Development of methodology to support molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: The STEpUP OA consortium.

Objectives: To develop a protocol for largescale analysis of synovial fluid proteins, for the identification of biological networks associated with subtypes of osteoarthritis.

Methods: Synovial Fluid To detect molecular Endotypes by Unbiased Proteomics in Osteoarthritis (STEpUP OA) is an international consortium utilising clinical data (capturing pain, radiographic severity and demographic features) and knee synovial fluid from 17 participating cohorts. 1746 samples from 1650 individuals comprising OA, joint injury, healthy and inflammatory arthritis controls, divided into discovery (n = 1045) and replication (n = 701) datasets, were analysed by SomaScan Discovery Plex V4.

Dynamic factor analysis with dependent Gaussian processes for high-dimensional gene expression trajectories.

The increasing availability of high-dimensional, longitudinal measures of gene expression can facilitate understanding of biological mechanisms, as required for precision medicine. Biological knowledge suggests that it may be best to describe complex diseases at the level of underlying pathways, which may interact with one another. We propose a Bayesian approach that allows for characterizing such correlation among different pathways through dependent Gaussian processes (DGP) and mapping the observed high-dimensional gene expression trajectories into unobserved low-dimensional pathway expression trajectories via Bayesian sparse factor analysis.

Protocol for a double-blind placebo-controlled randomised controlled trial assessing the impact of oral semaglutide in amyloid positivity (ISAP) in community dwelling UK adults.

Introduction: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), currently marketed for type 2 diabetes and obesity, may offer novel mechanisms to delay or prevent neurotoxicity associated with Alzheimer's disease (AD). The impact of semaglutide in amyloid positivity (ISAP) trial is investigating whether the GLP-1 RA semaglutide reduces accumulation in the brain of cortical tau protein and neuroinflammation in individuals with preclinical/prodromal AD.

Methods And Analysis: ISAP is an investigator-led, randomised, double-blind, superiority trial of oral semaglutide compared with placebo.

Exploring the Big Data Paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS).

Selection bias poses a substantial challenge to valid statistical inference in nonprobability samples. This study compared estimates of the first-dose COVID-19 vaccination rates among Indian adults in 2021 from a large nonprobability sample, the COVID-19 Trends and Impact Survey (CTIS), and a small probability survey, the Center for Voting Options and Trends in Election Research (CVoter), against national benchmark data from the COVID Vaccine Intelligence Network. Notably, CTIS exhibits a larger estimation error on average (0.

Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants.

A leading explanation for translational failure in neurodegenerative disease is that new drugs are evaluated late in the disease course when clinical features have become irreversible. Here, to address this gap, we cognitively profiled 21,051 people aged 17-85 years as part of the Genes and Cognition cohort within the National Institute for Health and Care Research BioResource across England. We describe the cohort, present cognitive trajectories and show the potential utility.

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.

Impact of penicillin allergy labels on surgical site infections in a large UK cohort of gastrointestinal surgery patients.

Objectives: Studies in the USA, Canada and France have reported higher surgical site infection (SSI) risk in patients with a penicillin allergy label (PAL). Here, we investigate the association between PALs and SSI in the UK, a country with distinct epidemiology of infecting pathogens and range of antimicrobial regimens in routine use.

Methods: Electronic health records and national SSI surveillance data were collated for a retrospective cohort of gastrointestinal surgery patients at Cambridge University Hospitals NHS Foundation Trust from 1 January 2015 to 31 December 2021.

A data-adaptive method for investigating effect heterogeneity with high-dimensional covariates in Mendelian randomization.

Background: Mendelian randomization is a popular method for causal inference with observational data that uses genetic variants as instrumental variables. Similarly to a randomized trial, a standard Mendelian randomization analysis estimates the population-averaged effect of an exposure on an outcome. Dividing the population into subgroups can reveal effect heterogeneity to inform who would most benefit from intervention on the exposure.

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study.

Objectives: To develop a disease stratification model for COVID-19 that updates according to changes in a patient's condition while in hospital to facilitate patient management and resource allocation.

Design: In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression.

Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness.

Biases have marked medical history, leading to unequal care affecting marginalised groups. The patterns of missingness in observational data often reflect these group discrepancies, but the algorithmic fairness implications of group-specific missingness are not well understood. Despite its potential impact, imputation is too often an overlooked preprocessing step.

Characterization of missing data patterns and mechanisms in longitudinal composite outcome trial in rheumatoid arthritis.

