Sensitivity Analysis for Quantiles of Hidden Biases in Matched Observational Studies

In matched observational studies, the inferred causal conclusions pretending that matching has taken into account all confounding can be sensitive to unmeasured confounding. In such cases, a sensitivity analysis is often conducted, which investigates whether the observed association between treatment and outcome is due to effects caused by the treatment or it is due to hidden confounding. In general, a sensitivity analysis tries to infer the minimum amount of hidden biases needed in order to explain away the observed association between treatment and outcome, assuming that the treatment has no effect. If the needed bias is large, then the treatment is likely to have significant effects. The Rosenbaum sensitivity analysis is a modern approach for conducting sensitivity analysis for matched observational studies. It investigates what magnitude the maximum of the hidden biases from all matched sets needs to be in order to explain away the observed association, assuming that the treatment has no effect. However, such a sensitivity analysis can be overly conservative and pessimistic, especially when the investigators believe that some matched sets may have exceptionally large hidden biases. In this paper, we generalize Rosenbaum's framework to conduct sensitivity analysis on quantiles of hidden biases from all matched sets, which are more robust than the maximum. Moreover, we demonstrate that the proposed sensitivity analysis on all quantiles of hidden biases is simultaneously valid and is thus a free lunch added to the conventional sensitivity analysis. The proposed approach works for general outcomes, general matched studies and general test statistics. Finally, we demonstrate that the proposed sensitivity analysis also works for bounded null hypotheses as long as the test statistic satisfies certain properties. An R package implementing the proposed method is also available online

SAFS: A Deep Feature Selection Approach for Precision Medicine

In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prioritizing risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach is to use deep learning to identify significant risk factors affecting left ventricular mass indexed to body surface area (LVMI) as an indicator of heart damage risk. The results show that our feature learning and representation approach leads to better results in comparison with others

A statistical framework for consolidating "sibling" probe sets for Affymetrix GeneChip data

<p>Abstract</p> <p>Background</p> <p>Affymetrix GeneChip typically contains multiple probe sets per gene, defined as sibling probe sets in this study. These probe sets may or may not behave similar across treatments. The most appropriate way of consolidating sibling probe sets suitable for analysis is an open problem. We propose the Analysis of Variance (ANOVA) framework to decide which sibling probe sets can be consolidated.</p> <p>Results</p> <p>The ANOVA model allows us to separate the sibling probe sets into two types: those behave similarly across treatments and those behave differently across treatments. We found that consolidation of sibling probe sets of the former type results in large increase in the number of differentially expressed genes under various statistical criteria. The approach to selecting sibling probe sets suitable for consolidating is implemented in R language and freely available from <url>http://research.stowers-institute.org/hul/affy/</url>.</p> <p>Conclusion</p> <p>Our ANOVA analysis of sibling probe sets provides a statistical framework for selecting sibling probe sets for consolidation. Consolidating sibling probe sets by pooling data from each greatly improves the estimates of a gene expression level and results in identification of more biologically relevant genes. Sibling probe sets that do not qualify for consolidation may represent annotation errors or other artifacts, or may correspond to differentially processed transcripts of the same gene that require further analysis.</p

A theoretical framework for the Hamiltonian of angular momentum optomechanical system

Photon carries linear momentum and angular momentum simultaneously. Within the light-matter interaction process, exchange of linear momentum results in optical forces, whereas exchange of angular momentum leads to optical torques. Use of optical forces (light pressure or damping) have been long and wide in quantum optomechanics, however, those of optical torque and optical angular momentum are not. Here we propose a theoretical framework based on optical angular momentum and optical torques to derive the Hamiltonians of cavity orbital and spin angular momentum optomechanical systems, respectively. Moreover, based on the method, we successfully obtain the Hamiltonian of the complex angular momentum optomechanical systems consisting of micro-cavity and several torsional oscillators, whose reflection coefficients are non-unit. Our results indicate the general applicability of our theoretical framework for the Hamiltonian of angular momentum optomechanical systems and extend the research scope of quantum optomechanics