeholder engagement, to describe how input from large and diverse stakeholder groups can be analysed and to propose a collaborative learning framework (CLF) to interpret stakeholder engagement results.

Bayesian Semi- and Non-parametric Models for Longitudinal Data with Multiple Membership Effects in R.

We introduce for R that performs analysis of repeated measures multiple membership (MM) data. This data structure arises in studies under which an intervention is delivered to each subject through the subject's participation in a set of multiple that characterize the intervention. In our motivating study design under which subjects receive a group cognitive behavioral therapy (CBT) treatment, an is a group CBT session and each subject attends multiple sessions that, together, comprise the treatment.

Bayesian hierarchical multivariate formulation with factor analysis for nested ordinal data.

This article devises a Bayesian multivariate formulation for analysis of ordinal data that records teacher classroom performance along multiple dimensions to assess aspects characterizing good instruction. Study designs for scoring teachers seek to measure instructional performance over multiple classroom measurement event sessions at varied occasions using disjoint intervals within each session and employment of multiple ratings on intervals scored by different raters; a design which instantiates a nesting structure with each level contributing a source of variation in recorded scores. We generally possess little a priori knowledge of the existence or form of a sparse generating structure for the multivariate dimensions at any level in the nesting that would permit collapsing over dimensions as is done under univariate modeling.

Bayesian Hierarchical Semiparametric Modelling of Longitudinal Post-treatment Outcomes from Open Enrolment Therapy Groups.

There are several challenges to testing the effectiveness of group therapy-based interventions in alcohol and other drug use (AOD) treatment settings. Enrollment into AOD therapy groups typically occurs on an open (rolling) basis. Changes in therapy group membership induce a complex correlation structure among client outcomes, with relatively small numbers of clients attending each therapy group session.

Bayesian Non-Parametric Hierarchical Modeling for Multiple Membership Data in Grouped Attendance Interventions.

We develop a dependent Dirichlet process (DDP) model for repeated measures multiple membership (MM) data. This data structure arises in studies under which an intervention is delivered to each client through a sequence of elements which overlap with those of other clients on different occasions. Our interest concentrates on study designs for which the overlaps of sequences occur for clients who receive an intervention in a shared or grouped fashion whose memberships may change over multiple treatment events.

The frequency and cost of treatment perceived to be futile in critical care.

Importance: Physicians often perceive as futile intensive care interventions that prolong life without achieving an effect that the patient can appreciate as a benefit. The prevalence and cost of critical care perceived to be futile have not been prospectively quantified.

Objective: To quantify the prevalence and cost of treatment perceived to be futile in adult critical care.