Review of statistical methods for disease mapping

Cancer poses an undeniable burden to the health and wellbeing of the Australian community. In a recent report commissioned by the Australian Institute for Health and Welfare(AIHW, 2010), one in every two Australians on average will be diagnosed with cancer by the age of 85, making cancer the second leading cause of death in 2007, preceded only by cardiovascular disease. Despite modest decreases in standardised combined cancer mortality over the past few decades, in part due to increased funding and access to screening programs, cancer remains a significant economic burden. In 2010, all cancers accounted for an estimated 19% of the country's total burden of disease, equating to approximately $3:8 billion in direct health system costs (Cancer Council Australia, 2011). Furthermore, there remains established socio-economic and other demographic inequalities in cancer incidence and survival, for example, by indigenous status and rurality. Therefore, in the interests of the nation's health and economic management, there is an immediate need to devise data-driven strategies to not only understand the socio-economic drivers of cancer but also facilitate the implementation of cost-effective resource allocation for cancer management...

Statistical methods for electromyography data and associated problems

This thesis proposes three novel models which extend the statistical methodology for motor unit number estimation, a clinical neurology technique. Motor unit number estimation is important in the treatment of degenerative muscular diseases and, potentially, spinal injury. Additionally, a recent and untested statistic to enable statistical model choice is found to be a practical alternative for larger datasets. The existing methods for dose finding in dual-agent clinical trials are found to be suitable only for designs of modest dimensions. The model choice case-study is the first of its kind containing interesting results using so-called unit information prior distributions.

Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.

Author response: "Missed opportunity related to statistical methods"

"We thank MrGilder for his considered comments and suggestions for alternative analyses of our data. We also appreciate Mr Gilder’s support of our call for larger studies to contribute to the evidence base for preoperative loading with high-carbohydrate fluids..."

Statistical Methods for Accelerated Life Testing with Particular Reference to Resin Impregnated Paper Bushings

Electrical failure of insulation is known to be an extremal random process wherein nominally identical pro-rated specimens of equipment insulation, at constant stress fail at inordinately different times even under laboratory test conditions. In order to be able to estimate the life of power equipment, it is necessary to run long duration ageing experiments under accelerated stresses, to acquire and analyze insulation specific failure data. In the present work, Resin Impregnated Paper (RIP) a relatively new insulation system of choice used in transformer bushings, is taken as an example. The failure data has been processed using proven statistical methods, both graphical and analytical. The physical model governing insulation failure at constant accelerated stress has been assumed to be based on temperature dependent inverse power law model.

Methods of sensory evaluation of quality and application of statistical methods in sensory evaluation problems with special reference to fishery products

The quality of raw and processed fishery products depend on several factors like physiological conditions at the time of capture, morphological differences, rigor mortis, species, rate of icing and subsequent storage conditions. Sensory evaluation is still the most reliable method for evaluation of the freshness of raw processed fishery products. Sophisticated methods like Intelectron fish tester, cell fragility technique and chemical and bacteriological methods like estimation of trimethylamine, hypoxanthine, carbonyl compounds, volatile acid and total bacterial count have no doubt been developed for accessing the spoilage in fish products.

Missing data in randomized clinical trials for weight loss: scope of the problem, state of the field, and performance of statistical methods.

BACKGROUND: Dropouts and missing data are nearly-ubiquitous in obesity randomized controlled trails, threatening validity and generalizability of conclusions. Herein, we meta-analytically evaluate the extent of missing data, the frequency with which various analytic methods are employed to accommodate dropouts, and the performance of multiple statistical methods. METHODOLOGY/PRINCIPAL FINDINGS: We searched PubMed and Cochrane databases (2000-2006) for articles published in English and manually searched bibliographic references. Articles of pharmaceutical randomized controlled trials with weight loss or weight gain prevention as major endpoints were included. Two authors independently reviewed each publication for inclusion. 121 articles met the inclusion criteria. Two authors independently extracted treatment, sample size, drop-out rates, study duration, and statistical method used to handle missing data from all articles and resolved disagreements by consensus. In the meta-analysis, drop-out rates were substantial with the survival (non-dropout) rates being approximated by an exponential decay curve (e(-lambdat)) where lambda was estimated to be .0088 (95% bootstrap confidence interval: .0076 to .0100) and t represents time in weeks. The estimated drop-out rate at 1 year was 37%. Most studies used last observation carried forward as the primary analytic method to handle missing data. We also obtained 12 raw obesity randomized controlled trial datasets for empirical analyses. Analyses of raw randomized controlled trial data suggested that both mixed models and multiple imputation performed well, but that multiple imputation may be more robust when missing data are extensive. CONCLUSION/SIGNIFICANCE: Our analysis offers an equation for predictions of dropout rates useful for future study planning. Our raw data analyses suggests that multiple imputation is better than other methods for handling missing data in obesity randomized controlled trials, followed closely by mixed models. We suggest these methods supplant last observation carried forward as the primary method of analysis.