This volume compiles fourteen peer‑reviewed papers that showcase state‑of‑the‑art developments in statistical modeling as applied to biological and medical data. It is divided into four thematic parts—survival analysis, longitudinal and time‑series modeling, model development, and applied modeling—each offering both theoretical innovations and practical case studies. The editors bring together contributions from leading statisticians and rising scholars, many of whom are affiliated with the BIO‑SI programme funded by Science Foundation Ireland. The book serves as both a reference for specialized methods (e.g., hierarchical generalized nonlinear models, frailty models) and a springboard for new research directions in biostatistics and bioinformatics.
📖 Overview of the Book
「Statistical Modelling in Biostatistics and Bioinformatics」 is a softcover reprint of the original 2014 edition, republished by Springer in August 2016. With 258 pages, it belongs to the Contributions to Statistics series (no. 36) and carries ISBN 978‑3319357645 . The volume gathers selected papers that reflect both methodological rigor and real‑world applications in computational biology and medical research.
🎯 Key Topics Covered
Survival Analysis and Multivariate Survival Models
「Frailty models with structural dispersion」, offering new approaches to unobserved heterogeneity in time‑to‑event data.
「Multivariate interval‑censored survival models」, including parametric, semi‑parametric, and non‑parametric frameworks for complex censoring schemes.
Longitudinal Data and Time‑Series Modeling
「Joint mean–covariance modeling」 in repeated measures, facilitating flexible inference when both mean response and covariance structure evolve over time.
「Seasonality and structural break detection」, with a case study on visitor patterns post‑9/11.
Statistical Model Development
「Hierarchical generalized nonlinear models」, extending GLM frameworks to multi‑level data.
「Finite‑mixture clustering of SNP data」, exemplifying applications in statistical genetics and population structure analysis.
Applied Statistical Modeling
「Detecting selective sweeps」 in genomic data, a crucial task in evolutionary biology.
「Mixture modeling and bootstrap techniques」 for reproductive allocation in plant ecology.
👩🔬 Editor and Contributor Background
「Professor Gilbert MacKenzie」 holds an adjunct Professorship in Statistics at the University of Limerick and served as President of the Irish Statistical Association. His research spans epidemiology, multivariate survival modeling, frailty models, and covariance structures. 「Professor Defen Peng」 was a senior research fellow in the BIO‑SI programme at Limerick and previously worked in economics at Zhongnan University of Economics and Law (PRC). Her current interests include frailty models for survival data, bivariate survival analysis, and the robustness of regression with categorical covariates.
🧑🎓 Target Audience and Use
This book is ideal for graduate students, postdoctoral researchers, and faculty in biostatistics, bioinformatics, epidemiology, and related fields who seek both methodological depth and examples of real‑data application. It assumes familiarity with generalized linear models, survival analysis, and basic mixed‑effects modeling, making it best suited for those with a solid statistical foundation.
📝 Academic Significance and Applications
By presenting rigorous theoretical developments alongside compelling applications—from toxicological assays to genomics—this volume bridges the gap between statistical theory and practice in life sciences. Its contributions have influenced subsequent work in multi‑parameter survival models (e.g., Peng, MacKenzie & Burke, 2019) and continue to inform the design and analysis of longitudinal studies and high‑throughput biological experiments.
Whether you are developing new statistical methods or applying advanced techniques to complex biological datasets, 「Statistical Modelling in Biostatistics and Bioinformatics」 offers a treasure trove of ideas and demonstrations that will enrich your research toolkit and inspire further innovation.
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