Joanne Gregory introduces a systematic analysis tool which can be used as an aid to ECG interpretation.The tool consists of six steps, which are each explored in detail.

A 3 4 7 -A 7 6 6 incidence functions for cohort models. Tasks were broken down into independent functions and validated against known output values for given inputs. Results: Visualisation aids were crucial in determining parametric form. Negative sigmoidal function shapes typical in survival curves can prevent MNRTs from converging based on survival function inputs -switching to cumulative hazards can rectify this (e.g. worked for Weibull and Log-Logistic joint models): Brent's method will work if it does not. Benchmarking and profiling tools proved vital to identify efficient code (300+ times increase) capable of performing PSA. ConClusions: Analysing and programming competing risk models using different parametric families is not easy but should be done if visualisation plots and fit statistics require it -models should fit the data (not vice-versa).objeCtives: This study provides an overview of methods, and related issues, applied to correct for time-varying covariates, especially exposure, in case of timeto-event outcomes. It also summarizes the advantages of each method and type of studies where each method is applied. Methods: We conducted a literature search in PubMed using relevant keywords including, but not limited to, Cox model, logistic model, time-dependent, time-varying, and exposure. On the results, we applied inclusion/exclusion criteria based on abstract. We also checked the reference lists of included articles to find other relevant papers on methodologies and related issues. Results: 129 papers were identified (79 applied, 33 methods and 17 review papers). Methods were classified into three groups: simple methods (censoring or excluding patients and external data validation), intermediate methods (Cox regression with time-varying covariates), and complex methods (inverse probability of censoring weights, rank preserving structural failure time models, iterative parameter estimation, marginal structural models, simple two-stage methods, structural nested methods, self-controlled case series and Bayesian methods). Most applications used the simpler methods. Issues related to the methods identified included measurement errors in real world databases, defining treatment episodes, simulating real world data, missing data in time-varying exposure analyses, modelling cumulative drug exposure and the use of targeted maximum likelihood estimation for dynamic and static longitudinal marginal structural working models. Some methods work better with observational studies, some are more suited for clinical trials, others are appropriate for rare events. ConClusions: Many different methods for dealing with time-varying covariates exist, but the simpler methods are most commonly used. Comparison and investigation of methods for time-varying exposure is greatly needed, especially the more complex methods The methods need to be explained, promoted, and implemented in standard statistical software. Guidelines on dealing with time-varying exposure are needed to ensure best practice and consistency.objeCtives: He...

Acute coronary syndromes (ACS) are characterized by the rupture of unstable plaque within coronary arteries. Depending on the extent of the ensuing occlusion and myocardial damage, ACS can be classified as unstable angina, non-ST elevation myocardial infarction (MI) and ST elevation MI. The electrocardiogram (ECG) is an invaluable tool in the assessment of patients with ACS. It provides evidence for the location of myocardial ischaemia, injury and infarction and is a crucial factor in the decision to administer thrombolytic agents and other management strategies. The 12-lead ECG is limited to a view of the left ventricle, however it can be extended to provide additional information about the right ventricular and posterior walls. Critical care nurses with ECG skills can contribute to the early detection and management of patients with ACS.

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post-baseline, treated period assessments (thru Cycles 5 and 6) for these items. Forest plots of odds ratios (OR) and associated confidence intervals (CI) from the GEE analyses were generated to characterize the likelihood of a 2-grade categorical improvement (e.g., improvement by 2 categories on the verbal response scale) on individual items for patients treated with MOGA compared to vorinostat during the first 6 cycles of therapy. RESULTS: The likelihood of patients experiencing a 2-grade improvement in skin symptoms was higher for patients treated with MOGA compared to vorinostat (OR > 1.0). Patients treated with MOGA were 62% more likely to observe a 2-grade improvement in burning or stinging skin (OR¼1.62, CI¼1.013-2.591, p<0.05) and 78% more likely to observe this level of improvement in painful skin (OR¼1.78, CI¼1.045-3.021, p<0.05) within 6 cycles of therapy. While not significant, MOGA patients were 60% more likely to report 2grade improvement in side effect bother associated with treatment (OR¼1.60, CI¼0.85-3.009, p<0.05) within 6 cycles of therapy. CONCLUSIONS: These data provide detailed information on the cumulative probability of categorical improvement of individual items on the skin symptoms of Skindex-29 and the toxicity bother items of FACT-G. These results support symptom benefit of MOGA over the course of treatment compared to vorinostat. PCN351 HEALTH-STATE UTILITY VALUES IN DIFFUSE LARGE B-CELL LYMPHOMAOBJECTIVES: Diffuse large B-cell lymphoma (DLBCL) is the commonest non-Hodgkin lymphoma (NHL), accounting for approximately 30-40% of new NHL diagnoses worldwide. Numerous cost-utility analyses have been conducted on DLBCL. Hitherto, however, the utilities applied have been derived from clinical trials of patients either managed with therapies that are no longer used, or who had other NHLs. This study aims to up-date the knowledge base, presenting robust health-state utility values (HSUV) specific to DLBCL. METHODS: 319 DLBCL patients from the UK's population-based Haematological Malignancy Research Network (www.hmrn.org) newly diagnosed 2004-15 who completed one or more EQ-5D-5L questionnaire were included. Two value sets were used to calculate utility: the EQ-5D-5L value set and the EQ-5D-5L crosswalk index value (mapping to EQ-5D-3L values). Descriptive statistics on utility values were summarized by six health states. RESULTS: Utility score varied with disease state, decreasing as the disease progressed: 0.81 (se¼0.016; 1st line treatment), 0.83 (0.012; 1st remission), 0.66 (0.025; 2nd line treatment), 0.81 (0.057; 2nd remission), 0.59 (0.093; 3rd line treatment and beyond) and 0.70 (0.059; 3rd remission and beyond) using the EQ-5D-5L value set; and 0.73 (0.019), 0.76 (0.013), 0.53 (0.065), 0.69 (0.081), 0.53 (0.105) and 0.58 (0.116) respectively, using the crosswalk value set. CONCLUSIONS: Based on "real-world" contemporary high quality data, this is the first study to measure HSUVs that are specific to DLBCL. Different value sets generated different utility values; making comparison work...