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Monitoring flood defence condition is a critical part of the effective management of flood defence infrastructure. In this study, the use of remotely sensed data to identify indicators of weakness, deterioration, and damage is explored. Such indicators are often tell‐tale signs of processes that can lead to flood defence failure and breaching. Sources of data and the techniques to analyse those data are discussed and a framework is developed that links data sources to indicators of flood defence performance. A range of examples are presented that highlight both the value and limitations of bringing together multiple sources of data such as Light Detection and Ranging and historical mapping to supplement visual inspection assessments. The paper concludes that nationally available sources of remotely sensed data can be used to supplement existing visual condition inspection procedures to help prioritise and programme maintenance and repair work. [ABSTRACT FROM AUTHOR]

Aspirin inhibits the enzyme cyclooxygenase (Cox), and there is a significant body of epidemiological evidence demonstrating that regular aspirin use is associated with a decreased incidence of developing cancer. Interest focussed on selective Cox-2 inhibitors both as cancer prevention agents and as therapeutic agents in patients with proven malignancy until concerns were raised about their toxicity profile. Aspirin has several additional mechanisms of action that may contribute to its anti-cancer effect. It also influences cellular processes such as apoptosis and angiogenesis that are crucial for the development and growth of malignancies. Evidence suggests that these effects can occur through Cox-independent pathways questioning the rationale of focussing on Cox-2 inhibition alone as an anti-cancer strategy. Randomised studies with aspirin primarily designed to prevent cardiovascular disease have demonstrated a reduction in cancer deaths with long-term follow-up. Concerns about toxicity, particularly serious haemorrhage, have limited the use of aspirin as a cancer prevention agent, but recent epidemiological evidence demonstrating regular aspirin use after a diagnosis of cancer improves outcomes suggests that it may have a role in the adjuvant setting where the risk:benefit ratio will be different. [ABSTRACT FROM AUTHOR]

The article discusses a study which examined the effect of adjuvant chemotherapy on patients with operable adjuvant non-small-cell lung cancer. Study authors conducted two comprehensive systematic reviews and meta-analysis of randomized trials done on or after January 1, 1965. The randomized trials selected were those that compared surgery plus adjuvant chemotherapy versus surgery alone, surgery plus adjuvant radiotherapy and chemotherapy versus surgery plus adjuvant radiotherapy. The study concluded that adjuvant chemotherapy following surgery improves patient survival, regardless of whether chemotherapy was adjuvant to surgery alone or surgery plus radiotherapy.

Abstract Background Clinical researchers have often preferred to use a fixed effects model for the primary interpretation of a meta-analysis. Heterogeneity is usually assessed via the well known Q and I2 statistics, along with the random effects estimate they imply. In recent years, alternative methods for quantifying heterogeneity have been proposed, that are based on a 'generalised' Q statistic. Methods We review 18 IPD meta-analyses of RCTs into treatments for cancer, in order to quantify the amount of heterogeneity present and also to discuss practical methods for explaining heterogeneity. Results Differing results were obtained when the standard Q and I2 statistics were used to test for the presence of heterogeneity. The two meta-analyses with the largest amount of heterogeneity were investigated further, and on inspection the straightforward application of a random effects model was not deemed appropriate. Compared to the standard Q statistic, the generalised Q statistic provided a more accurate platform for estimating the amount of heterogeneity in the 18 meta-analyses. Conclusions Explaining heterogeneity via the pre-specification of trial subgroups, graphical diagnostic tools and sensitivity analyses produced a more desirable outcome than an automatic application of the random effects model. Generalised Q statistic methods for quantifying and adjusting for heterogeneity should be incorporated as standard into statistical software. Software is provided to help achieve this aim.

Abstract Background In systematic reviews and meta-analyses, time-to-event outcomes are most appropriately analysed using hazard ratios (HRs). In the absence of individual patient data (IPD), methods are available to obtain HRs and/or associated statistics by carefully manipulating published or other summary data. Awareness and adoption of these methods is somewhat limited, perhaps because they are published in the statistical literature using statistical notation. Methods This paper aims to 'translate' the methods for estimating a HR and associated statistics from published time-to-event-analyses into less statistical and more practical guidance and provide a corresponding, easy-to-use calculations spreadsheet, to facilitate the computational aspects. Results A wider audience should be able to understand published time-to-event data in individual trial reports and use it more appropriately in meta-analysis. When faced with particular circumstances, readers can refer to the relevant sections of the paper. The spreadsheet can be used to assist them in carrying out the calculations. Conclusion The methods cannot circumvent the potential biases associated with relying on published data for systematic reviews and meta-analysis. However, this practical guide should improve the quality of the analysis and subsequent interpretation of systematic reviews and meta-analyses that include time-to-event outcomes.