Triad sou.

Causal inference with misspecified network interference structure Abstract. Under interference, the treatment of one unit may affect the outcomes of other units. Such interference patterns between units are typically repr

Causal inference with misspecified network interference structure. Bar Weinstein, Daniel Nevo. Biometrics. doi.org/10.1093/biom...

Diaconis–Ylvisaker prior penalized likelihood for 𝒑/𝒏 → 𝜿 ∈ (0,1) logistic regression. P Sterzinger, I Kosmidis. Biometrika. doi.org/10.1093/biom...

Sage Journals: Discover world-class research Subscription and open access journals from Sage, the world's leading independent academic publisher.

On flexible covariate adjustment under covariate-constrained randomization. Bingkai Wang, Fan Li. Clinical Trials. journals.sagepub.com/doi/abs/10.1...

Book Review: Fundamentals of Probability. Fundamentals of Probability, 5th edition. Saeed Ghahramani. Chapman & Hall/CRC Press 2024, 700 pages. doi.org/10.1093/jrss...

Analysis of Longitudinal Studies in Epidemiology Aimed at a nontechnical audience, with intuitive explanations instead of mathematical derivations, Analysis of Longitudinal Studies in Epidemiology covers a wide range of topics that include Poisson r...

Analysis of Longitudinal Studies in Epidemiology. Nicholas P. Jewell, Alan Hubbard. Chapman & Hall 2026, 288 Pages. www.routledge.com/Analysis-of-...

Empirical Likelihood Method in Survival Analysis: With R implementation, Second Edition This book systematically covers empirical likelihood methods in most important topics in survival analysis: the Kaplan–Meier and the Nelson–Aalen estimator, the log rank test, the Cox proportional haz...

Empirical Likelihood Method in Survival AnalysisWith R implementation, Second Edition. Mai Zhou. Chapman & Hall 2026, 384 Pages. www.routledge.com/Empirical-Li...

Likelihood and its Extensions Likelihood serves as a unifying concept in both the theory and practice of statistical science. This is in a sense inevitable when probability models are used as a basis for inference. While the key i...

Likelihood and its Extensions. Nancy Reid, Cristiano Varin, Grace Y. Yi. Chapman & Hall 2026, 328 Pages. www.routledge.com/Likelihood-a...

Doug Altman at The BMJ. Richard Smith. The BMJ. www.bmj.com/content/392/...

Likelihood Confidence Intervals for Misspecified Cox Models The robust Wald confidence interval (CI) for the Cox model is commonly used when the model may be misspecified or when weights are applied. However, it can perform poorly when there are few events in....

Likelihood Confidence Intervals for Misspecified Cox Models. Yongwu Shao, Xu Guo. Statistics in Medicine. onlinelibrary.wiley.com/doi/10.1002/...

Sage Journals: Discover world-class research Subscription and open access journals from Sage, the world's leading independent academic publisher.

Response-adaptive randomization with imperfect intermediate endpoints. Yousra Kherabi, Michael A Proschan, Lori E Dodd. Clinical Trials. journals.sagepub.com/doi/abs/10.1...

Winner’s Curse Free Robust Mendelian Randomization with Summary Data In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporati...

Winner’s Curse Free Robust Mendelian Randomization with Summary Data. Zhongming Xie, Wanheng Zhang, Jingshen Wang, Chong Wu. Journal of the American Statistical Association. www.tandfonline.com/doi/full/10....

Confidence intervals and point estimates for treatment effects in adaptive enrichment designs - Jinyu Zhu, Andrew Titman, Fang Wan, 2026 Adaptive enrichment designs allow subgroup selection of the patient population within a confirmatory trial via an interim analysis. However, this design complic...

Confidence intervals and point estimates for treatment effects in adaptive enrichment designs. Jinyu Zhu, Andrew Titman, Fang Wan. Statistical Methods in Medical Research. journals.sagepub.com/doi/full/10....

Statistical analysis of Likert-based ordinal scales: a guide for clinical trialists - BMC Medical Research Methodology BMC Medical Research Methodology - Likert-based scales are a popular tool in clinical trials for assessing patient-reported outcomes. A key analytical decision involves whether to treat these data...

