Bayesian Markov chain Monte Carlo inversion of geophysical data for the purpose of estimating the hydraulic properties of the unsaturated zone

Ground-penetrating radar (GPR) has the potential to provide valuable information on hydrological properties of the vadose zone because of their strong sensitivity to soil water content. In particular, recent evidence has suggested that the stochastic inversion of crosshole GPR data within a coupled geophysical-hydrological framework may allow for effective estimation of subsurface van-Genuchten-Mualem (VGM) parameters and their corresponding uncertainties. An important and still unresolved issue, however, is how to best integrate GPR data into a stochastic inversion in order to estimate the VGM parameters and their uncertainties, thus improving hydrological predictions. Recognizing the importance of this issue, the aim of the research presented in this thesis was to first introduce a fully Bayesian inversion called Markov-chain-Monte-carlo (MCMC) strategy to perform the stochastic inversion of steady-state GPR data to estimate the VGM parameters and their uncertainties. Within this study, the choice of the prior parameter probability distributions from which potential model configurations are drawn and tested against observed data was also investigated. Analysis of both synthetic and field data collected at the Eggborough (UK) site indicates that the geophysical data alone contain valuable information regarding the VGM parameters. However, significantly better results are obtained when these data are combined with a realistic, informative prior. A subsequent study explore in detail the dynamic infiltration case, specifically to what extent time-lapse ZOP GPR data, collected during a forced infiltration experiment at the Arrenaes field site (Denmark), can help to quantify VGM parameters and their uncertainties using the MCMC inversion strategy. The findings indicate that the stochastic inversion of time-lapse GPR data does indeed allow for a substantial refinement in the inferred posterior VGM parameter distributions. In turn, this significantly improves knowledge of the hydraulic properties, which are required to predict hydraulic behaviour. Finally, another aspect that needed to be addressed involved the comparison of time-lapse GPR data collected under different infiltration conditions (i.e., natural loading and forced infiltration conditions) to estimate the VGM parameters using the MCMC inversion strategy. The results show that for the synthetic example, considering data collected during a forced infiltration test helps to better refine soil hydraulic properties compared to data collected under natural infiltration conditions. When investigating data collected at the Arrenaes field site, further complications arised due to model error and showed the importance of also including a rigorous analysis of the propagation of model error with time and depth when considering time-lapse data. Although the efforts in this thesis were focused on GPR data, the corresponding findings are likely to have general applicability to other types of geophysical data and field environments. Moreover, the obtained results allow to have confidence for future developments in integration of geophysical data with stochastic inversions to improve the characterization of the unsaturated zone but also reveal important issues linked with stochastic inversions, namely model errors, that should definitely be addressed in future research.

Regression analysis of spatial data.

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Enzyme replacement therapy with agalsidase alfa in patients with Fabry's disease: an analysis of registry data.

BACKGROUND: We analysed 5-year treatment with agalsidase alfa enzyme replacement therapy in patients with Fabry's disease who were enrolled in the Fabry Outcome Survey observational database (FOS). METHODS: Baseline and 5-year data were available for up to 181 adults (126 men) in FOS. Serial data for cardiac mass and function, renal function, pain, and quality of life were assessed. Safety and sensitivity analyses were done in patients with baseline and at least one relevant follow-up measurement during the 5 years (n=555 and n=475, respectively). FINDINGS: In patients with baseline cardiac hypertrophy, treatment resulted in a sustained reduction in left ventricular mass (LVM) index after 5 years (from 71.4 [SD 22.5] g/m(2.7) to 64.1 [18.7] g/m(2.7), p=0.0111) and a significant increase in midwall fractional shortening (MFS) from 14.3% (2.3) to 16.0% (3.8) after 3 years (p=0.02). In patients without baseline hypertrophy, LVM index and MFS remained stable. Mean yearly fall in estimated glomerular filtration rate versus baseline after 5 years of enzyme replacement therapy was -3.17 mL/min per 1.73 m(2) for men and -0.89 mL/min per 1.73 m(2) for women. Average pain, measured by Brief Pain Inventory score, improved significantly, from 3.7 (2.3) at baseline to 2.5 (2.4) after 5 years (p=0.0023). Quality of life, measured by deviation scores from normal EuroQol values, improved significantly, from -0.24 (0.3) at baseline to -0.17 (0.3) after 5 years (p=0.0483). Findings were confirmed by sensitivity analysis. No unexpected safety concerns were identified. INTERPRETATION: By comparison with historical natural history data for patients with Fabry's disease who were not treated with enzyme replacement therapy, long-term treatment with agalsidase alfa leads to substantial and sustained clinical benefits. FUNDING: Shire Human Genetic Therapies AB.

