tional health status, self-perceived problems, and needs of newly diagnosed cancer patients to determine and plan supportive care strategies. breast, colorectal, head and neck, lung, and prostate carcinoma as well as nonmelanoma of the skin were selected randomly. Patients were interviewed prior to their

A randomized trial of family caregiver support for the home management of older people suffering from moderate to severe progressive irreversible dementia was conducted in an urban center in southern Ontario. Thirty caregivers were allocated to receive the experimental intervention consisting of: caregiver-focused health care, education about dementia and caregiving, assistance with problem solving, regularly scheduled in-home respite, and a self-help family caregiver support group. Thirty control subjects received conventional community nursing care. Before completion of the intervention, 18 (30%) were withdrawn, almost equally from each group. The most frequent reason was long-term institutionalization of the demented relative (n = 10). At baseline, caregivers in both groups were suffering from above-average levels of depression and anxiety. After the six-month intervention period, we found neither experimental nor control group improved in these areas. However, the experimental group showed a clinically important improvement in quality of life, experienced a slightly longer mean time to long-term institutionalization, found the caregiver role less problematic, and had greater satisfaction with nursing care than the control group.

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Objectives: The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes. Methods: Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups' replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R 2 ). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed. Results: Seven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed. Conclusions: Transparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results.