Statistical analysis of Likert-based ordinal scales: a guide for clinical trialists. Ahmed A. Al-Jaishi, Meaghan S. Cuerden, Bin Luo, Pavel S. Roshonov & Amit X. Garg. BMC Medical Research Methodology. link.springer.com/article/10.1...

Anytime validity is free: inducing sequential tests Abstract. Anytime valid sequential tests permit us to stop testing based on the current data, without invalidating the inference. Given a maximum number of

Anytime validity is free: inducing sequential tests. Nick W Koning, Sam van Meer. Journal of the Royal Statistical Society Series B: Statistical Methodology. doi.org/10.1093/jrss...

Identifying prior evidence for new trials (REVEAL): guidance for clinical researchers Conducting a systematic literature review before initiating a new clinical trial can prevent research waste and provide critical justification and valuable insights for designing the trial. Clinical r...

Identifying prior evidence for new trials (REVEAL): guidance for clinical researchers. Griebler U, Ledinger D, Klerings I, Schandelmaier S, Dobrescu A, Deschodt M et al. The BMJ. www.bmj.com/content/392/...

The synthetic instrument: from sparse association to sparse causation Abstract. In many observational studies, researchers are often interested in the effects of multiple exposures on a single outcome. Standard approaches for

The synthetic instrument: from sparse association to sparse causation. Dingke Tang, Dehan Kong, Linbo Wang. Journal of the Royal Statistical Society Series B: Statistical Methodology. doi.org/10.1093/jrss...

A Bayesian Treatment Selection Design for Phase II Randomised Cancer Clinical Trials It is crucial to design Phase II cancer clinical trials that balance the efficiency of treatment selection with clinical practicality. Sargent and Goldberg proposed a frequentist design that allows d....

A Bayesian Treatment Selection Design for Phase II Randomised Cancer Clinical Trials. Moka Komaki, Satoru Shinoda, Haiyan Zheng, Kouji Yamamoto. Statistics in Medicine. onlinelibrary.wiley.com/doi/10.1002/...

Win statistics (win ratio, win odds, and net benefit): Noncollapsibility and standardization for randomized clinical trials The win ratio, along with its stratified variant known as the stratified win ratio, has been widely utilized in many disease areas for both design and analysis of clinical trials. It is applied mos...

Win statistics (win ratio, win odds, and net benefit): Noncollapsibility and standardization for randomized clinical trials. Gaohong Dong, Margaret Gamalo-Siebers, Ying Cui, Bo Huang, Xiaolong Luo, Lu Tian. Journal of Biopharmaceutical Statistics. www.tandfonline.com/doi/full/10....

Abraham Wald and the Origins of the Sequential Probability Ratio Test Abraham Wald’s formalization of the sequential probability ratio test in the crucible of World War II is one of the more famous cases in the history of statistics of the interplay of statistical th...

Abraham Wald and the Origins of the Sequential Probability Ratio Test. Joel B. Greenhouse, Christopher J. Phillips. The American Statistician. www.tandfonline.com/doi/full/10....

Guiding causal inference research in general medical journals Improving the conduct and reporting of newer methodological approaches Causal inference, the multidisciplinary field focused on understanding cause and effect, underpins many health and social care r...

Guiding causal inference research in general medical journals. Timothy Feeney, Nazrul Islam, Tobias Kurth. The BMJ. www.bmj.com/content/392/...

The continuous net benefit: assessing the clinical utility of prediction models when informing a continuum of decisions - Diagnostic and Prognostic Research Diagnostic and Prognostic Research - The net benefit and decision curve analysis are increasingly being used to assess the clinical utility of prognostic models. This metric assesses the value...

The continuous net benefit: assessing the clinical utility of prediction models when informing a continuum of decisions. Jose Benitez-Aurioles, Laure Wynants, Niels Peek, Patrick Goodley, Philip Crosbie, Matthew Sperrin. Diagn Progn Res. link.springer.com/article/10.1...

