BACKGROUND The IFA test is one of the most usual methods for detecting anti-Toxoplasma antibodies, although it has not any unique standardization. It seems that the microscopic judgment of results is an important confounder in IFA test. Therefore, we conducted the present study to clarify the role of microscopic observer, and other confounders on the test. METHODS Eighty sera were collected f...

Air pollution epidemiologic studies use ambient pollutant concentrations as surrogates of personal exposure. Strong correlations among numerous ambient pollutant concentrations, however, have made it difficult to determine the relative contribution of each pollutant to a given health outcome and have led to criticism that health effect estimates for particulate matter may be biased due to confo...

Meta-analysis of observational studies is an exciting new area of innovation in statistical science. Unlike randomized controlled trials, which are the gold standard for proving causation, observational studies are prone to biases including confounding. In this article, we describe a novel Bayesian procedure to control for a confounder that is missing across the sequence of studies in a meta-...

Introduction Chronic widespread pain (CWP) is the defining feature of fibromyalgia (FM), a worldwide prevalent condition. Chronic widespread pain is, however, not pathognomonic of FM, and other conditions may present similarly with CWP, requiring consideration of a differential diagnosis. Objectives To conduct a literature search to identify medical conditions that may mimic FM and have highl...

Markov decision processes (MDPs) constitute one of the most general frameworks for modeling decision-making under uncertainty, being used in multiple fields, including economics, medicine, and engineering. The goal of the agent in an MDP setting is to learn more about the environment so as to optimize a certain criterion. This task is pursued through the exploration of the environment by active...

Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational st...