arXiv subjects

Joseph L. Jacobson

Publications and source records attributed to Joseph L. Jacobson.

7 recordsLinked to original sources

arXiv2026

Two-stage Estimation for Causal Inference Involving a Semi-continuous Exposure

Methods for causal inference are well developed for binary and continuous exposures, but in many settings, the exposure has a substantial mass at zero-such exposures are called semi-continuous. We propose a general causal framework for such semi-continuous exposures, together with a novel two-stage estimation strategy. A two-part propensity structure is introduced for the semi-continuous exposure, with one component for exposure status (exposed vs unexposed) and another for the exposure level among those exposed, and incorporates both into a marginal structural model that disentangles the effects of exposure status and dose. The two-stage procedure sequentially targets the causal dose-response among exposed individuals and the causal effect of exposure status at a reference dose, allowing flexibility in the choice of propensity score methods in the second stage. We establish consistency and asymptotic normality for the resulting estimators, and characterise their limiting values under misspecification of the propensity score models. Simulation studies evaluate finite sample performance and robustness, and an application to a study of prenatal alcohol exposure and child cognition demonstrates how the proposed methods can be used to address a range of scientific questions about both exposure status and exposure intensity.

stat.ME↗

arXiv2025

Prenatal alcohol exposure and child cognition: semi-continuous exposures, causal inference and evidence synthesis

We address the challenge of causal inference status and the dose-response effects with a semi-continuous exposure. A two-stage approach is proposed using estimating equation for multiple outcomes with large sample properties derived for the resulting estimators. Homogeneity tests are developed to assess whether causal effects of exposure status and the dose-response effects are the same across multiple outcomes. A global homogeneity test is also developed to assess whether the effect of exposure status (exposed/not exposed) and the dose-response effect of the continuous exposure level are each equal across all outcomes. The methods of estimation and testing are rigorously evaluated in simulation studies and applied to a motivating study on the effects of prenatal alcohol exposure on childhood cognition defined by executive function (EF), academic achievement in math, and learning and memory (LM).

stat.ME↗

arXiv2023

Semi-parametric Benchmark Dose Analysis with Monotone Additive Models

Benchmark dose analysis aims to estimate the level of exposure to a toxin that results in a clinically-significant adverse outcome and quantifies uncertainty using the lower limit of a confidence interval for this level. We develop a novel framework for benchmark dose analysis based on monotone additive dose-response models. We first introduce a flexible approach for fitting monotone additive models via penalized B-splines and Laplace-approximate marginal likelihood. A reflective Newton method is then developed that employs de Boor's algorithm for computing splines and their derivatives for efficient estimation of the benchmark dose. Finally, we develop and assess three approaches for calculating benchmark dose lower limits: a naive one based on asymptotic normality of the estimator, one based on an approximate pivot, and one using a Bayesian parametric bootstrap. The latter approaches improve upon the naive method in terms of accuracy and are guaranteed to return a positive lower limit; the approach based on an approximate pivot is typically an order of magnitude faster than the bootstrap, although they are both practically feasible to compute. We apply the new methods to make inferences about the level of prenatal alcohol exposure associated with clinically significant cognitive defects in children using data from an NIH-funded longitudinal study. Software to reproduce the results in this paper is available at https://github.com/awstringer1/bmd-paper-code.

stat.ME↗

arXiv2022

Bayesian outcome selection modelling

Psychiatric and social epidemiology often involves assessing the effects of environmental exposure on outcomes that are difficult to measure directly. To address this problem, it is common to measure outcomes using a comprehensive battery of different tests thought to be related to a common, underlying construct of interest. In the application that motivates our work, for example, researchers wanted to assess the impact of in utero alcohol exposure on child cognition and neuropsychological development, which were evaluated using a range of different tests. Statistical analysis of the resulting multiple outcomes data can be challenging, not only because of the need to account for the correlation between outcomes measured on the same individual, but because it is often unclear, a priori, which outcomes are impacted by the exposure under study. While researchers will generally have some hypotheses about which outcomes are important, a framework is needed to help identify outcomes that are sensitive to the exposure and to quantify the associated treatment or exposure effects of interest. We propose such a framework using a modification of stochastic search variable selection (SSVS), a popular Bayesian variable selection model and use it to quantify an overall effect of the exposure on the affected outcomes. We investigate the performance of the method via simulation and illustrate its application to data from a study involving the effects of prenatal alcohol exposure on child cognition.

