<citation type='book' id='insr70060-cit-0002'><bookTitle>Foundations of Bayesian Statistics for Data Scientists: With R and Python</bookTitle> <author><givenNames>Alan</givenNames> <familyName>Agresti...

Click on the article title to read more.

onlinelibrary.wiley.com

doi.org

Risk of bias assessment for Mendelian randomisation studies: a guide - European Journal of Epidemiology

European Journal of Epidemiology -

link.springer.com

A Modified Random Survival Forest for Improving Prediction Accuracy in Case‐Cohort and Generalized Case‐Cohort Studies

Case-cohort and generalized case-cohort study designs offer cost-effective alternatives to full cohort studies for analyzing associations between risk factors and survival outcomes. However, existing...

onlinelibrary.wiley.com

Xu Ning, Francis K.C. Hui and A.H. Welsh (2026). ASYMPTOTIC RESULTS FOR PENALIZED QUASI-LIKELIHOOD ESTIMATION IN GENERALIZED LINEAR MIXED MODELS. Vol 36 No. 3, 1257-1278. DOI:10.5705/ss.202023.0343.

www3.stat.sinica.edu.tw

Hang Qian (2026). BAYESIAN STATISTICS BY ARITHMETIC OPERATIONS OF CONJUGATE DISTRIBUTIONS. Vol 36 No. 3, 1175-1192. DOI:10.5705/ss.202024.0052.

www3.stat.sinica.edu.tw

Regression by composition

We describe a modular regression framework in which covariate-dependent transformations are composed together and act on probability distributions. This fr

doi.org

Variable selection for clinical prediction models in low-dimensional data - a simulation study comparing traditional regression and machine learning methods - BMC Medical Research Methodology

Purpose A wide range of methods exist for developing a clinical prediction model (CPM) and for performing variable selection. Our purpose was to develop a fair simulation study design and to investiga...

link.springer.com

Semiparametric fiducial inference for Cox models

Abstract. R.A. Fisher introduced the fiducial distribution as a potential replacement for the Bayesian posterior distribution in the 1930s. During the past

doi.org

Validate User

doi.org

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test

We apply covariate adjustment to the Wilcoxon two sample statistic and Wilcoxon–Mann–Whitney test in comparing two treatments. The covariate adjustment through calibration not only improves efficienc....

onlinelibrary.wiley.com

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives

Tree ensembles are nonparametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret an...

tandfonline.com

Rejoinder to Commentaries on “A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events”

Click on the article title to read more.

onlinelibrary.wiley.com

Reinterpreting I2 Thresholds: Toward Context‐Specific Heterogeneity Assessment in Evidence Synthesis

Background The Cochrane Handbook's I2 categorization system (0%–25% “low”, 25%–75% “moderate”, ≥ 50% “high” heterogeneity) defines the standard approach to interpreting heterogeneity in meta-analysi...

onlinelibrary.wiley.com

Leveraging external controls in clinical trials: estimands, estimation, assumptions

It is increasingly common to augment randomized controlled trial with external controls from observational data, to evaluate the treatment effect of an intervention. Traditional approaches to treat...

tandfonline.com

Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science, Resve Saleh, Sohaib Majzoub, A.K. Md. Ehsanes Saleh, John Wiley & Sons, 2025, xiv + 416 pages, £103.95, hardcover. ISBN: 978‐1‐394‐29437‐4

Click on the article title to read more.

onlinelibrary.wiley.com

Comparison of flexible parametric modeling and nonparametric methods to estimate restricted mean survival time: A simulation study

In randomized controlled trials with survival time as the primary endpoint, it can be difficult to evaluate treatment effects using hazard ratios, particularly when the proportional hazards (PH) as...

tandfonline.com

Simulated treatment comparisons with jackknife pseudo values for estimating population-adjusted marginal treatment effects

Matching-adjusted indirect comparisons (MAIC) and simulated treatment comparisons (STC) are commonly used for indirect treatment comparisons when patient-level data are available for some treatment...

tandfonline.com

INTACT: a method for integration of longitudinal physical activity data from multiple sources

ABSTRACT. Wearable devices and digital phenotyping are increasingly used in observational and interventional studies to measure real-time biosignals such a

doi.org

Practical considerations when using the covariate-adjusted log-rank test for the analysis of time-to-event endpoints in oncology trials

ABSTRACT. Adjusting for prognostic baseline covariates in the design and analysis of randomized controlled trials can increase statistical power and improv

doi.org

Integration of aggregate data in causally interpretable meta-analysis by inverse weighting

ABSTRACT. Obtaining causally interpretable meta-analysis results is challenging when there are differences in the distribution of effect modifiers between

doi.org

doi.org

jstatsoft.org

sciencedirect.com

AIC for many-regressor heteroskedastic regressions

The original and corrected Akaike information criteria (AIC) have been routinely used for model selection for ages. The penalty terms in these criteri…

sciencedirect.com

Validate User

doi.org

bayesNMF: Fast Bayesian Poisson NMF with Automatically Learned Rank Applied to Mutational Signatures

Bayesian Poisson Non-Negative Matrix Factorization (NMF) is widely used to model count data, including in cancer mutational signature analysis. However, standard Gibbs samplers rely on computationa...

tandfonline.com

Causal Inference: A Tale of Three Frameworks | Journal of Data Science | School of Statistics, Renmin University of China

Causal inference is a central goal across many scientific disciplines. Over the past several decades, three major frameworks have emerged to formalize causal questions and guide their analysis: the po...

jds-online.org

Automatic debiased machine learning for covariate shifts

SUMMARY. We present machine learning estimators for causal and predictive parameters under covariate shift, where covariate distributions differ between tr

doi.org

Global Sensitivity Analysis for Studies Extending Inferences From a Randomized Trial to a Target Population

When individuals participating in a randomized trial differ with respect to the distribution of effect modifiers compared with the target population where the trial results will be used, treatment ef....

onlinelibrary.wiley.com