Forecasts of non-Gaussian parameter spaces using Box-Cox transformations

Forecasts of statistical constraints on model parameters using the Fisher matrix abound in many fields of astrophysics. The Fisher matrix formalism involves the assumption of Gaussianity in parameter space and hence fails to predict complex features of posterior probability distributions. Combining the standard Fisher matrix with Box-Cox transformations, we propose a novel method that accurately predicts arbitrary posterior shapes. The Box-Cox transformations are applied to parameter space to render it approximately multivariate Gaussian, performing the Fisher matrix calculation on the transformed parameters. We demonstrate that, after the Box-Cox parameters have been determined from an initial likelihood evaluation, the method correctly predicts changes in the posterior when varying various parameters of the experimental setup and the data analysis, with marginally higher computational cost than a standard Fisher matrix calculation. We apply the Box-Cox-Fisher formalism to forecast cosmological parameter constraints by future weak gravitational lensing surveys. The characteristic non-linear degeneracy between matter density parameter and normalisation of matter density fluctuations is reproduced for several cases, and the capabilities of breaking this degeneracy by weak lensing three-point statistics is investigated. Possible applications of Box-Cox transformations of posterior distributions are discussed, including the prospects for performing statistical data analysis steps in the transformed Gaussianised parameter space.Comment: 14 pages, 7 figures; minor changes to match version published in MNRA

Hepatic resection for metastatic colorectal adenocarcinoma: A proposal of a prognostic scoring system

Background: Hepatic resection for metastatic colorectal cancer provides excellent longterm results in a substantial proportion of patients. Although various prognostic risk factors have been identified, there has been no dependable staging or prognostic scoring system for metastatic hepatic tumors. Study Design: Various clinical and pathologic risk factors were examined in 305 consecutive patients who underwent primary hepatic resections for metastatic colorectal cancer. Survival rates were estimated by the Cox proportional hazards model using the equation: S(t) = [S(o)(t)](exp(R - R(o))), where S(o)(t) is the survival rate of patients with none of the identified risk factors and R(o) = 0. Results: Preliminary multivariate analysis revealed that independently significant negative prognosticators were: (1) positive surgical margins, (2) extrahepatic tumor involvement including the lymph node(s), (3) tumor number of three or more, (4) bilobar tumors, and (5) time from treatment of the primary tumor to hepatic recurrence of 30 months or less. Because the survival rates of the 62 patients with positive margins or extrahepatic tumor were uniformly very poor, multivariate analysis was repeated in the remaining 243 patients who did not have these lethal risk factors. The reanalysis revealed that independently significant poor prognosticators were: (1) tumor number of three or more, (2) tumor size greater than 8 cm, (3) time to hepatic recurrence of 30 months or less, and (4) bilobar tumors. Risk scores (R) for tumor recurrence of the culled cohort (n = 243) were calculated by summation of coefficients from the multivariate analysis and were divided into five groups: grade 1, no risk factors (R = 0); grade 2, one risk factor (R = 0.3 to 0.7); grade 3, two risk factors (R = 0.7 to 1.1); grade 4, three risk factors (R = 1.2 to 1.6); and grade 5, four risk factors (R > 1.6). Grade 6 consisted of the 62 culled patients with positive margins or extrahepatic tumor. Kaplan-Meier and Cox proportional hazards estimated 5-year survival rates of grade 1 to 6 patients were 48.3% and 48.3%, 36.6% and 33.7%, 19.9% and 17.9%, 11.9% and 6.4%, 0% and 1.1%, and 0% and 0%, respectively (p < 0.0001). Conclusions: The proposed risk-score grading predicted the survival differences extremely well. Estimated survival as determined by the Cox proportional hazards model was similar to that determined by the Kaplan-Meier method. Verification and further improvements of the proposed system are awaited by other centers or international collaborative studies

Circular RNAs in Clear Cell Renal Cell Carcinoma: Their Microarray-Based Identification, Analytical Validation, and Potential Use in a Clinico-Genomic Model to Improve Prognostic Accuracy

