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.

Budget 2022

This Article outlines and assesses changes to the tax and welfare system announced as part of Budget 2022. It first looks at the main taxation measures announced before turning to employment, education and social welfare supports. It then considers the effect of the package of measures as a whole on the incomes of households using representative survey data from the Survey of Incomes and Living Conditions run on SWITCH – the ESRI’s tax and benefit microsimulation model – and ITSim – an indirect tax microsimulation model developed jointly by the ESRI and the Department of Finance. The Article concludes with some brief reflections on inflation forecasts and the policy-making process.

A Bayesian Neural Network-Based Method to Calibrate Microscopic Traffic Simulators

Calibrating the microsimulation model is essential to enhance its ability to capture reality. The paper proposes a Bayesian neural network (BNN)-based method to calibrate parameters of microscopic traffic simulators, which reduces repeated running of simulations in the calibration and thus significantly improves the calibration efficiency. We use BNN with probability distributions on the weights to quickly predict the simulation results according to the inputs of the parameters to be calibrated. Based on the BNN model with the best performance, heuristic algorithms (HAs) are performed to seek the optimal values of the parameters to be calibrated with the minimum difference between the predicted results of BNN and the field-measured values. Three HAs are considered, including genetic algorithm (GA), evolutionary strategy (ES), and bat algorithm (BA). A TransModeler case of highway links in Shanghai, China, indicates the validity of the proposed calibration method in terms of error and efficiency. The results demonstrate that the BNN model is able to accurately predict the simulation and adequately capture the uncertainty of the simulation. We also find that the BNN-BA model produces the best calibration efficiency, while the BNN-ES model offers the best performance in calibration accuracy.

Family as a Redistributive Principle of the Welfare State. The Case of Germany

Family is one of the major principles of welfare state redistribution. It has, however, rarely been at the centre of welfare state research. This contribution intends to help remedy the research gap in family-related redistribution. By examining the German welfare state which is known to be both redistributive and family-oriented, we want to answer the question of how and how far the German welfare state institutionalises family as a redistributive principle. Our case-study of German welfare state regulations in terms of family is based on the tax-benefit microsimulation model EUROMOD and its Hypothetical Household Tool (HHoT). We differentiate 54 family forms to adequately reflect our three theoretical assumptions, which are: (1) redistributive logics differ across family forms, and in part markedly; (2) these differences are not the result of one coherent set of regulations, but of an interplay of partially contradictory regulations; (3) family as a redistributive principle manifests itself not only in terms of additional benefits to families, but also in terms of particular obligations of families to financially support family members before they are entitled to public support. These aspects have hardly been analysed before and combining them allows a clear evaluation of family-related redistribution.

Trimodal therapy vs. radical cystectomy for muscle-invasive bladder cancer: A Markov microsimulation model

Introduction: Radical cystectomy (RC) is the historic gold standard treatment for muscle-invasive bladder cancer (MIBC), but trimodal therapy (TMT) has emerged as a valid therapeutic option for selected patients. Given that prospective clinical trials have been difficult to perform in this area, our aim was to compare these two primary treatment strategies using decision analytic methods. Method: A two-dimensional Markov microsimulation model was constructed using TreeAge Pro to compare RC and TMT for patients with newly diagnosed MIBC. A comprehensive literature search was used to populate model probabilities and utilities. Our primary outcome was quality-adjusted life expectancy (QALE). Secondary outcomes included crude life expectancy (LE) and bladder cancer recurrences. The simulated patient for our model was an adult with MIBC (pT2-4 N0 M0) who was a candidate for either RC or TMT. Results: A total of 500 000 patients were simulated. TMT resulted in an estimated mean QALE of 7.48 vs. 7.41 for RC. However, the average LE for patients treated with TMT was lower compared with RC (10.20 vs. 10.74 years). A sensitivity analysis evaluating the impact of age showed that younger patients treated with RC had greater QALE and longer LE than those treated with TMT; inverse findings were observed for elderly patients. Overall, 39.4% of patients treated with TMT experienced a bladder recurrence. Conclusions: RC results in a longer LE compared to TMT (0.54 years), but with a lower QALE (-0.07 years). The preferred treatment strategy varied with patient age.