Handling Missing Data in Participants with Baseline but No Post‐Baseline Data Participants who are randomized to treatment but have no post-baseline data pose a unique challenge. These participants need to be included to preserve randomization. Because there is no information ....

Handling Missing Data in Participants with Baseline but No Post‐Baseline Data. Craig Mallinckrodt, Ilya Lipkovich, Samuel Dickson, Suzanne Hendrix, Geert Molenberghs. Pharmaceutical Statistics. onlinelibrary.wiley.com/doi/10.1002/...

A Course in Large-sample and High-dimensional Theory This book provides a systematic treatment of two central regimes in statistical theory: classical large-sample theory for M- and Z-estimation with a fixed number of parameters, and high-dimensional th...

A Course in Large-sample and High-dimensional Theory. Zhiqiang Tan. Chapman & Hall, 248 Pages. www.routledge.com/A-Course-in-...

Introduction to Modern Randomization-Based Design and Analysis for Causal Inference Design of experiments is, in essence, a disciplined way to learn about cause and effect. Modern experiments can involve a few to millions of units and hundreds or thousands of covariates. These settin...

Introduction to Modern Randomization-Based Design and Analysis for Causal Inference. Tirthankar Dasgupta, Donald B. Rubin. Chapman & Hall, 360 Pages. www.routledge.com/Introduction...

Generalized win-odds regression models for composite endpoints - Lifetime Data Analysis The time-to-first-event analysis is often used for studies involving multiple event times, where each component is treated equally, regardless of their clinical importance. Alternative summaries such ...

Generalized win-odds regression models for composite endpoints. Bang Wang, Zi Wang & Yu Cheng. Lifetime Data Analysis. doi.org/10.1007/s109...

Fast Bayesian Functional Principal Components Analysis Functional Principal Components Analysis (FPCA) is a widely used analytic tool for dimension reduction of functional data. Traditional implementations of FPCA estimate the principal components from...

Fast Bayesian Functional Principal Components Analysis. Joseph Sartini, Xinkai Zhou, Elizabeth Selvin, Scott Zeger, Ciprian M. Crainiceanu. Journal of Computational and Graphical Statistics. www.tandfonline.com/doi/full/10....

A framework for causal estimand selection under positivity violations Abstract. Estimating the causal effect of a treatment or health policy with observational data can be challenging due to an imbalance of and a lack of over

A framework for causal estimand selection under positivity violations. Martha Barnard, Jared D Huling, Julian Wolfson. Biometrics. doi.org/10.1093/biom...

Non-boundary covariance matrix estimation in generalized linear mixed effects models using data augmentation priors Abstract. Boundary estimates of random effects covariance matrices commonly arise when using maximum likelihood (ML) estimation in generalized linear mixed

Non-boundary covariance matrix estimation in generalized linear mixed effects models using data augmentation priors. Tina Košuta, Erik Langerholc, Rok Blagus. Biometrics. doi.org/10.1093/biom...

Using Off‐Treatment Sequential Multiple Imputation for Binary Outcomes to Address Intercurrent Events Handled by a Treatment Policy Strategy The estimand framework proposes different strategies to address intercurrent events. The treatment policy strategy seems to be the most favoured as it is closely aligned with the pre-addendum intenti...

Using Off‐Treatment Sequential Multiple Imputation for Binary Outcomes to Address Intercurrent Events Handled by a Treatment Policy Strategy. Sunita Rehal, Nicky Best, Sarah Watts, Thomas Drury. Pharmaceutical Statistics. onlinelibrary.wiley.com/doi/10.1002/...

A Tutorial on Optimal Dynamic Treatment Regimes A dynamic treatment regime (DTR) is a sequence of treatment decision rules tailored to an individual's evolving status over time. In precision medicine, much focus has been placed on finding an optim...

Tutorials in Biostatistics

A Tutorial on Optimal Dynamic Treatment Regimes. Chunyu Wang, Brian D. M. Tom. Statistics in Medicine. onlinelibrary.wiley.com/doi/10.1002/...