skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy
Title: Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy
Abstract Mendelian Randomization (MR) studies are threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large databases. Here we describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats. Specifically, we propose the routine reporting of sensitivity statistics that reveal the minimal strength of violations necessary to explain away the MR results. We further provide intuitive displays of the robustness of the MR estimate to any degree of violation, and formal bounds on the worst-case bias caused by violations multiple times stronger than observed variables. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings by examining the effect of body mass index on diastolic blood pressure and Townsend deprivation index.  more » « less
Award ID(s):
2106908 1943497 1705121
PAR ID:
10337285
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Nature Communications
Volume:
13
Issue:
1
ISSN:
2041-1723
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, Biometrika)
    We develop an omitted variable bias framework for sensitivity analysis of instrumental variable estimates that naturally handles multiple side effects (violations of the exclusion restriction assumption) and confounders (violations of the ignorability of the instrument assumption) of the instrument, exploits expert knowledge to bound sensitivity parameters and can be easily implemented with standard software. Specifically, we introduce sensitivity statistics for routine reporting, such as (extreme) robustness values for instrumental variables, describing the minimum strength that omitted variables need to have to change the conclusions of a study. Next, we provide visual displays that fully characterize the sensitivity of point estimates and confidence intervals to violations of the standard instrumental variable assumptions. Finally, we offer formal bounds on the worst possible bias under the assumption that the maximum explanatory power of omitted variables is no stronger than a multiple of the explanatory power of observed variables. Conveniently, many pivotal conclusions regarding the sensitivity of the instrumental variable estimate (e.g., tests against the null hypothesis of a zero causal effect) can be reached simply through separate sensitivity analyses of the effect of the instrument on the treatment (the first stage) and the effect of the instrument on the outcome (the reduced form). We apply our methods in a running example that uses proximity to college as an instrumental variable to estimate the returns to schooling. 
    more » « less
  2. ; (, ACM)
    Scheduling in multi-channel wireless communication system presents formidable challenges in effectively allocating resources. To address these challenges, we investigate a multi-resource restless matching bandit (MR-RMB) model for heterogeneous resource systems with an objective of maximizing long-term discounted total rewards while respecting resource constraints. We have also generalized to applications beyond multi-channel wireless. We discuss the Max-Weight Index Matching algorithm, which optimizes resource allocation based on learned partial indexes. We have derived the policy gradient theorem for index learning. Our main contribution is the introduction of a new Deep Index Policy (DIP), an online learning algorithm tailored for MR-RMB. DIP learns the partial index by leveraging the policy gradient theorem for restless arms with convoluted and unknown transition kernels of heterogeneous resources. We demonstrate the utility of DIP by evaluating its performance for three different MR-RMB problems. Our simulation results show that DIP indeed learns the partial indexes efficiently. 
    more » « less
  3. ; ; (, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)
    Aldrich, Jonathan; Salvaneschi, Guido (Ed.)
    Timing channel attacks are emerging as real-world threats to computer security. In cryptographic systems, an effective countermeasure against timing attacks is the constant-time programming discipline. However, strictly enforcing the discipline manually is both time-consuming and error-prone. While various tools exist for analyzing/verifying constant-time programs, they sacrifice at least one feature among precision, soundness and efficiency. In this paper, we build CtChecker, a sound static analysis for constant-time programming. Under the hood, CtChecker uses a static information flow analysis to identify violations of constant-time discipline. Despite the common wisdom that sound, static information flow analysis lacks precision for real-world applications, we show that by enabling field-sensitivity, context-sensitivity and partial flow-sensitivity, CtChecker reports fewer false positives compared with existing sound tools. Evaluation on real-world cryptographic systems shows that CtChecker analyzes 24K lines of source code in under one minute. Moreover, CtChecker reveals that some repaired code generated by program rewriters supposedly remove timing channels are still not constant-time. 
    more » « less
  4. ; ; ; ; ; (, Radiology: Artificial Intelligence)
    Purpose: To determine if saliency maps in radiology artificial intelligence (AI) are vulnerable to subtle perturbations of the input, which could potentially lead to misleading interpretations, using Prediction-Saliency Correlation (PSC) for evaluating the sensitivity and robustness of saliency methods. Materials and Methods: In this retrospective study, locally trained deep learning models and a research prototype provided by a commercial vender were systematically evaluated on 191,229 chest radiographs from the CheXpert dataset(1,2) and 7,022 MRI images of human brain tumor classification dataset(3). Two radiologists performed a reader study on 270 chest radiographs pairs. A model-agnostic approach for computing the PSC coefficient was used to evaluate the sensitivity and robustness of seven commonly used saliency methods. Results: Leveraging locally trained model parameters, we revealed the saliency methods’ low sensitivity (maximum PSC = 0.25, 95% CI: 0.12, 0.38) and weak robustness (maximum PSC = 0.12, 95% CI: 0.0, 0.25) on the CheXpert dataset. Without model specifics, we also showed that the saliency maps from a commercial prototype could be irrelevant to the model output (area under the receiver operating characteristic curve dropped by 8.6% without affecting the saliency map). The human observer studies confirmed that is difficult for experts to identify the perturbed images, who had less than 44.8% correctness. Conclusion: Popular saliency methods scored low PSC values on the two datasets of perturbed chest radiographs, indicating weak sensitivity and robustness. The proposed PSC metric provides a valuable quantification tool for validating the trustworthiness of medical AI explainability. Abbreviations: AI = artificial intelligence, PSC = prediction-saliency correlation, AUC = area under the receiver operating characteristic curve, SSIM = structural similarity index measure. Summary: Systematic evaluation of saliency methods through subtle perturbations in chest radiographs and brain MRI images demonstrated low sensitivity and robustness of those methods, warranting caution when using saliency methods that may misrepresent changes in AI model prediction. 
    more » « less
  5. ; ; (, ACM)
    While Kubernetes enables practitioners to rapidly deploy their software and perform container orchestration efficiently, security of the Kubernetes-based deployment infrastructure is a concern for industry practitioners. A systematic understanding of how dynamic analysis can be used for securing Kubernetes deployments can aid practitioners in securing their Kubernetes deployments. We present an experience report, where we describe empirical findings from three dynamic application security testing (DAST) tools on a Kubernetes deployment used by 'Company-Z'. From our empirical study, we find (i) 3,442 recommended security configurations are violated in 'Company-Z's' Kubernetes deployment; and (ii) of the three studied DAST tools, Kubescape and Kubebench provide the highest support with respect to detecting 14 types of recommended security configurations. Based on our findings, we recommend practitioners to apply DAST tools for their Kubernetes deployments, and security researchers to investigate how to detect configuration violations dynamically in the Kubernetes deployment. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email