Mediation pathway selection with unmeasured mediator-outcome confounding

Causal mediation analysis aims to investigate how an intermediary factor, called a mediator, regulates the causal effect of a treatment on an outcome. With the increasing availability of measurements on a large number of potential mediators, methods for selecting important mediators have been proposed. However, these methods often assume the absence of unmeasured mediator-outcome confounding. We allow for such confounding in a linear structural equation model for the outcome and further propose an approach to tackle the mediator selection issue. To achieve this, we firstly identify causal parameters by constructing a pseudo proxy variable for unmeasured confounding. Leveraging this proxy variable, we propose a partially penalized method to identify mediators affecting the outcome. The resultant estimates are consistent, and the estimates of nonzero parameters are asymptotically normal. Motivated by these results, we introduce a two-step procedure to consistently select active mediation pathways, eliminating the need to test composite null hypotheses for each mediator that are commonly required by traditional methods. Simulation studies demonstrate the superior performance of our approach compared to existing methods. Finally, we apply our approach to genomic data, identifying gene expressions that potentially mediate the impact of a genetic variant on mouse obesity.Comment: 35 page

Identifying Causal Effects Using Instrumental Variables from the Auxiliary Population

Instrumental variable approaches have gained popularity for estimating causal effects in the presence of unmeasured confounding. However, the availability of instrumental variables in the primary population is often challenged due to stringent and untestable assumptions. This paper presents a novel method to identify and estimate causal effects in the primary population by utilizing instrumental variables from the auxiliary population, incorporating a structural equation model, even in scenarios with nonlinear treatment effects. Our approach involves using two datasets: one from the primary population with joint observations of treatment and outcome, and another from the auxiliary population providing information about the instrument and treatment. Our strategy differs from most existing methods by not depending on the simultaneous measurements of instrument and outcome. The central idea for identifying causal effects is to establish a valid substitute through the auxiliary population, addressing unmeasured confounding. This is achieved by developing a control function and projecting it onto the function space spanned by the treatment variable. We then propose a three-step estimator for estimating causal effects and derive its asymptotic results. We illustrate the proposed estimator through simulation studies, and the results demonstrate favorable performance. We also conduct a real data analysis to evaluate the causal effect between vitamin D status and BMI.Comment: 19 page

Identification and Estimation of Causal Effects Using non-Gaussianity and Auxiliary Covariates

Assessing causal effects in the presence of unmeasured confounding is a challenging problem. Although auxiliary variables, such as instrumental variables, are commonly used to identify causal effects, they are often unavailable in practice due to stringent and untestable conditions. To address this issue, previous researches have utilized linear structural equation models to show that the causal effect can be identifiable when noise variables of the treatment and outcome are both non-Gaussian. In this paper, we investigate the problem of identifying the causal effect using auxiliary covariates and non-Gaussianity from the treatment. Our key idea is to characterize the impact of unmeasured confounders using an observed covariate, assuming they are all Gaussian. The auxiliary covariate can be an invalid instrument or an invalid proxy variable. We demonstrate that the causal effect can be identified using this measured covariate, even when the only source of non-Gaussianity comes from the treatment. We then extend the identification results to the multi-treatment setting and provide sufficient conditions for identification. Based on our identification results, we propose a simple and efficient procedure for calculating causal effects and show the $\sqrt{n}$-consistency of the proposed estimator. Finally, we evaluate the performance of our estimator through simulation studies and an application.Comment: 16 papges, 7 Figure

A risk prediction model for efficient intubation in the emergency department: A 4‐year single‐center retrospective analysis

Abstract Objective To analyze the risk factors associated with intubated critically ill patients in the emergency department (ED) and develop a prediction model by machine learning algorithms. Methods This study was conducted in an academic tertiary hospital in Hangzhou, China. Critically ill patients admitted to the ED were retrospectively analyzed from May 2018 to July 2022. The demographic characteristics, distribution of organ dysfunction, parameters for different organs’ examination, and status of mechanical ventilation were recorded. These patients were assigned to the intubation and non‐intubation groups according to ventilation support. We used the eXtreme Gradient Boosting (XGBoost) algorithm to develop the prediction model and compared it with other algorithms, such as logistic regression, artificial neural network, and random forest. SHapley Additive exPlanations was used to analyze the risk factors of intubated critically ill patients in the ED. Results Of 14,589 critically ill patients, 10,212 comprised the training group and 4377 comprised the test group; 2289 intubated patients were obtained from the electronic medical records. The mean age, mean scores of vital signs, parameters of different organs, and blood oxygen examination results differed significantly between the two groups (p < 0.05). The white blood cell count, international normalized ratio, respiratory rate, and pH are the top four risk factors for intubation in critically ill patients. Based on the risk factors in different predictive models, the XGBoost model showed the highest area under the receiver operating characteristic curve (0.84) for predicting ED intubation. Conclusions For critically ill patients in the ED, the proposed model can predict potential intubation based on the risk factors in the clinically predictive model

Modulating Surface Composition and Oxygen Reduction Reaction Activities of Pt–Ni Octahedral Nanoparticles by Microwave-Enhanced Surface Diffusion during Solvothermal Synthesis

Compositional segregations in shaped alloy nanoparticles can significantly affect their catalytic activity and are largely dependent on their elemental anisotropic growth and diffusion during nanoparticle synthesis. An efficient approach to control the surface segregations while keeping the nanoparticle shape are highly desired for fine-tuning their catalytic properties. Using octahedral Pt–Ni nanoparticles as a typical example, we report a new strategy to modulate the surface composition of shaped bimetallic nanoparticles by microwave-enhanced surface diffusion during solvothermal synthesis. Compared to traditional solvothermal synthesis, the application of microwave significantly promotes atomic diffusion, particularly surface diffusion, within the Pt–Ni octahedrons, leading to Pt segregation on the {111} facets while largely keeping the octahedral shape. The obtained segregated Pt–Ni octahedral nanoparticles performed excellent activity toward oxygen reduction reaction. The revealed microwave-enhanced surface diffusion in a liquid phase provides a new way to modulate surface compositions of bimetallic alloy nanoparticles at relatively lower temperatures compared to the widely adopted high-temperature gas-phase thermal annealing

Determination of the

The neutron capture cross section of $$^{232}Th$$ has been measured with the time-of-flight technique in the energy range from 10 to 200 keV at the back-streaming white neutron beam-line (Back-n) of China Spallation Neutron Source (CSNS). The pulse height weighting technique (PHWT) was applied with four C$$_{6}$$D$$_{6}$$ liquid scintillators to measure the prompt gamma-ray energy release following neutron capture. The measurement data, corrected with the PHWT, have been normalized to the saturated resonances at 21.8 eV. The background was determined by a lead sample measurement and detailed Monte Carlo simulations. The $$^{232}Th(n,\gamma )$$ average cross sections have been determined relative to the $$^{197}Au(n,\gamma )$$ reaction cross sections. The results are consistent with the evaluation values of CENDL-3.2 and JENDL-5. The total uncertainties, including the PHWT, normalization, background subtraction, corrections, and relative measurement, are in the range of 4.5–4.8%