gems: An R Package for Simulating from Disease Progression Models

Mathematical models of disease progression predict disease outcomes and are useful epidemiological tools for planners and evaluators of health interventions. The R package gems is a tool that simulates disease progression in patients and predicts the effect of different interventions on patient outcome. Disease progression is represented by a series of events (e.g., diagnosis, treatment and death), displayed in a directed acyclic graph. The vertices correspond to disease states and the directed edges represent events. The package gems allows simulations based on a generalized multistate model that can be described by a directed acyclic graph with continuous transition-specific hazard functions. The user can specify an arbitrary hazard function and its parameters. The model includes parameter uncertainty, does not need to be a Markov model, and may take the history of previous events into account. Applications are not limited to the medical field and extend to other areas where multistate simulation is of interest. We provide a technical explanation of the multistate models used by gems, explain the functions of gems and their arguments, and show a sample application.

Outcomes of Active Surveillance for Ductal Carcinoma in Situ: A Computational Risk Analysis.

BACKGROUND Ductal carcinoma in situ (DCIS) is a noninvasive breast lesion with uncertain risk for invasive progression. Usual care (UC) for DCIS consists of treatment upon diagnosis, thus potentially overtreating patients with low propensity for progression. One strategy to reduce overtreatment is active surveillance (AS), whereby DCIS is treated only upon detection of invasive disease. Our goal was to perform a quantitative evaluation of outcomes following an AS strategy for DCIS. METHODS Age-stratified, 10-year disease-specific cumulative mortality (DSCM) for AS was calculated using a computational risk projection model based upon published estimates for natural history parameters, and Surveillance, Epidemiology, and End Results data for outcomes. AS projections were compared with the DSCM for patients who received UC. To quantify the propagation of parameter uncertainty, a 95% projection range (PR) was computed, and sensitivity analyses were performed. RESULTS Under the assumption that AS cannot outperform UC, the projected median differences in 10-year DSCM between AS and UC when diagnosed at ages 40, 55, and 70 years were 2.6% (PR = 1.4%-5.1%), 1.5% (PR = 0.5%-3.5%), and 0.6% (PR = 0.0%-2.4), respectively. Corresponding median numbers of patients needed to treat to avert one breast cancer death were 38.3 (PR = 19.7-69.9), 67.3 (PR = 28.7-211.4), and 157.2 (PR = 41.1-3872.8), respectively. Sensitivity analyses showed that the parameter with greatest impact on DSCM was the probability of understaging invasive cancer at diagnosis. CONCLUSION AS could be a viable management strategy for carefully selected DCIS patients, particularly among older age groups and those with substantial competing mortality risks. The effectiveness of AS could be markedly improved by reducing the rate of understaging.

Determination of the Weak Axial Vector Coupling λ=g_{A}/g_{V} from a Measurement of the β-Asymmetry Parameter A in Neutron Beta Decay

We report on a new measurement of the neutron beta-asymmetry parameter A with the instrument \perkeo. Main advancements are the high neutron polarization of P=99.7(1) from a novel arrangement of super mirror polarizers and reduced background from improvements in beam line and shielding. Leading corrections were thus reduced by a factor of 4, pushing them below the level of statistical error and resulting in a significant reduction of systematic uncertainty compared to our previous experiments. From the result A0=−0.11996(58), we derive the ratio of the axial-vector to the vector coupling constant λ=gA/gV=−1.2767(16)

Precise Measurement of the Neutrino Mixing Parameter θ₂₃ from Muon Neutrino Disappearance in an Off-Axis Beam

New data from the T2K neutrino oscillation experiment produce the most precise measurement of the neutrino mixing parameter θ 23 . Using an off-axis neutrino beam with a peak energy of 0.6 GeV and a data set corresponding to 6.57×10 20 protons on target, T2K has fit the energy-dependent ν μ oscillation probability to determine oscillation parameters. The 68% confidence limit on sin 2 (θ 23 ) is 0.514 +0.055 −0.056 (0.511±0.055 ), assuming normal (inverted) mass hierarchy. The best-fit mass-squared splitting for normal hierarchy is Δm 2 32 =(2.51±0.10)×10 −3   eV 2 /c 4 (inverted hierarchy: Δm 2 13 =(2.48±0.10)×10 −3   eV 2 /c 4 ). Adding a model of multinucleon interactions that affect neutrino energy reconstruction is found to produce only small biases in neutrino oscillation parameter extraction at current levels of statistical uncertainty.