This article provides a brief introduction to the state space modeling capabilities in SAS, a well-known statistical software system. SAS provides state space modeling in a few different settings. SAS/ETS, the econometric and time series analysis module of the SAS system, contains many procedures that use state space models to analyze univariate and multivariate time series data. In addition, SAS/IML, an interactive matrix language in the SAS system, provides Kalman filtering and smoothing routines for stationary and nonstationary state space models. SAS/IML also provides support for linear algebra and nonlinear function optimization, which makes it a convenient environment for generalpurpose state space modeling.

The purpose of this study was to determine the prevalence of musculoskeletal disorders in patients with chest pain and angiographically normal coronary arteries. The authors studied 40 consecutive patients with chest pain presenting at an Internal Medicine Clinic who had undergone coronary angiography and were found to have < 30% stenosis of all major coronary arteries. Patients with any known noncardiac cause of chest pain were excluded from the study. Each patient underwent a complete rheumatologic examination with x-rays and blood tests when indicated. The diagnosis of fibromyalgia was based on the presence of at least eight paired tender points. The diagnosis of costochondritis was made when palpation of the costal cartilages elicited tenderness. In the normal coronary artery group, 30% of the patients had fibromyalgia and 10% had costochondritis. In the control group of 40 patients with coronary artery disease, only 1 patient had fibromyalgia and none had costochondritis (P < 0.04). Other rheumatologic disorders were uncommon, with no statistical difference between the two groups. The authors conclude that many patients with chest pain and angiographically normal coronary arteries suffer from rheumatologic disorders with fibromyalgia being the most common.

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Objective: Electrical impedance myography (EIM) is a quantitative and objective tool to evaluate muscle status. EIM offers the possibility to replace conventional physical functioning scores or quality of life measures which depend on patient cooperation and mood. Methods: Here, we propose a functional mixed-effects model using a state-space approach to describe the response trajectories of EIM data measured on 16 boys with Duchenne muscular dystrophy (DMD) and 12 healthy controls, both groups measured over a period of 2 years. The modeling framework presented imposes a smoothing spline structure on EIM data collected at each visit and taking into account of within subject correlations of these curves along the longitudinal measurements. The modeling framework is recast in a state-space approach, thereby allowing for the employment of computationally efficient diffuse Kalman filtering and smoothing

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