Confidence intervals of the relative risk and odds ratio can predict when the optimal information size in a meta-analysis is not met.

Objective: We sought to empirically evaluate whether the width of confidence interval (CI) of the relative risk (RR) and odds ratio (OR) can obviate the need for calculating the optimal information size (OIS) when making GRADE imprecision judgments.

Study Design And Setting: We analyzed a convenience sample of meta-analyses extracted from the Cochrane Database of Systematic Reviews. From each meta-analysis, we calculated OIS based on relative risk reductions (RRR) of 15%-50% and evaluated the ratio of upper to lower 95% CI boundaries of RR (RR CI ratio) and OR (OR CI ratio).

A Bayesian latent class approach to causal inference with longitudinal data.

Bayesian methods are becoming increasingly in demand in clinical and public health comparative effectiveness research. Limited literature has explored parametric Bayesian causal approaches to handle time-dependent treatment and time-dependent covariates. In this article, building on to the work on Bayesian g-computation, we propose a fully Bayesian causal approach, implemented using latent confounder classes which represent the patient's disease and health status.

Acyl-CoA Thioesterase 1 Contributes to Transition of Steatosis to Metabolic-Associated Steatohepatitis.

Background: Metabolic dysfunction-associated steatohepatitis (MASH) has become the leading cause of chronic liver disease, but there has been no approved pharmacotherapy to date.

Methods: We used a network analysis approach to delineate protein-protein interactions that contribute to the transition from steatosis to MASH, in order to identify and target this transition as a potential pharmacotherapeutic strategy. Acyl-CoA thioesterase 1 (ACOT1) was identified as a critical node in the protein-protein interaction (PPI) network of the transition from steatosis to MASH in patient samples.

An International Factorial Vignette-Based Survey of Intubation Decisions in Acute Hypoxemic Respiratory Failure.

Objectives: Intubation is a common procedure in acute hypoxemic respiratory failure (AHRF), with minimal evidence to guide decision-making. We conducted a survey of when to intubate patients with AHRF to measure the influence of clinical variables on intubation decision-making and quantify variability.

Design: Factorial vignette-based survey asking "Would you recommend intubation?" Respondents selected an ordinal recommendation from a 5-point scale ranging from "Definite no" to "Definite yes" for up to ten randomly allocated vignettes.

A Bayesian analysis integrating expert beliefs to better understand how new evidence ought to update what we believe: a use case of chiropractic care and acute lumbar disc herniation with early surgery.

Background: A Bayesian approach may be useful in the study of possible treatment-related rare serious adverse events, particularly when there are strongly held opinions in the absence of good quality previous data. We demonstrate the application of a Bayesian analysis by integrating expert opinions with population-based epidemiologic data to investigate the association between chiropractic care and acute lumbar disc herniation (LDH) with early surgery.

Methods: Experts' opinions were used to derive probability distributions of the incidence rate ratio (IRR) for acute LDH requiring early surgery associated with chiropractic care.