Sequentially calibrating a Bayesian microsimulation model to incorporate new information and assumptions

Background Microsimulation models are mathematical models that simulate event histories for individual members of a population. They are useful for policy decisions because they simulate a large number of individuals from an idealized population, with features that change over time, and the resulting event histories can be summarized to describe key population-level outcomes. Model calibration is the process of incorporating evidence into the model. Calibrated models can be used to make predictions about population trends in disease outcomes and effectiveness of interventions, but calibration can be challenging and computationally expensive. Methods This paper develops a technique for sequentially updating models to take full advantage of earlier calibration results, to ultimately speed up the calibration process. A Bayesian approach to calibration is used because it combines different sources of evidence and enables uncertainty quantification which is appealing for decision-making. We develop this method in order to re-calibrate a microsimulation model for the natural history of colorectal cancer to include new targets that better inform the time from initiation of preclinical cancer to presentation with clinical cancer (sojourn time), because model exploration and validation revealed that more information was needed on sojourn time, and that the predicted percentage of patients with cancers detected via colonoscopy screening was too low. Results The sequential approach to calibration was more efficient than recalibrating the model from scratch. Incorporating new information on the percentage of patients with cancers detected upon screening changed the estimated sojourn time parameters significantly, increasing the estimated mean sojourn time for cancers in the colon and rectum, providing results with more validity. Conclusions A sequential approach to recalibration can be used to efficiently recalibrate a microsimulation model when new information becomes available that requires the original targets to be supplemented with additional targets.

Geology of southeastern Barbados

ABSTRACT This chapter presents geological documentation of Quaternary (and perhaps older) event histories of southeastern Barbados. The Barbados Limestone is herein formally defined. A time-stratigraphic division of the Barbados Limestone in southeastern Barbados and the properties of the stratigraphic units are presented. A major finding of this study is that the marine terraces originated wholly by marine erosion, not by reef construction, and evolved in stages over a long duration. The hydrology and thickness data of the Barbados Limestone are discussed, and hypotheses on causes of thickness variations are given. The study domain is divided into seven areas that contain a continuous flight of nine marine terraces preserved in various partial sequences. Discussions of these key seven areas in southeastern Barbados are supported by geologic maps at large scale and cross sections. Sections with VE > 1 display limestone stratigraphy and facies over relatively large lengths. Sections with VE = 1 show true structural configurations over short lengths. Detailed observations and radioisotopic dating of the limestone units permit differentiation and correlation among them.

Estimation of the increased risk associated with recurrent events or polyvascular atherosclerotic cardiovascular disease in the United Kingdom

Aims The aims of this study were to re-estimate the international REduction of Atherothrombosis for Continued Health (REACH) risk equation using United Kingdom data and to distinguish different relative hazards for specific atherosclerotic cardiovascular disease event histories. Methods and results Patients in the UK Clinical Research Practice Datalink (CPRD) were included as of 1 January 2005 if they were 40 years or older, had 2 or more years of prior data, received one or more moderate or high-intensity statin in the previous year, and had a history of myocardial infarction, ischemic stroke, or other atherosclerotic cardiovascular disease. Patients were followed until a composite endpoint of myocardial infarction, ischemic stroke or cardiovascular death, loss to follow-up, or end of observation. We re-estimated the REACH risk equation hazard ratios (HRs) using CPRD data (re-estimated REACH model). Our event history model replaced the REACH vascular bed variables with more specific event histories. There were 60,838 patients with 5.25 years of mean follow-up. In the validation model, HRs were in the same direction, and generally greater than REACH. In the event history model, HRs compared to other atherosclerotic cardiovascular disease alone included: recurrent myocardial infarction (HR 1.19, 95% confidence interval (CI) 1.05–1.34), recurrent ischemic stroke (HR 1.36, 95% CI 1.03–1.80), myocardial infarction and other atherosclerotic cardiovascular disease (HR 1.31, 95% CI 1.23–1.38), ischemic stroke and other atherosclerotic cardiovascular disease (HR 1.40, 95% CI 1.23–1.60), myocardial infarction and ischemic stroke (HR 1.94, 95% CI 1.23–3.04), and myocardial infarction, ischemic stroke and other atherosclerotic cardiovascular disease (HR 1.93, 95% CI 1.47–2.54). Conclusion A detailed cardiovascular event history may be useful for estimating the relative risk of future cardiovascular events.

Belle2VR: An Interactive Virtual Reality Visualization of GEANT4 Event Histories

Belle2VR is a novel interactive virtual reality visualization of the Belle II detector at KEK and the animation therein of GEANT4-simulated event histories. The user, wearing a VR headset, manipulates a gamepad or hand controller(s) to interact with and interrogate the detailed GEANT4 event history over time, to adjust the visibility and transparency of the particles and detector subsystems, to translate freely in 3D, to zoom in or out, and to control the event-history timeline. In this way, the user explores the world of subatomic physics via electron-positron collision events in the Belle II experiment at the SuperKEKB colliding-beam facility at KEK in Japan. Developed at Virginia Tech by an interdisciplinary team of researchers in physics, education, and virtual environments, the simulation is intended to be integrated into the undergraduate physics curriculum.

Heavy-tailed distributions for building stock data

The question of inferring the owner of a set of building stocks (e.g. from which country the buildings are taken) from building-related quantities like number of buildings or types of building event histories necessitates the knowledge of their distributions in order to compare them. If the distribution function is a power law, then a version of the 80/20 rule can be applied to describe the variable. This distribution is an example of a heavy-tailed distribution; another example is the log-normal distribution. Heavy-tailed distributions have the property that studying the effects of the few large values already yields most of the overall effect of the whole quantity. For example, if reducing the CO2 emissions of the buildings of a country is the issue, then in case of a heavy-tailed distribution, only the effects of the relatively few large cities need to be considered. It is shown that the number of buildings in German municipalities or counties or the number of building-related event histories of a certain vanished building stock follow a heavy-tailed distribution and give evidence for the type of underlying distribution. The methodology used is a recent statistical framework for discerning power law and other heavy-tailed distributions in empirical data.

Panel Size, Office Visits, and Care Coordination Events: A New Workload Estimation Methodology Based on Patient Longitudinal Event Histories

Background. Panel size, or the number of patients a primary care physician (PCP) and her care team can feasibly manage as part of a practice, remains a vital question in primary care. Objective. To Illustrate a new methodology for quantifying two types of workload associated with a panel size: 1) the PCP weekly office visit distribution and 2) the weekly distribution of non-PCP events (subspecialty visits, emergency room visits, hospitalizations) that potentially require non–face-to-face coordination. Methods. We assemble granular individual-level histories of events in the health system using the Medical Expenditure Panel Survey from 2011. Using the date on which each event occurred, we create weekly utilization estimates as a function of panel size for the general population and Medicare patients. Results. A PCP with a panel of 2,000 adults approximately representative of the US population can expect to have 93.54 office visits on average each week. A simple model quantifying demand–capacity mismatch suggests that a PCP with a weekly capacity of 80 to 90 appointments will struggle to satisfy this office-visit demand in a timely manner. Furthermore, each week the PCP can expect the same panel to have 9.08 visits to the emergency room, 4.69 hospital inpatient events, and 131.29 office-based visits to non–primary care subspecialists; these events contribute to the non–face-to-face coordination workload, increasing the probability of an overburdened workweek. Both PCP office visit and coordination events are highly concentrated in less than 200 individuals (<10% of the 2,000). Conclusion. Patient-level longitudinal event histories can be retrospectively assembled to quantify patterns of face-to-face office visits and coordination workload associated with a primary care panel.