Statistical Methods and Bayesian Inference

EPHierStats models hierarchical relationships in electrophysiological data to prevent pseudoreplication and improve statistical accuracy in neuroscience studies.

This paper compares Bayesian parameter inference with classical null hypothesis testing in fMRI group analysis, arguing that Bayesian methods provide more intuitive evidence for both null and alternative hypotheses.

This paper introduces a Bayesian method to estimate population prevalence from individual participant data, offering a quantitative estimate with uncertainty instead of binary inferences.

This paper introduces multi-level multi-growth models to address developmental theory using longitudinal designs, demonstrating their ability to model multiple growth processes affecting a single outcome.

OCRbayes is a Bayesian framework for analyzing Seahorse XF OCR data, accounting for complex data structures to improve statistical inference.

The paper introduces JointMM, a joint modeling framework for analyzing longitudinal microbiome proportions and time-to-event data, showing its effectiveness in identifying microbial associations with disease progression.

The paper applies joint longitudinal-survival models to adjust for censoring bias in HbA1c trajectory analysis in type 2 diabetes patients, finding a higher gradient with the joint model compared to linear mixed effects models.

partR2 quantifies part R2 and structure coefficients for fixed effects in generalized linear mixed models, providing variance decomposition and confidence intervals.

The paper applies a Bayesian multivariate growth mixture model to identify cognitive decline patterns in Alzheimer's Disease Neuroimaging Initiative data.

This paper introduces Trimmed Constrained Mixed Effects Models with efficient algorithms and an open-source Python package, showing improved accuracy and computational efficiency in handling outliers and nonlinear data.

Treating neurons as independent gave about 46–96% false positives in simulated nested data; a hierarchical bootstrap stayed near 5% with many subjects and kept more power than per-subject averaging.

The paper introduces a Bayesian predictive approach to address pseudoreplication in biological research, demonstrating its effectiveness with two in vivo datasets.

The paper develops a framework to test imputation methods on a clinical stroke dataset and finds that listwise deletion performs poorly, while the best method depends on the missing data mechanism.

This paper introduces a hierarchical Bayesian inference framework for simultaneous model comparison and parameter estimation in group studies, showing improved accuracy and robustness.

The paper introduces efficient hypothesis testing methods for regression-type tests with correlated data and heterogeneous variance, outperforming existing methods in speed and accuracy.

This paper introduces a Bayesian Model Selection method for group-level M/EEG data analysis using SPM, enabling hypothesis comparison across brain regions and participants.

The paper develops exact tests for variance components in longitudinal microbiome studies and applies them to HIV-infected patients, identifying associations between lung microbiome genera and lung function.

The paper evaluates imputation methods for missing structured data in EHRs using clinical lab measures from 602,366 patients, showing some methods perform well while others are unsuitable for multiple imputation.

The paper provides a theoretical and practical analysis of variational Bayes, VML, ReML, and ML methods applied to the general linear model in fMRI data analysis.

The paper critiques null hypothesis significance testing (NHST) and advocates for alternative statistical methods in biomedical and psychological research.

The paper introduces a Bayesian multivariate multilevel model to analyze hierarchical clinical proteomics data with non-random missingness, showing improved performance over existing methods.