Information-Based Composite Likelihood Method for Hybrid Meta-Analysis Integrating Individual Participant Data and Aggregated Data [0.03%]
Guoqing Diao,Arvind Shah,Jianxin Lin et al.
Guoqing Diao et al.
Meta-analysis is a popular statistical technique in biomedical research. In particular, meta-analysis can assist clinicians in determining whether an intervention is effective or which intervention is most effective. Conventional meta-analy...
Yongxi Long,Bart C Jacobs,Ewout W Steyerberg et al.
Yongxi Long et al.
Initially proposed for analyzing composite endpoints, the win odds have recently received increasing interest for the analysis of ordinal outcomes. When comparing an ordinal outcome between two groups, the win odds are the odds that a rando...
A Latent Variable Approach for Causal Effect Estimation Under Misclassified Treatment Assignment [0.03%]
Yimeng Shang,Yu-Han Chiu,Lan Kong
Yimeng Shang
Misclassification in treatment assignment is a common issue in causal inference with observational studies, often leading to biased estimates of causal effects if unaddressed. Several methods have been developed to handle this issue by maki...
Structure Identification, Estimation for Variable Selection and Varying Coefficient EV Models With Longitudinal Data [0.03%]
Mingtao Zhao,Jingxiang Cao,Jun Sun et al.
Mingtao Zhao et al.
In this article, we propose a bias-corrected double penalized quadratic inference functions method to simultaneously identify model structure, estimate parameters, and perform variable selection for varying coefficient errors-in-variables (...
Peng Wu,Pengtao Zeng,Zhaoqing Tian et al.
Peng Wu et al.
Heterogeneous treatment effects, which vary according to individual covariates, are crucial in fields such as personalized medicine and tailored treatment strategies. In many applications, rather than considering the heterogeneity induced b...
Generalized Functional Linear Regression Models With Functional and Scalar Covariates Prone to Measurement Error [0.03%]
Yuanyuan Luan,Roger S Zoh,Sneha Jadhav et al.
Yuanyuan Luan et al.
Most methods for adjusting for biases due to measurement errors in covariates in generalized linear regression models focus on scalar covariates. Less work exists to correct for biases due to measurement error in a mixture of functional and...
Emilie Højbjerre-Frandsen,Mark J van der Laan,Alejandro Schuler
Emilie Højbjerre-Frandsen
In randomized clinical trials (RCTs), the accurate estimation of marginal treatment effects is crucial for determining the efficacy of interventions. Enhancing the statistical power of these analyses is a key objective for statisticians. Th...
Flexible Modeling of Time-Dependent Covariate Effects in Survival Models With Correlated Competing Risks: Application to the Evaluation of Risk-Reducing Salpingo-Oophorectomy in Women With BRCA1 Pathogenic Variants [0.03%]
Seungwoo Lee,Laurent Briollais,Yun-Hee Choi;BCFR
Seungwoo Lee
Modeling of medical interventions, such as preventive surgeries, on a survival outcome necessitates an accurate and flexible representation of the time-dependent effect of the intervention. We propose using B-splines to model the time-depen...
Tuning LASSO Models for Propensity Score Weighting and Using Synthetic Negative Control Exposures for Residual Bias Detection [0.03%]
Richard Wyss,Ben B Hansen,Georg Hahn et al.
Richard Wyss et al.
The propensity score (PS) is widely used to control for large numbers of covariates in high-dimensional healthcare database studies. In these settings, the least absolute shrinkage and selection operator (LASSO) is commonly used to estimate...
Score Matching for Differential Abundance Testing of Compositional High-Throughput Sequencing Data [0.03%]
Johannes Ostner,Hongzhe Li,Christian L Müller
Johannes Ostner
The class of a-b power interaction models, proposed by [1], provides a general framework for modeling sparse compositional data with pairwise feature interactions. This class includes many distributions as special cases and enables modeling...