Principled Selection of Baseline Covariates to Account for Censoring in Randomized Trials with a Survival Endpoint

The analysis of randomized trials with time-to-event endpoints is nearly always plagued by the problem of censoring. As the censoring mechanism is usually unknown, analyses typically employ the assumption of non-informative censoring. While this assumption usually becomes more plausible as more baseline covariates are being adjusted for, such adjustment also raises concerns. Pre-specification of which covariates will be adjusted for (and how) is difficult, thus prompting the use of data-driven variable selection procedures, which may impede valid inferences to be drawn. The adjustment for covariates moreover adds concerns about model misspecification, and the fact that each change in adjustment set, also changes the censoring assumption and the treatment effect estimand. In this paper, we discuss these concerns and propose a simple variable selection strategy that aims to produce a valid test of the null in large samples. The proposal can be implemented using off-the-shelf software for (penalized) Cox regression, and is empirically found to work well in simulation studies and real data analyses

Functional and Structural Characteristics of Tumor Angiogenesis in Lung Cancers Overexpressing Different VEGF Isoforms Assessed by DCE- and SSCE-MRI

The expressions of different vascular endothelial growth factor (VEGF) isoforms are associated with the degree of tumor invasiveness and the patient's prognosis in human cancers. We hypothesized that different VEGF isoforms can exert different effects on the functional and structural characteristics of tumor angiogenesis. We used dynamic contrast-enhanced MRI (DCE-MRI) and steady-state contrast-enhanced MRI (SSCE-MRI) to evaluate in vivo vascular functions (e.g., perfusion and permeability) and structural characteristics (e.g., vascular size and vessel density) of the tumor angiogenesis induced by different VEGF isoforms (VEGF121, VEGF165, and VEGF189) in a murine xenograft model of human lung cancer. Tumors overexpressing VEGF189 were larger than those overexpressing the other two VEGF isoforms. The Ktrans map obtained from DCE-MRI revealed that the perfusion and permeability functions of tumor microvessels was highest in both the rim and core regions of VEGF189-overexpressing tumors (p<0.001 for both tumor rim and core). The relative vessel density and relative vessel size indexes derived from SSCE-MRI revealed that VEGF189-overexpressing tumors had the smallest (p<0.05) and the most-dense (p<0.01) microvessels, which penetrated deeply from the tumor rim into the core, followed by the VEGF165-overepxressing tumor, whose microvessels were located mainly in the tumor rim. The lowest-density microvessels were found in the VEGF121-overexpressing tumor; these microvessels had a relatively large lumen and were found mainly in the tumor rim. We conclude that among the three VEGF isoforms evaluated, VEGF189 induces the most densely sprouting and smallest tumor microvessels with the highest in vivo perfusion and permeability functions. These characteristics of tumor microvessels may contribute to the reported adverse effects of VEGF189 overexpression on tumor progression, metastasis, and patient survival in several human cancers, including non-small cell lung cancer, and suggest that applying aggressive therapy may be necessary in human cancers in which VEGF189 is overexpressed

Nicotinic acetylcholine receptors modulate osteoclastogenesis

Background: Our aim was to investigate the role of nicotinic acetylcholine receptors (nAChRs) in in-vitro osteoclastogenesis and in in-vivo bone homeostasis. Methods: The presence of nAChR subunits as well as the in-vitro effects of nAChR agonists were investigated by ex vivo osteoclastogenesis assays, real-time polymerase chain reaction, Western blot and flow cytometry in murine bone marrow-derived macrophages differentiated in the presence of recombinant receptor activator of nuclear factor kappa B ligand (RANKL) and macrophage colony-stimulating factor (M-CSF). The bone phenotype of mice lacking various nAChR subunits was investigated by peripheral quantitative computed tomography and histomorphometric analysis. Oscillations in the intracellular calcium concentration were detected by measuring the Fura-2 fluorescence intensity. Results: We could demonstrate the presence of several nAChR subunits in bone marrow-derived macrophages stimulated with RANKL and M-CSF, and showed that they are capable of producing acetylcholine. nAChR ligands reduced the number of osteoclasts as well as the number of tartrate-resistant acidic phosphatase-positive mononuclear cells in a dose-dependent manner. In vitro RANKL-mediated osteoclastogenesis was reduced in mice lacking α7 homomeric nAChR or β2-containing heteromeric nAChRs, while bone histomorphometry revealed increased bone volume as well as impaired osteoclastogenesis in male mice lacking the α7 nAChR. nAChR ligands inhibited RANKL-induced calcium oscillation, a well-established phenomenon of osteoclastogenesis. This inhibitory effect on Ca2+ oscillation subsequently led to the inhibition of RANKL-induced NFATc1 and c-fos expression after long-term treatment with nicotine. Conclusions: We have shown that the activity of nAChRs conveys a marked effect on osteoclastogenesis in mice. Agonists of these receptors inhibited calcium oscillations in osteoclasts and blocked the RANKL-induced activation of c-fos and NFATc1. RANKL-mediated in-vitro osteoclastogenesis was reduced in α7 knockout mice, which was paralleled by increased tibial bone volume in male mice in vivo. © 2016 Mandl et al

