Quantifying simulator discrepancy in discrete-time dynamical simulators

When making predictions with complex simulators it can be important to quantify the various sources of uncertainty. Errors in the structural specification of the simulator, for example due to missing processes or incorrect mathematical specification, can be a major source of uncertainty, but are often ignored. We introduce a methodology for inferring the discrepancy between the simulator and the system in discrete-time dynamical simulators. We assume a structural form for the discrepancy function, and show how to infer the maximum likelihood parameter estimates using a particle filter embedded within a Monte Carlo expectation maximization (MCEM) algorithm. We illustrate the method on a conceptual rainfall runoff simulator (logSPM) used to model the Abercrombie catchment in Australia. We assess the simulator and discrepancy model on the basis of their predictive performance using proper scoring rules

Simulation sample sizes for Monte Carlo partial EVPI calculations

Partial expected value of perfect information (EVPI) quantifies the value of removing uncertainty about unknown parameters in a decision model. EVPIs can be computed via Monte Carlo methods. An outer loop samples values of the parameters of interest, and an inner loop samples the remaining parameters from their conditional distribution. This nested Monte Carlo approach can result in biased estimates if small numbers of inner samples are used and can require a large number of model runs for accurate partial EVPI estimates. We present a simple algorithm to estimate the EVPI bias and confidence interval width for a specified number of inner and outer samples. The algorithm uses a relatively small number of model runs (we suggest approximately 600), is quick to compute, and can help determine how many outer and inner iterations are needed for a desired level of accuracy. We test our algorithm using three case studies. (C) 2010 Elsevier B.V. All rights reserved

A web-based tool for eliciting probability distributions from experts

We present a web-based probability distribution elicitation tool: The MATCH Uncertainty Elicitation Tool. The Tool is designed to help elicit probability distributions about uncertain model parameters from experts, in situations where suitable data is either unavailable or sparse. The Tool is free to use, and offers five different techniques for eliciting univariate probability distributions. A key feature of the Tool is that users can log in from different sites and view and interact with the same graphical displays, so that expert elicitation sessions can be conducted remotely (in conjunction with tele- or videoconferencing). This will make probability elicitation easier in situations where it is difficult to interview experts in person. Even when conducting elicitation remotely, interviewers will be able to follow good elicitation practice, advise the experts, and provide instantaneous feedback and assistance

Early high flow nasal cannula therapy in bronchiolitis, a prospective randomised control trial (protocol): A Paediatric Acute Respiratory Intervention Study (PARIS)

Background Bronchiolitis imposes the largest health care burden on non-elective paediatric hospital admissions worldwide, with up to 15 % of cases requiring admission to intensive care. A number of previous studies have failed to show benefit of pharmaceutical treatment in respect to length of stay, reduction in PICU admission rates or intubation frequency. The early use of non-invasive respiratory support devices in less intensive scenarios to facilitate earlier respiratory support may have an impact on outcome by avoiding progression of the disease process. High Flow Nasal Cannula (HFNC) therapy has emerged as a new method to provide humidified air flow to deliver a non-invasive form of positive pressure support with titratable oxygen fraction. There is a lack of high-grade evidence on use of HFNC therapy in bronchiolitis. Methods/Design Prospective multi-centre randomised trial comparing standard treatment (standard subnasal oxygen) and High Flow Nasal Cannula therapy in infants with bronchiolitis admitted to 17 hospitals emergency departments and wards in Australia and New Zealand, including 12 non-tertiary regional/metropolitan and 5 tertiary centres. The primary outcome is treatment failure; defined as meeting three out of four pre-specified failure criteria requiring escalation of treatment or higher level of care; i) heart rate remains unchanged or increased compared to admission/enrolment observations, ii) respiratory rate remains unchanged or increased compared to admission/enrolment observations, iii) oxygen requirement in HFNC therapy arm exceeds FiO2 ≥ 40 % to maintain SpO2 ≥ 92 % (or ≥94 %) or oxygen requirement in standard subnasal oxygen therapy arm exceeds >2L/min to maintain SpO2 ≥ 92 % (or ≥94 %), and iv) hospital internal Early Warning Tool calls for medical review and escalation of care. Secondary outcomes include transfer to tertiary institution, admission to intensive care, length of stay, length of oxygen treatment, need for non-invasive/invasive ventilation, intubation, adverse events, and cost. Discussion This large multicenter randomised trial will allow the definitive assessment of the efficacy of HFNC therapy as compared to standard subnasal oxygen in the treatment of bronchiolitis

Quantifying simulator discrepancy in discrete-time dynamical simulators

When making predictions with complex simulators it can be important to quantify the various sources of uncertainty. Errors in the structural specification of the simulator, for example due to missing processes or incorrect mathematical specification, can be a major source of uncertainty, but are often ignored. We introduce a methodology for inferring the discrepancy between the simulator and the system in discrete-time dynamical simulators. We assume a structural form for the discrepancy function, and show how to infer the maximum likelihood parameter estimates using a particle filter embedded within a Monte Carlo expectation maximization (MCEM) algorithm. We illustrate the method on a conceptual rainfall runoff simulator (logSPM) used to model the Abercrombie catchment in Australia. We assess the simulator and discrepancy model on the basis of their predictive performance using proper scoring rules

Improving ART programme retention and viral suppression are key to maximising impact of treatment as prevention - a modelling study.

