Gang Li

Instrumental variable (IV) analysis is widely used in fields such as economics and epidemiology to address unobserved confounding and measurement error when estimating the causal effects of intermediate covariates on outcomes. However, extending the commonly used two-stage least squares (TSLS) approach to survival settings is nontrivial due to censoring. This paper introduces a novel extension of TSLS to the semiparametric accelerated failure time (AFT) model with right-censored data, supported by rigorous theoretical justification. Specifically, we propose a generalized estimating equation (GEE) approach that combines Leurgans’ synthetic variable method with adaptive weighting, establish the asymptotic properties of the resulting estimator, and derive a consistent variance estimator, enabling valid causal inference. Simulation studies are conducted to evaluate the finite-sample performance of the proposed method across different scenarios. The results show that it outperforms the naïve unweighted GEE method, a parametric IV approach, and a one-stage estimator without IV. The proposed method is also highly scalable to large datasets, achieving a 300- to 1500-fold speedup relative to a Bayesian parametric IV approach. We further illustrate its utility through a real-data application using the UK Biobank data.

Figure S2 shows the effects of siRNA-mediated knockdown of PKAa and PKAb on CREB Ser133 phosphorylation, PKAa/b protein expression and CREB protein expression in PANC-1 cells.

Table S2 shows the gene expression in the pancreas as profiled with the Mouse Dopamine and Serotonin pathway RT Profiler PCR Array

Figure S1 shows AKT Ser473 and CREB Ser133 phosphorylation in PANC-1 cells.

Table S1 shows the gene expression in the pancreas as profiled with the Mouse Neurotransmitter Receptors RT Profile PCR Array

Joint modeling has become increasingly popular for characterizing the association between one or more longitudinal biomarkers and competing risks time-to-event outcomes. However, semiparametric multivariate joint modeling for large-scale data encounter substantial statistical and computational challenges, primarily due to the high dimensionality of random effects and the complexity of estimating nonparametric baseline hazards. These challenges often lead to prolonged computation time and excessive memory usage, limiting the utility of joint modeling for biobank-scale datasets. In this article, we introduce an efficient implementation of a semiparametric multivariate joint model, supported by a normal approximation and customized linear scan algorithms within an expectation-maximization (EM) framework. Our method significantly reduces computation time and memory consumption, enabling the analysis of data from thousands of subjects. The scalability and estimation accuracy of our approach are demonstrated through two simulation studies. We also present an application to the Primary Biliary Cirrhosis (PBC) dataset involving five longitudinal biomarkers as an illustrative example. A user-friendly R package, \texttt{FastJM}, has been developed for the shared random effects joint model with efficient implementation. The package is publicly available on the Comprehensive R Archive Network: https://CRAN.R-project.org/package=FastJM.

Table S2 shows the gene expression in the pancreas as profiled with the Mouse Dopamine and Serotonin pathway RT Profiler PCR Array

One of the deadliest types of cancer is pancreatic ductal adenocarcinoma (PDAC). Chronic stress and obesity are recognized as risk factors for PDAC. We hypothesized that the combination of stress and obesity strongly promotes pancreatic cancer development and growth. Here, we show that the stress mediator norepinephrine and the β-adrenergic receptor agonist isoproterenol rapidly stimulate cyclic adenosine monophosphate response element-binding protein (CREB) phosphorylation at Ser133 in human PDAC cells. Exposure to the nonselective β-adrenergic receptor antagonist propranolol or selective antagonists, including nebivolol, atenolol, or ICI118551, blocked CREB phosphorylation elicited by norepinephrine or isoproterenol in PDAC cells. Stimulation of PDAC cells with neurotensin, a neuropeptide implicated in obesity and PDAC, also stimulated CREB phosphorylation at Ser133. Mechanistically, norepinephrine induced CREB phosphorylation at Ser133 via PKA, whereas neurotensin promoted CREB phosphorylation predominantly through protein kinase D. Our results indicate that CREB is a point of signal convergence that mediates proliferation in PDAC cells and raised the possibility that stress and diet cooperate in promoting PDAC in vivo. To test this notion, mice expressing KrasG12D in all pancreatic lineages (KC mice) and fed an obesogenic high fat, calorie diet that promotes early PDAC development were subjected to social isolation stress. We show that social isolation stress induced a significant increase in the proportion of advanced PDAC precursor lesions (pancreatic intraepithelial neoplasia) in KC mice subjected to an obesogenic high fat, calorie diet.Implications: Our data imply that chronic (social isolation) stress cooperates with diet-induced obesity in accelerating the development of pancreatic cancer.

Table S1 shows the gene expression in the pancreas as profiled with the Mouse Neurotransmitter Receptors RT Profile PCR Array

Figure S1 shows AKT Ser473 and CREB Ser133 phosphorylation in PANC-1 cells.