Inverting estimating equations for causal inference on quantiles

The causal inference literature frequently focuses on estimating the mean of the potential outcome, whereas the quantiles of the potential outcome may carry important additional information. We propose a universal approach, based on the inverse estimating equations, to generalize a wide class of causal inference solutions from estimating the mean of the potential outcome to its quantiles. We assume that an identifying moment function is available to identify the mean of the threshold-transformed potential outcome, based on which a convenient construction of the estimating equation of quantiles of potential outcome is proposed. In addition, we also give a general construction of the efficient influence functions of the mean and quantiles of potential outcomes, and identify their connection. We motivate estimators for the quantile estimands with the efficient influence function, and develop their asymptotic properties when either parametric models or data-adaptive machine learners are used to estimate the nuisance functions. A broad implication of our results is that one can rework the existing result for mean causal estimands to facilitate causal inference on quantiles, rather than starting from scratch. Our results are illustrated by several examples

B-meson Semi-inclusive Decay to 2−+2^{-+} Charmonium in NRQCD and X(3872)

The semi-inclusive B-meson decay into spin-singlet D-wave $2^{-+}$ charmonium, $B\to \eta_{c2}+X$, is studied in nonrelativistic QCD (NRQCD). Both color-singlet and color-octet contributions are calculated at next-to-leading order (NLO) in the strong coupling constant $\alpha_s$. The non-perturbative long-distance matrix elements are evaluated using operator evolution equations. It is found that the color-singlet $^1D_2$ contribution is tiny, while the color-octet channels make dominant contributions. The estimated branching ratio $B(B\to \eta_{c2}+X)$ is about $0.41\,\times10^{-4}$ in the Naive Dimensional Regularization (NDR) scheme and $1.24\,\times10^{-4}$ in the t'Hooft-Veltman (HV) scheme, with renormalization scale $\mu=m_b=4.8$\,GeV. The scheme-sensitivity of these numerical results is due to cancelation between ${}^1S_0^{[8]}$ and ${}^1P_1^{[8]}$ contributions. The $\mu$-dependence curves of NLO branching ratios in both schemes are also shown, with $\mu$ varying from $\frac{m_b}{2}$ to $2m_b$ and the NRQCD factorization or renormalization scale $\mu_{\Lambda}$ taken to be $2m_c$. Comparison of the estimated branching ratio of $B\to \eta_{c2}+X$ with the observed branching ratio of $B \to X(3872)+K$ may lead to the conclusion that X(3872) is unlikely to be the $2^{-+}$ charmonium state $\eta_{c2}$.Comment: Version published in PRD, references added, 26 pages, 9 figure

Broadband negative refraction in stacked fishnet metamaterial

We demonstrate a scheme to utilize the stacked fishnet metamaterial for all-angle negative refraction and subwavelength imaging within a wide frequency range starting from zero frequency. The theoretical predictions are verified by the finite-difference-in-time-domain (FDTD) numerical simulations. The phenomena come from the negative evanescent coupling between the adjacent slab waveguides through the breathing air holes perforated on metal layers.Comment: 8 pages, 4 figure

Genome-wide association analysis identifies resistance loci for bacterial blight in a diverse collection of indica rice germplasm

Bacterial blight, which is caused by Xanthomonas oryzae pv. oryzae (Xoo), is one of the most devastating rice diseases worldwide. The development and use of disease-resistant cultivars have been the most effective strategy to control bacterial blight. Identifying the genes mediating bacterial blight resistance is a prerequisite for breeding cultivars with broad-spectrum and durable resistance. We herein describe a genome-wide association study involving 172 diverse Oryza sativa ssp. indica accessions to identify loci influencing the resistance to representative strains of six Xoo races. Twelve resistance loci containing 121 significantly associated signals were identified using 317,894 single nucleotide polymorphisms, which explained 13.3–59.9% of the variability in lesion length caused by Xoo races P1, P6, and P9a. Two hotspot regions (L11 and L12) were located within or nearby two cloned R genes (xa25 and Xa26) and one fine-mapped R gene (Xa4). Our results confirmed the relatively high resolution of genome-wide association studies. Moreover, we detected novel significant associations on chromosomes 2, 3, and 6–10. Haplotype analyses of xa25, the Xa26 paralog (MRKc; LOC_Os11g47290), and a Xa4 candidate gene (LOC_11g46870) revealed differences in bacterial blight resistance among indica subgroups. These differences were responsible for the observed variations in lesion lengths resulting from infections by Xoo races P1 and P9a. Our findings may be relevant for future studies involving bacterial blight resistance gene cloning, and provide insights into the genetic basis for bacterial blight resistance in indica rice, which may be useful for knowledge-based crop improvement. (Résumé d'auteur