BACKGROUND: UNAIDS calls for fewer than 500,000 new HIV infections/year by 2020, with treatment-as-prevention being a key part of their strategy for achieving the target. A better understanding of the contribution to transmission of people at different stages of the care pathway can help focus intervention services at populations where they may have the greatest effect. We investigate this using Uganda as a case study. METHODS: An individual-based HIV/ART model was fitted using history matching. 100 model fits were generated to account for uncertainties in sexual behaviour, HIV epidemiology, and ART coverage up to 2015 in Uganda. A number of different ART scale-up intervention scenarios were simulated between 2016 and 2030. The incidence and proportion of transmission over time from people with primary infection, post-primary ART-naïve infection, and people currently or previously on ART was calculated. RESULTS: In all scenarios, the proportion of transmission by ART-naïve people decreases, from 70% (61%-79%) in 2015 to between 23% (15%-40%) and 47% (35%-61%) in 2030. The proportion of transmission by people on ART increases from 7.8% (3.5%-13%) to between 14% (7.0%-24%) and 38% (21%-55%). The proportion of transmission by ART dropouts increases from 22% (15%-33%) to between 31% (23%-43%) and 56% (43%-70%). CONCLUSIONS: People who are currently or previously on ART are likely to play an increasingly large role in transmission as ART coverage increases in Uganda. Improving retention on ART, and ensuring that people on ART remain virally suppressed, will be key in reducing HIV incidence in Uganda

Calibration of stochastic computer simulators using likelihood emulation

We calibrate a stochastic computer simulation model of ‘moderate’ computational expense. The simulator is an imperfect representation of reality, and we recognise this discrepancy to ensure a reliable calibration. The calibration model combines a Gaussian process emulator of the likelihood surface with importance sampling. Changing the discrepancy specification changes only the importance weights, which lets us investigate sensitivity to different discrepancy specifications at little computational cost. We present a case study of a natural history model that has been used to characterise UK bowel cancer incidence. Data sets and computer code are provided as supplementary material

Traumatic brain injury in young children with isolated scalp haematoma

Objective Despite high-quality paediatric head trauma clinical prediction rules, the management of otherwise asymptomatic young children with scalp haematomas (SH) can be difficult. We determined the risk of intracranial injury when SH is the only predictor variable using definitions from the Pediatric Emergency Care Applied Research Network (PECARN) and Children’s Head Injury Algorithm for the Prediction of Important Clinical Events (CHALICE) head trauma rules.Design Planned secondary analysis of a multicentre prospective observational study.Setting Ten emergency departments in Australia and New Zealand.Patients Children 5 cm haematoma in any region of the head) rule-based definition of isolated SH in both childre

A Randomized Trial of High-Flow Oxygen Therapy in Infants with Bronchiolitis

BACKGROUND: High-flow oxygen therapy through a nasal cannula has been increasingly used in infants with bronchiolitis, despite limited high-quality evidence of its efficacy. The efficacy of high-flow oxygen therapy through a nasal cannula in settings other than intensive care units (ICUs) is unclear. METHODS: In this multicenter, randomized, controlled trial, we assigned infants younger than 12 months of age who had bronchiolitis and a need for supplemental oxygen therapy to receive either high-flow oxygen therapy (high-flow group) or standard oxygen therapy (standard-therapy group). Infants in the standard-therapy group could receive rescue high-flow oxygen therapy if their condition met criteria for treatment failure. The primary outcome was escalation of care due to treatment failure (defined as meeting ≥3 of 4 clinical criteria: persistent tachycardia, tachypnea, hypoxemia, and medical review triggered by a hospital early-warning tool). Secondary outcomes included duration of hospital stay, duration of oxygen therapy, and rates of transfer to a tertiary hospital, ICU admission, intubation, and adverse events. RESULTS: The analyses included 1472 patients. The percentage of infants receiving escalation of care was 12% (87 of 739 infants) in the high-flow group, as compared with 23% (167 of 733) in the standard-therapy group (risk difference, −11 percentage points; 95% confidence interval, −15 to −7; P<0.001). No significant differences were observed in the duration of hospital stay or the duration of oxygen therapy. In each group, one case of pneumothorax (<1% of infants) occurred. Among the 167 infants in the standard-therapy group who had treatment failure, 102 (61%) had a response to high-flow rescue therapy. CONCLUSIONS: Among infants with bronchiolitis who were treated outside an ICU, those who received high-flow oxygen therapy had significantly lower rates of escalation of care due to treatment failure than those in the group that received standard oxygen therapy. (Funded by the National Health and Medical Research Council and others; Australian and New Zealand Clinical Trials Registry number, ACTRN12613000388718.