Circular RNAs (circRNAs) may act as novel cancer biomarkers. However, a genome-wide evaluation of circRNAs in clear cell renal cell carcinoma (ccRCC) has yet to be conducted. Therefore, the objective of this study was to identify and validate circRNAs in ccRCC tissue with a focus to evaluate their potential as prognostic biomarkers. A genome-wide identification of circRNAs in total RNA extracted from ccRCC tissue samples was performed using microarray analysis. Three relevant differentially expressed circRNAs were selected (circEGLN3, circNOX4, and circRHOBTB3), their circular nature was experimentally confirmed, and their expression-along with that of their linear counterparts-was measured in 99 malignant and 85 adjacent normal tissue samples using specifically established RT-qPCR assays. The capacity of circRNAs to discriminate between malignant and adjacent normal tissue samples and their prognostic potential (with the endpoints cancer-specific, recurrence-free, and overall survival) after surgery were estimated by C-statistics, Kaplan-Meier method, univariate and multivariate Cox regression analysis, decision curve analysis, and Akaike and Bayesian information criteria. CircEGLN3 discriminated malignant from normal tissue with 97% accuracy. We generated a prognostic for the three endpoints by multivariate Cox regression analysis that included circEGLN3, circRHOBT3 and linRHOBTB3. The predictive outcome accuracy of the clinical models based on clinicopathological factors was improved in combination with this circRNA-based signature. Bootstrapping as well as Akaike and Bayesian information criteria confirmed the statistical significance and robustness of the combined models. Limitations of this study include its retrospective nature and the lack of external validation. The study demonstrated the promising potential of circRNAs as diagnostic and particularly prognostic biomarkers in ccRCC patients

Determinants of healthcare utilisation and predictors of outcome in colorectal cancer patients from Northern Iran

We aimed to assess healthcare utilisation (HU), its determinants, as well as its relationship with survival in colorectal cancer (CRC) patients. This study was conducted on incident CRC cases from Northern Iran. Information on HU was collected using a valid questionnaire, considering eight diagnostic and four therapeutic services. The results were categorised as good and poor HU. Multivariate logistic regression analysis was used to assess the relationship between HU and other variables. Cox regression analysis was performed to determine major predictors of survival. In total, 227 new cases of CRC were enrolled. HU could be assessed in 218 subjects (96). Living in rural areas was the strongest variable related to poor HU (adjusted OR, odds ratio=2.65; CI, confidence interval: 1.30-5.40). The median survival time was 40.5months. The 1-, 3- and 5-year survival rates were 71, 52 and 44 respectively. Cox regression analysis showed a significant lower survival rate in patients with poor HU (HR=2.3; CI: 1.46-3.64). HU was an independent predictor of survival in our CRC patients. Patients' place of residence was a significant determinant of HU. Regarding its effects on patients' outcome, HU and its determinants should be considered in designing CRC controlling programmes in our region and similar high-risk populations. © 2016 John Wiley & Sons Ltd

Peri-prostatic fat volume measurement as a predictive tool for castration resistance in advanced prostate cancer

Background: Obesity and aggressive prostate cancer (PC) may be linked, but how local peri-prostatic fat relates to tumour response following androgen deprivation therapy (ADT) is unknown. Objective: To test if peri-prostatic fat volume (PPFV) predicts tumour response to ADT. Design, setting, and participants: We performed a retrospective study on consecutive patients receiving primary ADT. From staging pelvic magnetic resonance imaging scans, the PPFV was quantified with OsirixX 6.5 imaging software. Statistical (univariate and multivariate) analysis were performed using R Version 3.2.1. Results and limitations: Of 224 consecutive patients, 61 with advanced (≥T3 or N1 or M1) disease had (3-mm high resolution axial sections) pelvic magnetic resonance imaging scan before ADT. Median age = 75 yr; median PPFV = 24.8 cm3 (range, 7.4–139.4 cm3). PPFV was significantly higher in patients who developed castration resistant prostate cancer (CRPC; n = 31), with a median of 37.9 cm3 compared with 16.1 cm3 (p &lt; 0.0001, Wilcoxon rank sum test) in patients who showed sustained response to ADT (n = 30). Multivariate analysis using Cox proportional hazards models were performed controlling for known predictors of CRPC. PPFV was shown to be independent of all included factors, and the most significant predictor of time to CRPC. Using our multivariate model consisting of all known factors prior to ADT, PPFV significantly improved the area under the curve of the multivariate models receiver operating characteristic analysis. The main study limitation is a relatively small cohort to account for multiple variables, necessitating a future large-scale prospective analysis of PPFV in advanced PC. Conclusions: PPFV quantification in patients with advanced PC predicts tumour response to ADT

Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes

We present a non-parametric prognostic framework for individualized event prediction based on joint modeling of both longitudinal and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of longitudinal signals and a Cox model to map time-to-event data with longitudinal data modeled through the MGCP. Taking advantage of the unique structure imposed by convolved processes, we provide a variational inference framework to simultaneously estimate parameters in the joint MGCP-Cox model. This significantly reduces computational complexity and safeguards against model overfitting. Experiments on synthetic and real world data show that the proposed framework outperforms state-of-the art approaches built on two-stage inference and strong parametric assumptions

