Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables.

The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelling approach that makes use of a response to guide inference toward relevant clusterings. Previous applications of profile regression have considered univariate continuous, categorical, and count outcomes.

A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19.

We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks.

Changes in in-hospital mortality in the first wave of COVID-19: a multicentre prospective observational cohort study using the WHO Clinical Characterisation Protocol UK.

Background: Mortality rates in hospitalised patients with COVID-19 in the UK appeared to decline during the first wave of the pandemic. We aimed to quantify potential drivers of this change and identify groups of patients who remain at high risk of dying in hospital.

Methods: In this multicentre prospective observational cohort study, the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK recruited a prospective cohort of patients with COVID-19 admitted to 247 acute hospitals in England, Scotland, and Wales during the first wave of the pandemic (between March 9 and Aug 2, 2020).

Optimal individualized decision rules from a multi-arm trial: A comparison of methods and an application to tailoring inter-donation intervals among blood donors in the UK.

There is a growing interest in precision medicine where individual heterogeneity is incorporated into decision-making and treatments are tailored to individuals to provide better healthcare. One important aspect of precision medicine is the estimation of the optimal individualized treatment rule (ITR) that optimizes the expected outcome. Most methods developed for this purpose are restricted to the setting with two treatments, while clinical studies with more than two treatments are common in practice.

Two-part models with stochastic processes for modelling longitudinal semicontinuous data: Computationally efficient inference and modelling the overall marginal mean.

Several researchers have described two-part models with patient-specific stochastic processes for analysing longitudinal semicontinuous data. In theory, such models can offer greater flexibility than the standard two-part model with patient-specific random effects. However, in practice, the high dimensional integrations involved in the marginal likelihood (i.

Multiple Imputation of Missing Composite Outcomes in Longitudinal Data.

In longitudinal randomised trials and observational studies within a medical context, a composite outcome-which is a function of several individual patient-specific outcomes-may be felt to best represent the outcome of interest. As in other contexts, missing data on patient outcome, due to patient drop-out or for other reasons, may pose a problem. Multiple imputation is a widely used method for handling missing data, but its use for composite outcomes has been seldom discussed.

Trivariate mover-stayer counting process models for investigating joint damage in psoriatic arthritis.

In psoriatic arthritis, many patients do not develop permanent joint damage even after a prolonged follow-up. This has led several authors to consider the possibility of a subpopulation of stayers (those who do not have the propensity to experience the event of interest), as opposed to assuming the entire population consist of movers (those who have the propensity to experience the event of interest). In addition, it is recognised that the damaged joints process may act very differently across different joint areas, particularly the hands, feet and large joints.

A systematic review of randomised controlled trials in rheumatoid arthritis: the reporting and handling of missing data in composite outcomes.

Background: Most reported outcome measures in rheumatoid arthritis (RA) trials are composite, whose components comprise single measures that are combined into one outcome. The aims of this review were to assess the range of missing data rates in primary composite outcomes and to document the current practice for handling and reporting missing data in published RA trials compared to the Consolidated Standards of Reporting Trials (CONSORT) recommendations.

Methods: A systematic search for randomised controlled trials was conducted for RA trials published between 2008 and 2013 in four rheumatology and four high impact general medical journals.

Validation of the Toronto Psoriatic Arthritis Screen Version 2 (ToPAS 2).

Objective: We previously developed and performed an initial validation of a screening questionnaire, the Toronto Psoriatic Arthritis Screen (ToPAS), for psoriatic arthritis (PsA). In our original analysis, we found that the index constructed appeared to discriminate well between those with a confirmed diagnosis of PsA and those without PsA in various clinical settings. However, it was suggested that ToPAS would benefit from additional refinement to the questions and the scoring system, because items pertaining to axial involvement were not included in our original index.

A corrected formulation for marginal inference derived from two-part mixed models for longitudinal semi-continuous data.

For semi-continuous data which are a mixture of true zeros and continuously distributed positive values, the use of two-part mixed models provides a convenient modelling framework. However, deriving population-averaged (marginal) effects from such models is not always straightforward. Su et al.

The versatility of multi-state models for the analysis of longitudinal data with unobservable features.

Multi-state models provide a convenient statistical framework for a wide variety of medical applications characterized by multiple events and longitudinal data. We illustrate this through four examples. The potential value of the incorporation of unobserved or partially observed states is highlighted.

Mixture distributions in multi-state modelling: some considerations in a study of psoriatic arthritis.

In many studies, interest lies in determining whether members of the study population will undergo a particular event of interest. Such scenarios are often termed 'mover-stayer' scenarios, and interest lies in modelling two sub-populations of 'movers' (those who have a propensity to undergo the event of interest) and 'stayers' (those who do not). In general, mover-stayer scenarios within data sets are accounted for through the use of mixture distributions, and in this paper, we investigate the use of various random effects distributions for this purpose.

A randomized placebo-controlled trial of methotrexate in psoriatic arthritis.

Objective: MTX is widely used to treat synovitis in PsA without supporting trial evidence. The aim of our study was to test the value of MTX in the first large randomized placebo-controlled trial (RCT) in PsA.

Methods: A 6-month double-blind RCT compared MTX (15 mg/week) with placebo in active PsA.

Longitudinal study of the bidirectional association between pain and depressive symptoms in patients with psoriatic arthritis.

Objective: To test the bidirectional hypothesis that depressive symptoms influence changes in pain over time, and pain influences changes in depressive symptoms.

Methods: A total of 394 patients attending the University of Toronto Psoriatic Arthritis clinic were followed over a mean period of 7.5 years with annual assessments, including number of swollen joints (SJC), Health Assessment Questionnaire (HAQ), and the Medical Outcomes Survey Short Form 36 (SF-36).

A case-study in the clinical epidemiology of psoriatic arthritis: multistate models and causal arguments.

In psoriatic arthritis, permanent joint damage characterizes disease progression and represents a major debilitating aspect of the disease. Understanding the process of joint damage will assist in the treatment and disease management of patients. Multistate models provide a means to examine patterns of disease, such as symmetric joint damage.

Intermittent observation of time-dependent explanatory variables: a multistate modelling approach.

Motivated by investigations of factors related to various patient-reported outcome measures in psoriatic arthritis patients, after controlling for the effect of disease activity on these outcomes, we outline an approach for dealing with a rapidly fluctuating explanatory variable in a multistate model. On the basis of a representation of this variable as an ordinal classification, we suggest the use of an expanded multistate model. We examine the bias in estimating effects associated with other variables via simulation for different modelling choices.