Predicting future risk of asthma exacerbations using individual conditional probabilities

Determination of future risk of exacerbations is a key issue in the management of asthma. We previously developed a method to calculate conditional probabilities (π) of future decreases in lung function by using the daily fluctuations in peak expiratory flow (PEF).

Unconventional Monetary Policy and the Great Recession: Estimating the Macroeconomic Effects of a Spread Compression at the Zero Lower Bound

We explore the macroeconomic effects of a compression in the long-term bond yield spread within the context of the Great Recession of 2007–09 via a time-varying parameter structural VAR model. We identify a “pure” spread shock defined as a shock that leaves the policy rate unchanged, which allows us to characterize the macroeconomic consequences of a decline in the yield spread induced by central banks’ asset purchases within an environment in which the policy rate is constrained by the effective zero lower bound. Two key findings stand out. First, compressions in the long-term yield spread exert a powerful effect on both output growth and inflation. Second, conditional on available estimates of the impact of the Federal Reserve’s and the Bank of England’s asset purchase programs on long-term yield spreads, our counterfactual simulations suggest that U.S. and U.K. unconventional monetary policy actions have averted significant risks both of deflation and of output collapses comparable to those that took place during the Great Depression.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

A sequential Cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV Cohort Study

When estimating the effect of treatment on HIV using data from observational studies, standard methods may produce biased estimates due to the presence of time-dependent confounders. Such confounding can be present when a covariate, affected by past exposure, is both a predictor of the future exposure and the outcome. One example is the CD4 cell count, being a marker for disease progression for HIV patients, but also a marker for treatment initiation and influenced by treatment. Fitting a marginal structural model (MSM) using inverse probability weights is one way to give appropriate adjustment for this type of confounding. In this paper we study a simple and intuitive approach to estimate similar treatment effects, using observational data to mimic several randomized controlled trials. Each 'trial' is constructed based on individuals starting treatment in a certain time interval. An overall effect estimate for all such trials is found using composite likelihood inference. The method offers an alternative to the use of inverse probability of treatment weights, which is unstable in certain situations. The estimated parameter is not identical to the one of an MSM, it is conditioned on covariate values at the start of each mimicked trial. This allows the study of questions that are not that easily addressed fitting an MSM. The analysis can be performed as a stratified weighted Cox analysis on the joint data set of all the constructed trials, where each trial is one stratum. The model is applied to data from the Swiss HIV cohort study.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

A transgenic model for conditional induction and rescue of portal hypertension reveals a role of VEGF-mediated regulation of sinusoidal fenestrations

Portal hypertension (PH) is a common complication and a leading cause of death in patients with chronic liver diseases. PH is underlined by structural and functional derangement of liver sinusoid vessels and its fenestrated endothelium. Because in most clinical settings PH is accompanied by parenchymal injury, it has been difficult to determine the precise role of microvascular perturbations in causing PH. Reasoning that Vascular Endothelial Growth Factor (VEGF) is required to maintain functional integrity of the hepatic microcirculation, we developed a transgenic mouse system for a liver-specific-, reversible VEGF inhibition. The system is based on conditional induction and de-induction of a VEGF decoy receptor that sequesters VEGF and preclude signaling. VEGF blockade results in sinusoidal endothelial cells (SECs) fenestrations closure and in accumulation and transformation of the normally quiescent hepatic stellate cells, i.e. provoking the two processes underlying sinusoidal capillarization. Importantly, sinusoidal capillarization was sufficient to cause PH and its typical sequela, ascites, splenomegaly and venous collateralization without inflicting parenchymal damage or fibrosis. Remarkably, these dramatic phenotypes were fully reversed within few days from lifting-off VEGF blockade and resultant re-opening of SECs' fenestrations. This study not only uncovered an indispensible role for VEGF in maintaining structure and function of mature SECs, but also highlights the vasculo-centric nature of PH pathogenesis. Unprecedented ability to rescue PH and its secondary manifestations via manipulating a single vascular factor may also be harnessed for examining the potential utility of de-capillarization treatment modalities.