Adjuvant chemotherapy for localised resectable soft-tissue sarcoma of adults: meta-analysis of individual data

BACKGROUND: Individually, randomised trials have not shown conclusively whether adjuvant chemotherapy benefits adult patients with localised resectable soft-tissue sarcoma.METHODS: A quantitative meta-analysis of updated data from individual patients from all available randomised trials was carried out to assess whether adjuvant chemotherapy improves overall survival, recurrence-free survival, and local and distant recurrence-free intervals (RFI) and whether chemotherapy is differentially effective in patients defined by age, sex, disease status at randomisation, disease site, histology, grade, tumour size, extent of resection, and use of radiotherapy.FINDINGS: 1568 patients from 14 trials of doxorubicin-based adjuvant chemotherapy were included (median follow-up 9.4 years). Hazard ratios of 0.73 (95% CI 0.56-0.94, p = 0.016) for local RFI, 0.70 (0.57-0.85, p = 0.0003) for distant RFI, and 0.75 (0.64-0.87, p = 0.0001) for overall recurrence-free survival, correspond to absolute benefits from adjuvant chemotherapy of 6% (95% CI 1-10), 10% (5-15), and 10% (5-15), respectively, at 10 years. For overall survival the hazard ratio of 0.89 (0.76-1.03) was not significant (p = 0.12), but represents an absolute benefit of 4% (1-9) at 10 years. These results were not affected by prespecified changes in the groups of patients analysed. There was no consistent evidence that the relative effect of adjuvant chemotherapy differed for any subgroup of patients for any endpoint. However, the best evidence of an effect of adjuvant chemotherapy for survival was seen in patients with sarcomas of the extremities.INTERPRETATION: The meta-analysis provides evidence that adjuvant doxorubicin-based chemotherapy significantly improves the time to local and distant recurrence and overall recurrence-free survival. There is a trend towards improved overall survival.

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

Reliability and validity of physiological data obtained within a cycle-run transition test in age-group triathletes.

This study examined the validity and reliability of a sequential "Run-Bike-Run" test (RBR) in age-group triathletes. Eight Olympic distance (OD) specialists (age 30.0 ± 2.0 years, mass 75.6 ± 1.6 kg, run VO2max 63.8 ± 1.9 ml· kg(-1)· min(-1), cycle VO2peak 56.7 ± 5.1 ml· kg(-1)· min(-1)) performed four trials over 10 days. Trial 1 (TRVO2max) was an incremental treadmill running test. Trials 2 and 3 (RBR1 and RBR2) involved: 1) a 7-min run at 15 km· h(-1) (R1) plus a 1-min transition to 2) cycling to fatigue (2 W· kg(-1) body mass then 30 W each 3 min); 3) 10-min cycling at 3 W· kg(-1) (Bsubmax); another 1-min transition and 4) a second 7-min run at 15 km· h(-1) (R2). Trial 4 (TT) was a 30-min cycle - 20-min run time trial. No significant differences in absolute oxygen uptake (VO2), heart rate (HR), or blood lactate concentration ([BLA]) were evidenced between RBR1 and RBR2. For all measured physiological variables, the limits of agreement were similar, and the mean differences were physiologically unimportant, between trials. Low levels of test-retest error (i.e. ICC <0.8, CV<10%) were observed for most (logged) measurements. However [BLA] post R1 (ICC 0.87, CV 25.1%), [BLA] post Bsubmax (ICC 0.99, CV 16.31) and [BLA] post R2 (ICC 0.51, CV 22.9%) were least reliable. These error ranges may help coaches detect real changes in training status over time. Moreover, RBR test variables can be used to predict discipline specific and overall TT performance. Cycle VO2peak, cycle peak power output, and the change between R1 and R2 (deltaR1R2) in [BLA] were most highly related to overall TT distance (r = 0.89, p < 0. 01; r = 0.94, p < 0.02; r = 0.86, p < 0.05, respectively). The percentage of TR VO2max at 15 km· h(-1), and deltaR1R2 HR, were also related to run TT distance (r = -0.83 and 0.86, both p < 0.05).

The prognostic value of health-related quality-of-life data in predicting survival in glioblastoma cancer patients: results from an international randomised phase III EORTC Brain Tumour and Radiation Oncology Groups, and NCIC Clinical Trials Group study.

This is one of the few studies that have explored the value of baseline symptoms and health-related quality of life (HRQOL) in predicting survival in brain cancer patients. Baseline HRQOL scores (from the EORTC QLQ-C30 and the Brain Cancer Module (BN 20)) were examined in 490 newly diagnosed glioblastoma cancer patients for the relationship with overall survival by using Cox proportional hazards regression models. Refined techniques as the bootstrap re-sampling procedure and the computation of C-indexes and R(2)-coefficients were used to try and validate the model. Classical analysis controlled for major clinical prognostic factors selected cognitive functioning (P=0.0001), global health status (P=0.0055) and social functioning (P<0.0001) as statistically significant prognostic factors of survival. However, several issues question the validity of these findings. C-indexes and R(2)-coefficients, which are measures of the predictive ability of the models, did not exhibit major improvements when adding selected or all HRQOL scores to clinical factors. While classical techniques lead to positive results, more refined analyses suggest that baseline HRQOL scores add relatively little to clinical factors to predict survival. These results may have implications for future use of HRQOL as a prognostic factor in cancer patients.

Imputation in data fusion of heterogeneous data sets a model-based numerical experiment

Given the very large amount of data obtained everyday through population surveys, much of the new research again could use this information instead of collecting new samples. Unfortunately, relevant data are often disseminated into different files obtained through different sampling designs. Data fusion is a set of methods used to combine information from different sources into a single dataset. In this article, we are interested in a specific problem: the fusion of two data files, one of which being quite small. We propose a model-based procedure combining a logistic regression with an Expectation-Maximization algorithm. Results show that despite the lack of data, this procedure can perform better than standard matching procedures.