stat.ME↗

arXiv2020

Bayesian structural equation modeling for data from multiple cohorts

While it is well known that high levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, the exact nature of the dose response is less well understood. In particular, there is a pressing need to identify the levels of PAE associated with an increased risk of clinically significant adverse effects. To address this issue, data have been combined from six longitudinal birth cohort studies in the United States that assessed the effects of PAE on cognitive outcomes measured from early school age through adolescence. Structural equation models (SEMs) are commonly used to capture the association among multiple observed outcomes in order to characterise the underlying variable of interest (in this case, cognition) and then relate it to PAE. However, it was not possible to apply classic SEM software in our context because different outcomes were measured in the six studies. In this paper we show how a Bayesian approach can be used to fit a multi-group multi-level structural model that maps cognition to a broad range of observed variables measured at multiple ages. These variables map to several different cognitive subdomains and are examined in relation to PAE after adjusting for confounding using propensity scores. The model also tests the possibility of a change point in the dose-response function.

stat.AP↗

arXiv2020

A Hierarchical Meta-Analysis for Settings Involving Multiple Outcomes across Multiple Cohorts

Evidence from animal models and epidemiological studies has linked prenatal alcohol exposure (PAE) to a broad range of long-term cognitive and behavioral deficits. However, there is virtually no information in the scientific literature regarding the levels of PAE associated with an increased risk of clinically significant adverse effects. During the period from 1975-1993, several prospective longitudinal cohort studies were conducted in the U.S., in which maternal reports regarding alcohol use were obtained during pregnancy and the cognitive development of the offspring was assessed from early childhood through early adulthood. The sample sizes in these cohorts did not provide sufficient power to examine effects associated with different levels and patterns of PAE. To address this critical public health issue, we have developed a hierarchical meta-analysis to synthesize information regarding the effects of PAE on cognition, integrating data on multiple endpoints from six U.S. longitudinal cohort studies. Our approach involves estimating the dose-response coefficients for each endpoint and then pooling these correlated dose-response coefficients to obtain an estimated `global' effect of exposure on cognition. In the first stage, we use individual participant data to derive estimates of the effects of PAE by fitting regression models that adjust for potential confounding variables using propensity scores. The correlation matrix characterizing the dependence between the endpoint-specific dose-response coefficients estimated within each cohort is then run, while accommodating incomplete information on some endpoints. We also compare and discuss inferences based on the proposed approach to inferences based on a full multivariate analysis

stat.ME↗

arXiv2019

Fast Infant MRI Skullstripping with Multiview 2D Convolutional Neural Networks

Skullstripping is defined as the task of segmenting brain tissue from a full head magnetic resonance image~(MRI). It is a critical component in neuroimage processing pipelines. Downstream deformable registration and whole brain segmentation performance is highly dependent on accurate skullstripping. Skullstripping is an especially challenging task for infant~(age range 0--18 months) head MRI images due to the significant size and shape variability of the head and the brain in that age range. Infant brain tissue development also changes the $T_1$-weighted image contrast over time, making consistent skullstripping a difficult task. Existing tools for adult brain MRI skullstripping are ill equipped to handle these variations and a specialized infant MRI skullstripping algorithm is necessary. In this paper, we describe a supervised skullstripping algorithm that utilizes three trained fully convolutional neural networks~(CNN), each of which segments 2D $T_1$-weighted slices in axial, coronal, and sagittal views respectively. The three probabilistic segmentations in the three views are linearly fused and thresholded to produce a final brain mask. We compared our method to existing adult and infant skullstripping algorithms and showed significant improvement based on Dice overlap metric~(average Dice of 0.97) with a manually labeled ground truth data set. Label fusion experiments on multiple, unlabeled data sets show that our method is consistent and has fewer failure modes. In addition, our method is computationally very fast with a run time of 30 seconds per image on NVidia P40/P100/Quadro 4000 GPUs.

cs.CV↗