Quantitative Predictive Modelling Approaches to Understanding Rheumatoid Arthritis: A Brief Review

Rheumatoid arthritis is a chronic autoimmune disease that is a major public health challenge. The disease is characterised by inflammation of synovial joints and cartilage erosion, which lead to chronic pain, poor life quality and, in some cases, mortality. Understanding the biological mechanisms behind the progression of the disease, as well as developing new methods for quantitative predictions of disease progression in the presence/absence of various therapies is important for the success of therapeutic approaches. The aim of this study is to review various quantitative predictive modelling approaches for understanding rheumatoid arthritis. To this end, we start by briefly discussing the biology of this disease and some current treatment approaches, as well as emphasising some of the open problems in the field. Then, we review various mathematical mechanistic models derived to address some of these open problems. We discuss models that investigate the biological mechanisms behind the progression of the disease, as well as pharmacokinetic and pharmacodynamic models for various drug therapies. Furthermore, we highlight models aimed at optimising the costs of the treatments while taking into consideration the evolution of the disease and potential complications.

Use of linear models to analyze physicians' decisions

Linear models of judgment are powerful tools for studying medical decision making. The recent increase in applications of these models to medicine reflects more available computing resources and the parallel development of clinical prediction rules derived from multivariate analysis of patient data. Psychological research into expert and novice decision making shows that linear models derived from judges' decisions usually predict future decisions more accurately than either the judge or a mechanical application of the judge's stated policies. Studies of medical decision making have shown similar results, as well as marked variation among experts in how they appear to use clinical information. Cognitive feedback, which is feedback to the learner of the judgment model derived from previous decisions, is highly effective for teaching complex judgment tasks. Many technical problems remain to be mastered in constructing linear models of medical judgment. These include how to select the correct variables, how to provide a selection of variables broad enough to accommodate individual variations in strategy, how to model intercorrelated variables, and how to characterize and aggregate individual strategies. Despite the methodologic challenges, linear models remain a powerful method for studying how physicians combine multiple items of imperfect information to make a judgment. These techniques may provide important insights into variation in physician judgments. In addition, they hold promise in teaching the appropriate integration of complex data in the day-to-day practice of medicine.