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ISCB 2014 Vienna, Austria • Abstracts - Oral Presentations 29Monday, 25th August 2014 • 14:00-15:30 Monday25thAugustTuesday26thAugustThursday28thAugustAuthorIndexPostersWednesday27thAugustSunday24thAugust allowing heterogeneity in predefined gene sets and implemented in an R package. Statistical properties of this approach have been studied through simulations. Association between abundance of genes selected in step 1, immune responses at w16 and viral replication after ART inter- ruption was analysed in step 2 using sparse-Partial Least Square. Results: TcGSA simulations showed good statistical properties (type I and type II errors). In DALIA, although the vaccine elicited strong immunologi- cal responses, no differential expression was found with a gene-by-gene analysis. Using TcGSA, we found 69 genesets out of 260 that varied sig- nificantly during vaccination. In step 2, we show relationships between HIV-specific responses, gene expression and viral replication. Conclusions: The new proposed approach allowed in this example to detect relevant genesets associated with the immune response and viral dynamics.   C12.4 Assessing vaccine effectiveness using observational data in the presence of hidden confounders LR Rodgers1 , N Lin1 , W Henley1 1 University of Exeter, Exeter, United Kingdom   Electronic healthcare databases, such as the Clinical Practice Datalink, have the potential to provide a wealth of information on vaccine efficacy. The problem with using observational data is that the lack of randomisa- tion can lead to bias in estimates of treatment effect due to hidden con- founders. A novel quasi-experimental design, the prior event rate ratio, in- corporates information from a period prior to treatment. In these methods the ratio of period before and after treatment reflects the combined effect of known and unknown confounders. These novel designs along with tra- ditional models are applied to an analysis of vaccine effectiveness using observational data. Our approach is to design a simulation study along- side the data analysis to test the validity of model assumptions. We illustrate the methods using the influenza vaccination. The efficacy of this vaccination in the elderly, particularly due to lack of randomised controlled trials, is a subject of debate. The quasi-experimental methods and established models are applied to a dataset of antibiotic prescriptions before and after an influenza vaccination in elderly patients. The simula- tion study is designed to replicate key features of the motivating data set. Confounders, continuous and binary, are generated to test the impact of imbalance between treatment and control groups and the influence study period on treatment effect estimates.The sensitivity of the methods to the effects of hidden confounders is explored. Of particular interest is the im- pact of subgroups within the data, a feature in studies of the elderly due to the potential for immune senescence. C12.5 Modeling reporting delays for outbreak detection of infectious diseases A Noufaily1 , Y Weldeselassie1 , P Farrington1 , D Enki2 , P Garthwaite1 , N Andrews3 , A Charlett3 1 The Open University, Milton Keynes, United Kingdom, 2 Plymouth University, Plymouth, United Kingdom, 3 Public Health England, London, United Kingdom The delay that necessarily occurs between the emergence of symptoms and the identification of the cause of those symptoms affects the timeli- ness of detection of emerging outbreaks of infectious diseases, and hence the ability to take preventive action. We propose a new method to anal- yse reporting delays using a continuous time spline-based model for the hazard, along with an associated proportional hazards model. This allows analysis of both long and short delays. The delay distributions for labo- ratory-based surveillance data from the UK are found to have extremely long tails, the hazard at longer delays being roughly constant, suggestive of a memoryless process, though some laboratories appear to stop report- ing after a certain delay. We use these findings to inform outbreak detec- tion of infectious diseases based on laboratory reports.

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