Purpose. To characterize the economic and quality of life burden of diabetic macular edema (DME) in Canadian patients. Patients and Methods. 145 patients with DME were followed for 6 months with monthly telephone interviews and medical chart reviews at months 0, 3, and 6. Visual acuity in the worst-seeing eye was assessed at months 0 and 6. DME-related healthcare costs were determined over 6 months, and vision-related (National Eye Institute Visual Functioning Questionnaire) and generic (EQ-5D) quality of life was assessed at months 0, 3, and 6. Results. Mean age of patients was 63.7 years: 52% were male and 72% had bilateral DME. At baseline, visual acuity was categorized as normal/mild loss for 63.4% of patients, moderate loss for 10.4%, and severe loss/nearly blind for 26.2%. Mean 6-month DME-related costs/patient were as follows: all patients (n = 135), $2,092; normal/mild loss (n = 88), $1,776; moderate loss (n = 13), $1,845; and severe loss/nearly blind (n = 34), $3,007. Composite scores for vision-related quality of life declined with increasing visual acuity loss; generic quality of life scores were highest for moderate loss and lowest for severe loss/nearly blind. Conclusions. DME-related costs in the Canadian healthcare system are substantial. Costs increased and vision-related quality of life declined with increasing visual acuity severity.

Background Patient preference information is increasingly being used to inform decision making; however, further work is required to support the collection of preference information in rare diseases. This study illustrates the use of direct preference elicitation methods to collect preference data from small samples in the context of early decision making to inform the development of a product for the treatment of immunoglobulin A nephropathy. Method An interview-based swing weighting approach was used to elicit preferences from 40 patients in the US and China. Attributes were identified through a background review, expert engagement and patient focus groups. Participants completed a series of tasks that involved ranking, rating and scoring improvements in the attributes to obtain attribute swing weights and partial value functions. The preference results were then incorporated into a benefit-risk assessment simulation tool. Results Participants placed the greatest value on avoiding end-stage renal/kidney disease. Similar weight was given to shortterm quality-of-life improvements and avoiding infections. Treatment burden (number of vaccinations) received the least weight. Heterogeneity in preferences was also observed. Consistency tests did not identify statistically significant variation in preferences, and qualitative data suggested that the elicitation exercise was sensitive to participants' interpretation of attributes and that participants were able to express their preferences. Conclusion Direct preference elicitation methods can be used to collect preference data from small samples. Further work should continue to test the validity of the estimate generated by such methods.

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model developed for national decision-making. We replicated an existing PLS model of this type to evaluate the advantages and disadvantages of different software. Methods: The model was originally created for a NICE clinical guideline on hyperphosphataemia in chronic kidney disease (CG157). It was built in visual basic for applications (VBA). PLS was chosen because it allowed the model to translate continuous biochemical measures from trials into effects on patient-relevant outcomes. We replicated the VBA model in Simul8 ® , a dedicated simulation environment, to explore differences in build-time, results, run-time and usability. Results: The Simul8 ® implementation was slower than the original VBA model when simulating 8 cohorts of 10,000 patients (approximately 12 minutes versus 7.5 minutes). The model structure required the use of multiple patient-level variables ('labels') in Simul8 ® , which are known to affect run-time. When event-logs were recorded for debugging, both VBA and Simul8 ® took considerably longer and Simul8 ® crashed frequently. Model validation was challenging due to the stochastic nature of PLS. When potentially non-trivial differences emerged between implementations, it was difficult to determine whether these were due to technical errors, Monte-Carlo error, or bias from different pseudo-random number generators, an area in which VBA is known to perform poorly. Object-oriented VBA was difficult for new users to grasp and coding errors were difficult to identify. The Simul8 ® interface allowed for patients' routes through the model to be easily visualised and tracked; however, some simple operations (e.g. lookups across categories) required convoluted coding. Conclusions: It is not clear that dedicated simulation software provides meaningful benefits over a generic scripting language for PLS. We are extending this project to encompass further replications in R and discretely integrated condition event simulation (DICE).

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model developed for national decision-making. We replicated an existing PLS model of this type to evaluate the advantages and disadvantages of different software. Methods: The model was originally created for a NICE clinical guideline on hyperphosphataemia in chronic kidney disease (CG157). It was built in visual basic for applications (VBA). PLS was chosen because it allowed the model to translate continuous biochemical measures from trials into effects on patient-relevant outcomes. We replicated the VBA model in Simul8 ® , a dedicated simulation environment, to explore differences in build-time, results, run-time and usability. Results: The Simul8 ® implementation was slower than the original VBA model when simulating 8 cohorts of 10,000 patients (approximately 12 minutes versus 7.5 minutes). The model structure required the use of multiple patient-level variables ('labels') in Simul8 ® , which are known to affect run-time. When event-logs were recorded for debugging, both VBA and Simul8 ® took considerably longer and Simul8 ® crashed frequently. Model validation was challenging due to the stochastic nature of PLS. When potentially non-trivial differences emerged between implementations, it was difficult to determine whether these were due to technical errors, Monte-Carlo error, or bias from different pseudo-random number generators, an area in which VBA is known to perform poorly. Object-oriented VBA was difficult for new users to grasp and coding errors were difficult to identify. The Simul8 ® interface allowed for patients' routes through the model to be easily visualised and tracked; however, some simple operations (e.g. lookups across categories) required convoluted coding. Conclusions: It is not clear that dedicated simulation software provides meaningful benefits over a generic scripting language for PLS. We are extending this project to encompass further replications in R and discretely integrated condition event simulation (DICE).

A283or previous use of inhaled anticholinergics was not associated with a significant increased risk of AACG (active use: adjusted OR 1.32, 95% CI 0.83-2.10; non-active use: adjusted OR 1.13, 95% CI 0.72-1.75) compared to non-use, adjusting for use of other medications related to AACG. ConClusions: A relationship between the use of inhaled anticholinergics and the risk of AACG was not observed in this COPD population. However, the findings are limited due to the small sample size of the events.