Statistical Methods and Bayesian Inference

A method for analyzing physiological data with multiple non-independent observations

The paper introduces a method to analyze physiological data with multiple non-independent observations, ensuring each subject is represented once and without distribution assumptions.

FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets

FEMA-Long enables efficient modeling of time-dependent effects in longitudinal data, discovering genetic variants with time-dependent effects on infant growth traits.

Improving Statistical Rigor in Animal Aging Research by Addressing Clustering and Nesting Effects: Illustration with the National Institute on Aging's Intervention Testing Program Data

This paper demonstrates how accounting for clustering and nesting effects in lifespan studies improves statistical rigor and reduces Type I error rates using data from the National Institute on Aging's Intervention Testing Program.

Joint Modeling of Longitudinal Biomarker and Survival Outcomes with the Presence of Competing Risk in Nested Case-Control Studies with Application to the TEDDY Microbiome Dataset

The paper introduces JM-NCC, a joint modeling framework for nested case-control studies with competing risks, applied to the TEDDY microbiome dataset to analyze biomarker and survival outcomes.

Hierarchical Bayesian modeling of multi-region brain cell count data

The paper applies hierarchical Bayesian modeling to multi-region brain cell count data, showing improved inference over traditional methods in under-sampled datasets.

Missing data approaches for longitudinal neuroimaging research: Examples from the Adolescent Brain and Cognitive Development (ABCD) Study

The paper discusses methods for handling missing data in longitudinal neuroimaging datasets, using the ABCD Study as an example, and advocates for advanced statistical techniques over conventional approaches.

Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution

The paper provides a practical guide to Bayesian inference in neuroscience, demonstrating its application through regression and ANOVA models with rodent auditory data and offering an open-source toolkit.

Adaptive predictor-set linear model: an imputation-free method for linear regression prediction on datasets with missing values

This paper introduces aps-lm, a linear regression method that predicts outcomes from incomplete datasets without imputation, showing improved accuracy and reduced bias compared to imputation strategies.

Non-Gaussian Normative Modelling With Hierarchical Bayesian Regression

This paper extends hierarchical Bayesian regression to model non-Gaussian data with skewness and kurtosis, showing improved performance on neuroimaging datasets.

LIMBARE: an Advanced Linear Mixed-effects Breakpoint Analysis with Robust Estimation Method with Applications to Longitudinal Ophthalmic Studies

LIMBARE improves breakpoint estimation in longitudinal ophthalmic studies by using robust linear mixed-effects modeling, outperforming other methods in simulations and real data.

lmeEEG: Mass linear mixed-effects modeling of EEG data with crossed random effects

The paper introduces lmeEEG, a method for applying linear mixed-effects models to EEG data with crossed random effects, showing improved performance and computational efficiency.

Statistical power and false positive rates for interdependent outcomes are strongly influenced by test type: Implications for behavioral neuroscience

The paper evaluates statistical methods for interdependent outcomes in behavioral neuroscience, finding high false positives with linear models and limited power with alternatives.

Increasing information gain in animal research by improving statistical model accuracy

The paper improves statistical model accuracy in animal research to increase information gain per animal, showing benefits for small sample sizes.

A real data-driven simulation strategy to select an imputation method for mixed-type trait data

The paper evaluates imputation methods for mixed-type trait data using a real data-driven simulation strategy, finding that random forest with phylogenetic information performs best.

A monotone single index model for missing-at-random longitudinal proportion data

The paper develops a semi-parametric Beta regression model for longitudinal proportion data with missing-at-random responses, using a monotone single index transform and Bayesian inference.

Systematic Bayesian posterior analysis guided by Kullback-Leibler divergence facilitates hypothesis formation

This paper introduces a KL divergence-based Bayesian framework to systematically rank parameters for hypothesis formation, demonstrating its effectiveness in two computational models.

Accessible Analysis of Longitudinal Data with Linear Mixed Effects Models

The paper re-analyzes longitudinal mouse growth data using linear mixed effects models and compares them to ANOVA, showing inconsistencies and the importance of modeling correlated data correctly.

An empirically-driven guide on using Bayes Factors for M/EEG decoding

This paper provides a guide on using Bayes Factors for M/EEG decoding results, examining their impact on statistical assessment of neural data.

Evidence for the null hypothesis in functional magnetic resonance imaging using group-level Bayesian inference

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.

Bayesian inference of population prevalence

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

Multi-Level Multi-Growth Models: New opportunities for addressing developmental theory using longitudinal designs

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: A Bayesian hierarchical modeling framework for Seahorse extracellular flux oxygen consumption rate data analysis

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

Joint Modeling of Zero-inflated Longitudinal Proportions and Time-to-event Data with Application to a Gut Microbiome Study

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.

partR2: Partitioning R2 in generalized linear mixed models

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

Trimmed Constrained Mixed Effects Models: Formulations and Algorithms.

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.

A Bayesian predictive approach for dealing with pseudoreplication

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

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies

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

Highly Efficient Hypothesis Testing Methods for Regression-type Tests with Correlated Observations and Heterogeneous Variance Structure

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

Bayesian Model Selection Maps for group studies using M/EEG data

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.

Characterizing and Managing Missing Structured Data in Electronic Health Records

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.