Estimating loss to follow-up in HIV-infected patients on antiretroviral therapy: the effect of the competing risk of death in Zambia and Switzerland

Background Loss to follow-up (LTFU) is common in antiretroviral therapy (ART) programmes. Mortality is a competing risk (CR) for LTFU; however, it is often overlooked in cohort analyses. We examined how the CR of death affected LTFU estimates in Zambia and Switzerland. Methods and Findings HIV-infected patients aged ≥18 years who started ART 2004–2008 in observational cohorts in Zambia and Switzerland were included. We compared standard Kaplan-Meier curves with CR cumulative incidence. We calculated hazard ratios for LTFU across CD4 cell count strata using cause-specific Cox models, or Fine and Gray subdistribution models, adjusting for age, gender, body mass index and clinical stage. 89,339 patients from Zambia and 1,860 patients from Switzerland were included. 12,237 patients (13.7%) in Zambia and 129 patients (6.9%) in Switzerland were LTFU and 8,498 (9.5%) and 29 patients (1.6%), respectively, died. In Zambia, the probability of LTFU was overestimated in Kaplan-Meier curves: estimates at 3.5 years were 29.3% for patients starting ART with CD4 cells <100 cells/µl and 15.4% among patients starting with ≥350 cells/µL. The estimates from CR cumulative incidence were 22.9% and 13.6%, respectively. Little difference was found between naïve and CR analyses in Switzerland since only few patients died. The results from Cox and Fine and Gray models were similar: in Zambia the risk of loss to follow-up and death increased with decreasing CD4 counts at the start of ART, whereas in Switzerland there was a trend in the opposite direction, with patients with higher CD4 cell counts more likely to be lost to follow-up. Conclusions In ART programmes in low-income settings the competing risk of death can substantially bias standard analyses of LTFU. The CD4 cell count and other prognostic factors may be differentially associated with LTFU in low-income and high-income settings.

Estimating the net contribution of interleukin-28B variation to spontaneous hepatitis C virus clearance

The identification of associations between interleukin-28B (IL-28B) variants and the spontaneous clearance of hepatitis C virus (HCV) raises the issues of causality and the net contribution of host genetics to the trait. To estimate more precisely the net effect of IL-28B genetic variation on HCV clearance, we optimized genotyping and compared the host contributions in multiple- and single-source cohorts to control for viral and demographic effects. The analysis included individuals with chronic or spontaneously cleared HCV infections from a multiple-source cohort (n = 389) and a single-source cohort (n = 71). We performed detailed genotyping in the coding region of IL-28B and searched for copy number variations to identify the genetic variant or haplotype carrying the strongest association with viral clearance. This analysis was used to compare the effects of IL-28B variation in the two cohorts. Haplotypes characterized by carriage of the major alleles at IL-28B single-nucleotide polymorphisms (SNPs) were highly overrepresented in individuals with spontaneous clearance versus those with chronic HCV infections (66.1% versus 38.6%, P = 6 × 10(-9) ). The odds ratios for clearance were 2.1 [95% confidence interval (CI) = 1.6-3.0] and 3.9 (95% CI = 1.5-10.2) in the multiple- and single-source cohorts, respectively. Protective haplotypes were in perfect linkage (r(2) = 1.0) with a nonsynonymous coding variant (rs8103142). Copy number variants were not detected. CONCLUSION: We identified IL-28B haplotypes highly predictive of spontaneous HCV clearance. The high linkage disequilibrium between IL-28B SNPs indicates that association studies need to be complemented by functional experiments to identify single causal variants. The point estimate for the genetic effect was higher in the single-source cohort, which was used to effectively control for viral diversity, sex, and coinfections and, therefore, offered a precise estimate of the net host genetic contribution.