Robust Estimation of Heterogeneous Treatment Effects: An Algorithm-based Approach.

Heterogeneous treatment effect estimation is an essential element in the practice of tailoring treatment to suit the characteristics of individual patients. Most existing methods are not sufficiently robust against data irregularities. To enhance the robustness of the existing methods, we recently put forward a general estimating equation that unifies many existing learners. But the performance of model-based learners depends heavily on the correctness of the underlying treatment effect model. This paper addresses this vulnerability by converting the treatment effect estimation to a weighted supervised learning problem. We combine the general estimating equation with supervised learning algorithms, such as the gradient boosting machine, random forest, and artificial neural network, with appropriate modifications. This extension retains the estimators' robustness while enhancing their flexibility and scalability. Simulation shows that the algorithm-based estimation methods outperform their model-based counterparts in the presence of nonlinearity and non-additivity. We developed an R package, RCATE, for public access to the proposed methods. To illustrate the methods, we present a real data example to compare the blood pressure-lowering effects of two classes of antihypertensive agents.

Multivariate partial linear varying coefficients model for gene-environment interactions with multiple longitudinal traits.

Correlated phenotypes often share common genetic determinants. Thus, a multi-trait analysis can potentially increase association power and help in understanding pleiotropic effect. When multiple traits are jointly measured over time, the correlation information between multivariate longitudinal responses can help to gain power in association analysis, and the longitudinal traits can provide insights on the dynamic gene effect over time. In this work, we propose a multivariate partially linear varying coefficients model to identify genetic variants with their effects potentially modified by environmental factors. We derive a testing framework to jointly test the association of genetic factors and illustrated with a bivariate phenotypic trait, while taking the time varying genetic effects into account. We extend the quadratic inference functions to deal with the longitudinal correlations and used penalized splines for the approximation of nonparametric coefficient functions. Theoretical results such as consistency and asymptotic normality of the estimates are established. The performance of the testing procedure is evaluated through Monte Carlo simulation studies. The utility of the method is demonstrated with a real data set from the Twin Study of Hormones and Behavior across the menstrual cycle project, in which single nucleotide polymorphisms associated with emotional eating behavior are identified.

Observed increasing water constraint on vegetation growth over the last three decades.

Despite the growing interest in predicting global and regional trends in vegetation productivity in response to a changing climate, changes in water constraint on vegetation productivity (i.e., water limitations on vegetation growth) remain poorly understood. Here we conduct a comprehensive evaluation of changes in water constraint on vegetation growth in the extratropical Northern Hemisphere between 1982 and 2015. We document a significant increase in vegetation water constraint over this period. Remarkably divergent trends were found with vegetation water deficit areas significantly expanding, and water surplus areas significantly shrinking. The increase in water constraints associated with water deficit was also consistent with a decreasing response time to water scarcity, suggesting a stronger susceptibility of vegetation to drought. We also observed shortened water surplus period for water surplus areas, suggesting a shortened exposure to water surplus associated with humid conditions. These observed changes were found to be attributable to trends in temperature, solar radiation, precipitation, and atmospheric CO2. Our findings highlight the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate.

Robust estimation of heterogeneous treatment effects using electronic health record data.

Estimation of heterogeneous treatment effects is an essential component of precision medicine. Model and algorithm-based methods have been developed within the causal inference framework to achieve valid estimation and inference. Existing methods such as the A-learner, R-learner, modified covariates method (with and without efficiency augmentation), inverse propensity score weighting, and augmented inverse propensity score weighting have been proposed mostly under the square error loss function. The performance of these methods in the presence of data irregularity and high dimensionality, such as that encountered in electronic health record (EHR) data analysis, has been less studied. In this research, we describe a general formulation that unifies many of the existing learners through a common score function. The new formulation allows the incorporation of least absolute deviation (LAD) regression and dimension reduction techniques to counter the challenges in EHR data analysis. We show that under a set of mild regularity conditions, the resultant estimator has an asymptotic normal distribution. Within this framework, we proposed two specific estimators for EHR analysis based on weighted LAD with penalties for sparsity and smoothness simultaneously. Our simulation studies show that the proposed methods are more robust to outliers under various circumstances. We use these methods to assess the blood pressure-lowering effects of two commonly used antihypertensive therapies.

Expired Tidal Volume Variation in Extremely Low Birth Weight and Very Low Birth Weight Infants on Volume-Targeted Ventilation.

In a prospective study we describe the delivery of small tidal volumes to extremely low birth weight (ELBW) and very low birth weight (VLBW) infants using a volume-targeted ventilation mode (VTV). Tidal volume delivery was consistent for both ELBW and VLBW infants independent of gestational age, birth weight, and the target volume.

Statistical Identification of Gene-gene Interactions Triggered By Nonlinear Environmental Modulation.

Complex diseases are often caused by the function of multiple genes, gene-gene (G×G) interactions as well as gene-environment (G×E) interactions. G×G and G×E interactions are ubiquitous in nature. Empirical evidences have shown that the effect of G×G interaction on disease risk could be largely modified by environmental changes. Such a G×G×E triple interaction could be a potential contributing factor to phenotypic plasticity. Although the role of environmental factors moderating genetic influences on disease risk has been broadly recognized, no statistical method has been developed to rigorously assess how environmental changes modify G×G interactions to affect disease risk. To address this issue, we developed a G×G×E triple interaction model in this work. We modeled the environmental modification effect via a varying-coefficient model where the structure of the varying effect is determined by data. Thus the model has the flexibility to assess nonlinear environmental moderation effect on G×G interaction. Simulation and real data analysis were conducted to show the utility of the method. Our approach provides a quantitative framework to assess triple interactions hypothesized in literature.

A powerful statistical method identifies novel loci associated with diastolic blood pressure triggered by nonlinear gene-environment interaction.

The genetic basis of blood pressure often involves multiple genetic factors and their interactions with environmental factors. Gene-environment interaction is assumed to play an important role in determining individual blood pressure variability. Older people are more prone to high blood pressure than younger ones and the risk may not display a linear trend over the life span. However, which gene shows sensitivity to aging in its effect on blood pressure is not clear. In this work, we allowed the genetic effect to vary over time and propose a varying-coefficient model to identify potential genetic players that show nonlinear response across different age stages. We detected 2 novel loci, gene MIR1263 (a microRNA coding gene) on chromosome 3 and gene UNC13B on chromosome 9, that are nonlinearly associated with diastolic blood pressure. Further experimental validation is needed to confirm this finding.