Causal mediation analysis with a failure time outcome in the presence of exposure measurement error

Causal mediation analysis is widely used in health science research to evaluate the extent to which an intermediate variable explains an observed exposure-outcome relationship. However, the validity of analysis can be compromised when the exposure is measured with error, which is common in health science studies. This article investigates the impact of exposure measurement error on assessing mediation with a failure time outcome, where a Cox proportional hazard model is considered for the outcome. When the outcome is rare with no exposure-mediator interaction, we show that the unadjusted estimators of the natural indirect and direct effects can be biased into either direction, but the unadjusted estimator of the mediation proportion is approximately unbiased as long as measurement error is not large or the mediator-exposure association is not strong. We propose ordinary regression calibration and risk set regression calibration approaches to correct the exposure measurement error-induced bias in estimating mediation effects and to allow for an exposure-mediator interaction in the Cox outcome model. The proposed approaches require a validation study to characterize the measurement error process between the true exposure and its error-prone counterpart. We apply the proposed approaches to the Health Professionals Follow-up study to evaluate extent to which body mass index mediates the effect of vigorous physical activity on the risk of cardiovascular diseases, and assess the finite-sample properties of the proposed estimators via simulations

Doubly robust estimation and sensitivity analysis for marginal structural quantile models

The marginal structure quantile model (MSQM) is a useful tool to characterize the causal effect of a time-varying treatment on the full distribution of potential outcomes. However, to date, only the inverse probability weighting (IPW) approach has been developed to identify the structural causal parameters in MSQM, which requires correct specification of the propensity score models for the treatment assignment mechanism. We propose a doubly robust approach for the MSQM under the semiparametric framework. We derive the efficient influence function associated with a MSQM and estimate causal parameters in the MSQM by combining IPW and a new iterative conditional regression approach that models the full potential outcome distribution. The proposed approach is consistent if either of the models associated with treatment assignment or the potential outcome distributions is correctly specified, and is locally efficient if both models are correct. To implement the doubly robust MSQM estimator, we propose to solve a smoothed estimating equation to facilitate efficient computation of the point and variance estimates. In addition, we develop a new confounding function and sensitivity analysis strategy to investigate the robustness of several MSQM estimators when the no unmeasured confounding assumption is violated. We apply the proposed methods to the Yale New Haven Health System Electronic Health Record data to study the causal effect of antihypertensive medications to inpatients with severe hypertension, and assess the robustness of findings to unmeasured time-varying confounding

Multiply robust estimation for causal survival analysis with treatment noncompliance

Comparative effectiveness research with randomized trials or observational studies frequently addresses a time-to-event outcome and can require unique considerations in the presence of treatment noncompliance. Motivated by the challenges in addressing noncompliance in the ADAPTABLE pragmatic trial, we develop a multiply robust estimator to estimate the principal survival causal effects under the principal ignorability and monotonicity assumption. The multiply robust estimator involves several working models including that for the treatment assignment, the compliance strata, censoring, and time to event of interest. We demonstrate that the proposed estimator is consistent even if one, and sometimes two, of the working models are incorrectly specified. We further contribute sensitivity analysis strategies for investigating the robustness of the multiply robust estimator under violation of two identification assumptions specific to noncompliance. We implement the multiply robust method in the ADAPTABLE trial to evaluate the effect of low- versus high-dose aspirin assignment on patients' death and hospitalization from cardiovascular diseases, and further obtain the causal effect estimates when the identification assumptions fail to hold. We find that, comparing to low-dose assignment, assignment to the high-dose leads to differential effects among always high-dose takers, compliers, and always low-dose takers. Such treatment effect heterogeneity contributes to the null intention-to-treatment effect, and suggests that policy makers should design personalized strategies based on potential compliance patterns to maximize treatment benefits to the entire study population