A note on the risk management of CDOs

The purpose of this note is to describe a risk management procedure applicable to options on large credit portfolios such as CDO tranches on iTraxx or CDX. Credit spread risk is dynamically hedged using single name defaultable claims such as CDS while default risk is kept under control thanks to diversification. The proposed risk management approach mixes ideas from finance and insurance and departs from standard approaches used in incomplete markets such as mean-variance hedging or expected utility maximisation. In order to ease the analysis and the exposure, default dates follow a multivariate Cox process.CDOs; default risk; credit spread risk; dynamic hedging; diversification, large portfolios; incomplete markets; Cox process; doubly stochastic Poisson process

Five-year mortality and related prognostic factors after inpatient stroke rehabilitation : A European multi-centre study

Objective: To determine 5-year mortality and its association with baseline characteristics and functional status 6 months post-stroke for patients who received inpatient rehabilitation. Design: A prospective rehabilitation-based cohort study. Subjects: A total of 532 consecutive stroke patients from 4 European rehabilitation centres. Methods: Predictors were recorded on admission. Barthel Index was assessed at 6 months (BI6mths) and patients were followed for 5 years post-stroke. Survival probability was computed using Kaplan-Meier analysis and compared across 3 BI6mths-classes (0-60, 65-90, 95-100) (log-rank test). Significant independent predictors were determined using multivariate Cox regression analysis (hazard ratio (HR)). Results: Five-year cumulative risk of death was 29.12% (95% confidence interval (CI): 22.86-35.38). Age (HR = 1.06, 95% CI: 1.04-1.09), cognitive impairment (HR = 1.77, 95% CI: 1.21-2.57), diabetes mellitus (HR = 1.68, 95% CI: 1.16- 2.41) and atrial fibrillation (HR = 1.52, 95% CI: 1.08-2.14) were independent predictors of increased mortality. Hyperlipidaemia (HR = 0.66, 95% CI: 0.46-0.94), and higher BI6mths (HR = 0.98, 95% CI: 0.97-0.99) were independent predictors of decreased mortality. Five-year survival probability was 0.85 (95% CI: 0.80-0.89) for patients in BI6mthsclass: 95-100, 0.72 (95% CI: 0.63-0.79) in BI6mths-class: 65-90 and 0.50 (95% CI: 0.40-0.60) in BI6mths-class: 0-60 (p < 0.0001). Conclusion: Nearly one-third of rehabilitation patients died during the first 5 years following stroke. Functional status at 6 months was a powerful predictor of long-term mortality. Maximum functional independence at 6 months post-stroke should be promoted through medical interventions and rehabilitation. Future studies are recommended to evaluate the direct effect of rehabilitation on long-term survival

Germline and Somatic DNA Damage Repair Gene Mutations and Overall Survival in Metastatic Pancreatic Adenocarcinoma Patients Treated with FOLFIRINOX

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with lack of predictive biomarkers. We conducted a study to assess DNA damage repair (DDR) gene mutations as a predictive biomarker in PDAC patients treated with FOLFIRINOX. Experimental Design: Indiana University Simon Cancer Center pancreatic cancer database was used to identify patients with metastatic PDAC, treated with FOLFIRINOX and had tissue available for DNA sequencing. Baseline demographic, clinical, and pathologic information was gathered. DNA isolation and targeted sequencing was performed using the Ion AmpliSeq protocol. Overall survival (OS) analysis was conducted using Kaplan–Meier, logistic regression and Cox proportional hazard methods. Multivariate models were adjusted for age, gender, margin status, CA 19-9, adjuvant chemotherapy, tumor and nodal stage. Results: Overall, 36 patients were sequenced. DDR gene mutations were found in 12 patients. Mutations were seen in BRCA1 (N = 7), BRCA2 (N = 5), PALB2 (N = 3), MSH2 (N = 1), and FANCF (N = 1) of all the DDR genes sequenced. Median age was 65.5 years, 58% were male, 97.2% were Caucasian and 51.4% had any family history of cancer. The median OS was near significantly superior in those with DDR gene mutations present vs. absent [14 vs. 5 months; HR, 0.58; 95% confidence interval (CI), 0.29–1.14; log-rank P = 0.08]. Multivariate logistic (OR, 1.47; 95% CI, 1.04–2.06; P = 0.04) and Cox regression (HR, 0.37; 95% CI, 0.15–0.94; P = 0.04) showed presence of DDR gene mutations was associated with improved OS. Conclusions: In a single institution, retrospective study, we found that the presence of DDR gene mutations are associated with improved OS in PDAC patients treated with FOLFIRINOX