Background: Composite measures, like the Disease Activity Score for 28 joints (DAS28), are key primary outcomes in rheumatoid arthritis (RA) trials. DAS28 combines four different components in a continuous measure. When one or more of these components are missing the overall composite score is also missing at intermediate or trial endpoint assessments.

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.

Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering.

Survival analysis involves the modelling of the times to event. Proposed neural network approaches maximise the predictive performance of traditional survival models at the cost of their interpretability. This impairs their applicability in high stake domains such as medicine.

Baseline predictors of remission, pain and fatigue in rheumatoid arthritis: the TITRATE trial.

Background: Clinical trials show intensive treatment to induce remission is effective in patients with highly active rheumatoid arthritis (RA). The TITRATE trial showed that the benefits of intensive treatment also extend to moderately active RA. However, many patients failed to achieve remission or show improvements in pain and fatigue.

A Report on the Impact of the COVID-19 Pandemic on the Health and Social Welfare of the Pacific Islander Population in Hawai'i.

Hawai'i's Pacific Islander (PI) population has suffered a higher burden of coronavirus disease 2019 (COVID-19) infections, hospitalizations, and deaths compared to other groups in the state. The Hawai'i Emergency Management Agency Community Care Outreach Unit conducted an assessment across the state to gain an understanding of the impact of the COVID-19 pandemic on the health and social welfare of households. Survey data was collected from individuals across the state during a period of 3 weeks (August 12-September 5, 2020).

In-depth phenotyping for clinical stratification of Gaucher disease.

Background: The Gaucher Investigative Therapy Evaluation is a national clinical cohort of 250 patients aged 5-87 years with Gaucher disease in the United Kingdom-an ultra-rare genetic disorder. To inform clinical decision-making and improve pathophysiological understanding, we characterized the course of Gaucher disease and explored the influence of costly innovative medication and other interventions. Retrospective and prospective clinical, laboratory and radiological information including molecular analysis of the GBA1 gene and comprising > 2500 variables were collected systematically into a relational database with banking of collated biological samples in a central bioresource.

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).

Inference under unequal probability sampling with the Bayesian exponentially tilted empirical likelihood.

Fully Bayesian inference in the presence of unequal probability sampling requires stronger structural assumptions on the data-generating distribution than frequentist semiparametric methods, but offers the potential for improved small-sample inference and convenient evidence synthesis. We demonstrate that the Bayesian exponentially tilted empirical likelihood can be used to combine the practical benefits of Bayesian inference with the robustness and attractive large-sample properties of frequentist approaches. Estimators defined as the solutions to unbiased estimating equations can be used to define a semiparametric model through the set of corresponding moment constraints.

Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.

Objective: Informative presence (IP) is the phenomenon whereby the presence or absence of patient data is potentially informative with respect to their health condition, with informative observation (IO) being the longitudinal equivalent. These phenomena predominantly exist within routinely collected healthcare data, in which data collection is driven by the clinical requirements of patients and clinicians. The extent to which IP and IO are considered when using such data to develop clinical prediction models (CPMs) is unknown, as is the existing methodology aiming at handling these issues.

The clinical effectiveness of intensive management in moderate established rheumatoid arthritis: The titrate trial.

Objectives: Many trials have shown that intensive management is effective in patients with early active rheumatoid arthritis (RA). But its benefits are unproven for the large number of RA patients seen in routine care who have established, moderately active RA and are already taking conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs). The TITRATE trial studied whether these patients also benefit from intensive management and, in particular, achieve more remissions.

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.

Efficient Real-Time Monitoring of an Emerging Influenza Pandemic: How Feasible?

A prompt public health response to a new epidemic relies on the ability to monitor and predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK revealed pandemic data as noisy, contaminated, potentially biased and originating from multiple sources. This seriously challenges the capacity for real-time monitoring.

The European Prevention of Alzheimer's Dementia (EPAD) Longitudinal Cohort Study: Baseline Data Release V500.0.

Background: The European Prevention of Alzheimer's Dementia (EPAD) Programme is a pan-European project whose objective is to deliver a platform, adaptive, Phase 2 proof of concept (PoC) trial for the secondary prevention of Alzheimer's dementia. A component of this platform is the Longitudinal Cohort Study (LCS) which acts as a readiness cohort for the PoC Trial as well as generating data for disease modelling work in the preclinical and prodromal phases of Alzheimer's dementia.

Objectives: The first data wave has been collected, quality checked, released and now available for analysis to answer numerous research questions.