Simulating study data to support expected value of sample information calculations : a tutorial

The Expected Value of Sample Information (EVSI) can be used to prioritise avenues for future research and design studies that support medical decision making and offer value for money spent. EVSI is calculated based on three key elements. Two of these, a probabilistic model-based economic evaluation and updating model uncertainty based on simulated data, have been frequently discussed in the literature. By contrast, the third element, simulating data from the proposed studies, has received little attention. This tutorial contributes to bridging this gap by providing a step-by-step guide to simulating study data for EVSI calculations. We discuss a general purpose algorithm for simulating data and demonstrate its use to simulate three different outcome types. We then discuss how to induce correlations in the generated data, how to adjust for common issues in study implementation such as missingness and censoring and how individual patient data from previous studies can be leveraged to undertake EVSI calculations. For all examples, we provide comprehensive code written in the R language and, where possible, Excel spreadsheets in the supplementary materials. This tutorial facilitates practical EVSI calculations and allows EVSI to be used to prioritise research and design studies

Risk assessment and management of new and existing chemicals

An evaluation was made of the recently developed risk assessment methodologies for new and existing chemicals in the European Communities. The evaluation also included the methodologies to prioritize chemicals and procedures for risk management, i.e., the (draft) guidance document for the development of strategies for risk reduction. The way in which chemicals are prioritized is accepted with only very few comments. Clear progress has been made in the development and harmonization of risk assessment methodologies and the application of estimation methodologies. Nevertheless, improvements are necessary for the estimation of consumer and occupational exposure, the derivation, use and transparency of assessment factors for chemicals and classes of chemicals based on the mode of toxic action, environmental exposure models and their validation and relation with monitoring data. As far as risk management is concerned it was recommended to improve the integration of the myriad of directives and regulations, to clarify definitions, to provide clear guidance on the determination and weighing of advantages and implications of risk reduction measures and to develop tools, including voluntary agreements, to speed up the slow chemical-by-chemical approach

Factors affecting outcomes following pelvic exenteration for locally recurrent rectal cancer

Background: Pelvic exenteration for locally recurrent rectal cancer (LRRC) is associated with variable outcomes, with the majority of data from single-centre series. This study analysed data from an international collaboration to determine robust parameters that could inform clinical decision-making. Methods: Anonymized data on patients who had pelvic exenteration for LRRC between 2004 and 2014 were accrued from 27 specialist centres. The primary endpoint was survival. The impact of resection margin, bone resection, node status and use of neoadjuvant therapy (before exenteration) was assessed. Results: Of 1184 patients, 614 (51\ub79 per cent) had neoadjuvant therapy. A clear resection margin (R0 resection) was achieved in 55\ub74 per cent of operations. Twenty-one patients (1\ub78 per cent) died within 30 days and 380 (32\ub71 per cent) experienced a major complication. Median overall survival was 36 months following R0 resection, 27 months after R1 resection and 16 months following R2 resection (P < 0\ub7001). Patients who received neoadjuvant therapy had more postoperative complications (unadjusted odds ratio (OR) 1\ub753), readmissions (unadjusted OR 2\ub733) and radiological reinterventions (unadjusted OR 2\ub712). Three-year survival rates were 48\ub71 per cent, 33\ub79 per cent and 15 per cent respectively. Bone resection (when required) was associated with a longer median survival (36 versus 29 months; P < 0\ub7001). Node-positive patients had a shorter median overall survival than those with node-negative disease (22 versus 29 months respectively). Multivariable analysis identified margin status and bone resection as significant determinants of long-term survival. Conclusion: Negative margins and bone resection (where needed) were identified as the most important factors influencing overall survival. Neoadjuvant therapy before pelvic exenteration did not affect survival, but was associated with higher rates of readmission, complications and radiological reintervention