Measurement of the Bs Lifetime in Fully and Partially Reconstructed Bs -> Ds- (phi pi-)X Decays in pbar-p Collisions at sqrt(s) = 1.96 TeV

We present a measurement of the Bs lifetime in fully and partially reconstructed Bs -> Ds(phi pi)X decays in 1.3 fb-1 of pbar-p collisions at sqrt(s) = 1.96 TeV collected by the CDF II detector at the Fermilab Tevatron. We measure tau(Bs) = 1.518 +/- 0.041 (stat.) +/- 0.027 (syst.) ps. The ratio of this result and the world average B0 lifetime yields tau(Bs)/tau(B0) = 0.99 +/-0.03, which is in agreement with recent theoretical predictions.Comment: submitted to Phys. Rev. Let

Observation of the Y(4140)Y(4140) structure in the J/ψ ϕJ/\psi\,\phi Mass Spectrum in B±→J/ψ ϕKB^\pm\to J/\psi\,\phi K cays

The observation of the $Y(4140)$ structure in $B^\pm\rightarrow J/\psi\,\phi K^\pm$ decays produced in $\bar{p} p$ collisions at \sqrt{s}=1.96~\TeV is reported with a statistical significance greater than 5 standard deviations. A fit to the $J/\psi\,\phi$ mass spectrum is performed assuming the presence of a Breit-Wigner resonance. The fit yields a signal of $19^{+6}_{-5}$ resonance events, and resonance mass and width of 4143.4^{+2.9}_{-3.0}(\mathrm{stat})\pm0.6(\mathrm{syst})~\MeVcc and 15.3^{+10.4}_{-6.1}(\mathrm{stat})\pm2.5(\mathrm{syst})~\MeVcc respectively. The parameters of this resonance-like structure are consistent with values reported from an earlier CDF analysis.Comment: 7 pages, 2 figures, submited to Phys. Rev. Let

Max-norm optimization for robust matrix recovery

This paper studies the matrix completion problem under arbitrary sampling schemes. We propose a new estimator incorporating both max-norm and nuclear-norm regularization, based on which we can conduct efficient low-rank matrix recovery using a random subset of entries observed with additive noise under general non-uniform and unknown sampling distributions. This method significantly relaxes the uniform sampling assumption imposed for the widely used nuclear-norm penalized approach, and makes low-rank matrix recovery feasible in more practical settings. Theoretically, we prove that the proposed estimator achieves fast rates of convergence under different settings. Computationally, we propose an alternating direction method of multipliers algorithm to efficiently compute the estimator, which bridges a gap between theory and practice of machine learning methods with max-norm regularization. Further, we provide thorough numerical studies to evaluate the proposed method using both simulated and real datasets

Mining Massive Amounts of Genomic Data: A Semiparametric Topic Modeling Approach

Characterizing the functional relevance of transcription factors (TFs) in different biological contexts is pivotal in systems biology. Given the massive amount of genomic data, computational identification of TFs is emerging as a useful approach to bridge functional genomics with disease risk loci. In this article, we use large-scale gene expression and chromatin immunoprecipitation (ChIP) data corpuses to conduct high-throughput TF-biological context association analysis. This work makes two contributions: (i) From a methodological perspective, we propose a unified topic modeling framework for exploring and analyzing large and complex genomic datasets. Under this framework, we develop new statistical optimization algorithms and semiparametric theoretical analysis, which are also applicable to a variety of large-scale data analyses. (ii) From an experimental perspective, our method generates an informative list of tumor-related TFs and their possible effected tumor types. Our data-driven analysis of 38 TFs in 68 tumor biological contexts identifies functional signatures of epigenetic regulators, such as SUZ12 and SET-DB1, and nuclear receptors, in many tumor types. In particular, the TF signature of SUZ12 is present in a broad range of tumor types, many of which have not been reported before. In summary, our work established a robust method to identify the association between TFs and biological contexts. Given the limited amount of genome-wide binding profiles of TFs and the massive number of expression profiles, our work provides a useful tool to deconvolute the gene regulatory network for tumors and other biological contexts. Supplementary materials for this article are available online

Max-norm optimization for robust matrix recovery

This paper studies the matrix completion problem under arbitrary sampling schemes. We propose a new estimator incorporating both max-norm and nuclear-norm regularization, based on which we can conduct efficient low-rank matrix recovery using a random subset of entries observed with additive noise under general non-uniform and unknown sampling distributions. This method significantly relaxes the uniform sampling assumption imposed for the widely used nuclear-norm penalized approach, and makes low-rank matrix recovery feasible in more practical settings. Theoretically, we prove that the proposed estimator achieves fast rates of convergence under different settings. Computationally, we propose an alternating direction method of multipliers algorithm to efficiently compute the estimator, which bridges a gap between theory and practice of machine learning methods with max-norm regularization. Further, we provide thorough numerical studies to evaluate the proposed method using both simulated and real datasets