General Design Bayesian Generalized Linear Mixed Models

Linear mixed models are able to handle an extraordinary range of complications in regression-type analyses. Their most common use is to account for within-subject correlation in longitudinal data analysis. They are also the standard vehicle for smoothing spatial count data. However, when treated in full generality, mixed models can also handle spline-type smoothing and closely approximate kriging. This allows for nonparametric regression models (e.g., additive models and varying coefficient models) to be handled within the mixed model framework. The key is to allow the random effects design matrix to have general structure; hence our label general design. For continuous response data, particularly when Gaussianity of the response is reasonably assumed, computation is now quite mature and supported by the R, SAS and S-PLUS packages. Such is not the case for binary and count responses, where generalized linear mixed models (GLMMs) are required, but are hindered by the presence of intractable multivariate integrals. Software known to us supports special cases of the GLMM (e.g., PROC NLMIXED in SAS or glmmML in R) or relies on the sometimes crude Laplace-type approximation of integrals (e.g., the SAS macro glimmix or glmmPQL in R). This paper describes the fitting of general design generalized linear mixed models. A Bayesian approach is taken and Markov chain Monte Carlo (MCMC) is used for estimation and inference. In this generalized setting, MCMC requires sampling from nonstandard distributions. In this article, we demonstrate that the MCMC package WinBUGS facilitates sound fitting of general design Bayesian generalized linear mixed models in practice.Comment: Published at http://dx.doi.org/10.1214/088342306000000015 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Comparison of Raw Acceleration from the GENEA and ActiGraph™ GT3X+ Activity Monitors

Purpose: To compare raw acceleration output of the ActiGraph™ GT3X+ and GENEA activity monitors. Methods: A GT3X+ and GENEA were oscillated in an orbital shaker at frequencies ranging from 0.7 to 4.0 Hz (ten 2-min trials/frequency) on a fixed radius of 5.08 cm. Additionally, 10 participants (age = 23.8 ± 5.4 years) wore the GT3X+ and GENEA on the dominant wrist and performed treadmill walking (2.0 and 3.5 mph) and running (5.5 and 7.5 mph) and simulated free-living activities (computer work, cleaning a room, vacuuming and throwing a ball) for 2-min each. A linear mixed model was used to compare the mean triaxial vector magnitude (VM) from the GT3X+ and GENEA at each oscillation frequency. For the human testing protocol, random forest machine-learning technique was used to develop two models using frequency domain (FD) and time domain (TD) features for each monitor. We compared activity type recognition accuracy between the GT3X+ and GENEA when the prediction model was fit using one monitor and then applied to the other. Z-statistics were used to compare the proportion of accurate predictions from the GT3X+ and GENEA for each model. Results: GENEA produced significantly higher (p \u3c 0.05, 3.5 to 6.2%) mean VM than GT3X+ at all frequencies during shaker testing. Training the model using TD input features on the GENEA and applied to GT3X+ data yielded significantly lower (p \u3c 0.05) prediction accuracy. Prediction accuracy was not compromised when interchangeably using FD models between monitors. Conclusions: It may be inappropriate to apply a model developed on the GENEA to predict activity type using GT3X+ data when input features are TD attributes of raw acceleration

Macroeconomic trends and practice models impacting acute care surgery

Acute care surgery (ACS) diagnoses are responsible for approximately a quarter of the costs of inpatient care in the US government, and individuals will be responsible for a larger share of the costs of this healthcare as the population ages. ACS as a specialty thus has the opportunity to meet a significant healthcare need, and by optimizing care delivery models do so in a way that improves both quality and value. ACS practice models that have maintained or added emergency general surgery (EGS) and even elective surgery have realized more operative case volume and surgeon satisfaction. However, vulnerabilities exist in the ACS model. Payer mix in a practice varies by geography and distribution of EGS, trauma, critical care, and elective surgery. Critical care codes constitute approximately 25% of all billing by acute care surgeons, so even small changes in reimbursement in critical care can have significant impact on professional revenue. Staffing an ACS practice can be challenging depending on reimbursement and due to uneven geographic distribution of available surgeons. Empowered by an understanding of economics, using team-oriented leadership inherent to trauma surgeons, and in partnership with healthcare organizations and regulatory bodies, ACS surgeons are positioned to significantly influence the future of healthcare in the USA

The Feasibility of Reducing and Measuring Sedentary Time among Overweight, Non-Exercising Office Workers

This study examined the feasibility of reducing free-living sedentary time (ST) and the convergent validity of various tools to measure ST. Twenty overweight/obese participants wore the activPAL (AP) (criterion measure) and ActiGraph (AG; 100 and 150 count/minute cut-points) for a 7-day baseline period. Next, they received a simple intervention targeting free-living ST reductions (7-day intervention period). ST was measured using two questionnaires following each period. ST significantly decreased from 67% of wear time (baseline period) to 62.7% of wear time (intervention period) according to AP (n = 14, P < 0.01). No other measurement tool detected a reduction in ST. The AG measures were more accurate (lower bias) and more precise (smaller confidence intervals) than the questionnaires. Participants reduced ST by ~5%, which is equivalent to a 48_min reduction over a 16-hour waking day. These data describe ST measurement properties from wearable monitors and self-report tools to inform sample-size estimates for future ST interventions

Understanding software development: Processes, organisations and technologies

Our primary goal is to understand what people do when they develop software and how long it takes them to do it. To get a proper perspective on software development processes we must study them in their context — that is, in their organizational and technological context. An extremely important means of gaining the needed understanding and perspective is to measure what goes on. Time and motion studies constitute a proven approach to understanding and improving any engineering processes. We believe software processes are no different in this respect; however, the fact that software development yields a collaborative intellectual, as opposed to physical, output calls for careful and creative measurement techniques. In attempting to answer the question &quot;what do people do in software development? &quot; we have experimented with two novel forms of data collection in the software development field: time diaries and direct observation. We found both methods to be feasible and to yield useful information about time utilization. In effect, we have quantified the effect of these social processes using the observational data. Among the insights gained from our time diary experiment are 1) developers switch between developments to minimize blocking and maximize overall throughput, and 2) there is a high degree of dynamic reassignment in response to changing project and organizational priorities. Among the insights gained from our direct observation experiment are 1) time diaries are a valid and accurate instrument with respect to their level of resolution, 2) unplanned interruptions constitute a significant time factor, and 3) the amount and kinds of communication are significant time and social factors.- 2-1