STATISTICS IN BIOPHARMACEUTICAL RESEARCH (SBR)，发表关注生物制药行业研究人员和应用统计学家需求的文章；来自医学院、兽医学院、公共卫生学院和药学院的学术生物统计学家；在监管机构工作的统计学家和定量分析师（例如，美国食品和药物管理局及其在其他国家的对应机构）；有兴趣将本期刊中介绍的方法应用于自己的领域的统计学家；以及对将统计方法应用于生物制药问题感兴趣的非统计学家。

The journal "Statistics in Biopharmaceutical Research" focuses on the biopharmaceutical industry, publishing articles that meet the needs of researchers and applied statisticians. We welcome academic biostatisticians from medical schools, veterinary schools, schools of public health, and schools of pharmacy, as well as statisticians and quantitative analysts working in regulatory agencies such as the U.S. Food and Drug Administration and its counterparts in other countries. Additionally, we encourage statisticians interested in applying the methods presented in this journal to their own fields, as well as non-statisticians who are interested in statistical methods applied to biopharmaceutical issues.

《Statistics in Biopharmaceutical Research》期刊专注于生物制药行业的研究，发表满足研究人员和应用统计学家需求的文章。我们欢迎来自医学院、兽医学院、公共卫生学院和药学院的学术生物统计学家，以及在监管机构（如美国食品和药物管理局及其国际对应机构）工作的统计学家和定量分析师。此外，我们也鼓励有兴趣将本期刊中介绍的方法应用于自身领域的统计学家，以及对生物制药问题中的统计方法感兴趣的非统计学家投稿。

Statistics in Biopharmaceutical Research

The progression of Alzheimer's disease (AD) is a continuous process in cognitive and biomarker changes, with only a fraction of mild cognitive impairment (MCI) patients eventually advancing to AD. There is no definite biomarker signature to determine this eventual progress. The discovery and development of prognostic biomarker signatures for AD is essential to address the challenges of AD drug discovery and development. The deep learning (DL) technique is a recent breakthrough in data science that enables researchers to discover previously unknown features comprised by complicated patterns learnt from large datasets. It has outperformed many traditional machine learning methods in computer vision tasks. In this article, we evaluated the performance of DL algorithms in differentiating patients with diagnosis of AD, MCI, or no evidence of dementia using baseline MRI data of the brain, and integrated features extracted from the neural network with other baseline biomarkers to develop an AD prognostic signature. This signature can be used to understand the patient heterogeneity in a study cohort and provide enrichment strategies for AD clinical trial design.

Liuqing Yang

Xifeng Wang

Qi Guo

Scott Gladstein

Dustin Wooten

Tengfei Li

Weining Z Robieson

Yan Sun

Xin Huang

Deep Learning Based Multimodal Progression Modeling for Alzheimer’s Disease

基于深度学习的多模态阿尔茨海默病进展建模

Minjeong Park

Yeonhee Park

H.M. James Hung

Distribution of a P-Value When the Alternative Hypothesis is True for Binary Outcomes

二元结果下备择假设为真时P值的分布

Surrogate endpoints are pivotal in clinical trials, replacing true endpoints that may be challenging to measure. However, their imprecise association with true endpoints can lead to biased results, necessitating validation through trials. This paper proposes a bias-corrected group sequential design in the presence of surrogate endpoints to address these challenges. We introduce bias-corrected test statistics tailored to scenarios where both true and surrogate endpoints are available or where true endpoints are limited. Our approach is exemplified through a simulation study and a case study redesign of the Pamoja Tulinde Maisha (PALM) trial, showcasing its practical implementation and benefits. The proposed design enhances the accuracy of interim analyses by mitigating biases associated with surrogate endpoints. Through rigorous evaluation and comparison with alternative designs, we demonstrate the effectiveness of the proposed methods.

Bias-corrected group sequential design in the presence of surrogate endpoints with application to PALM trial

存在替代终点的偏差校正组序贯设计及其在PALM试验中的应用

We present a unified three-state model (TSM) framework for evaluating treatment effects in clinical trials in the presence of treatment crossover. Researchers have proposed diverse methodologies to estimate the treatment effect that would have hypothetically been observed if treatment crossover had not occurred. However, there is little work on understanding the connections between these different approaches from a statistical point of view. The proposed TSM framework unifies existing methods, effectively identifying potential biases, model assumptions, and inherent limitations for each method. This can guide researchers in understanding when these methods are appropriate and choosing a suitable approach for their data. The TSM framework also facilitates the creation of new methods to adjust for confounding effects from treatment crossover. To illustrate this capability, we introduce a new imputation method that falls under its scope. Through simulation experiments, we demonstrate the performance of different approaches for estimating the treatment effects. Codes for implementing the methods within the TSM framework are available at https://github.com/JasonZhao111/TSM.

Zile Zhao

Ye Li

Xiaodong Luo

Ray Bai

A Unified Three-State Model Framework for Analysis of Treatment Crossover in Survival Trials

生存试验中治疗交叉分析的统一三态模型框架

Wim Van der Elst

Fenny Ong

Florian Stijven

Ariel Alonso Abad

Ingrid Van Keilegom

Helena Geys

Lewin Eisele

Geert Molenberghs

Multiple surrogates in the meta-analytic setting for normally distributed endpoints

元分析设置中正态分布终点的多重替代方法

tables of COVID-19 deaths classified by two comorbidities, for example, diabetes and hypertension. We take the marginal tables of comorbidities as multinomial with table-specific cell probabilities drawn from a Dirichlet distribution. Conditionally hereon, we model the death counts for cell in the 2×2

Amy Racine-Poon

Jens Praestgaard

Oleksandr Sverdlov

Random Effects Meta-Analysis of Contingency Tables with Complete and Partially Complete Data, with Application to COVID-19 Research

完全和部分完整数据的列联表随机效应Meta分析及其在COVID-19研究中的应用

Abbreviation	Stat. Biopharm. Res.
Journal Impact	1.62
Quartiles(Global)	STATISTICS & PROBABILITY(Q2)

ISSN	1946-6315
h-index	25

Publication Information	Publisher: Taylor and Francis Ltd.，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2011，Proportion of original research papers: 98.00%，Self Citation Rate:26.70%， Gold OA Rate: 14.02%
Average review cycle	期刊官网数据：平均Submission to first decision: 65 days; Submission to first post-review decision: 101 days; Acceptance to online publication: 48 days;网友分享经验：>12周，或约稿
Average recruitment ratio	网友分享经验：41%

Statistics in Biopharmaceutical Research

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Deep Learning Based Multimodal Progression Modeling for Alzheimer’s Disease

Distribution of a P-Value When the Alternative Hypothesis is True for Binary Outcomes

Bias-corrected group sequential design in the presence of surrogate endpoints with application to PALM trial

A Unified Three-State Model Framework for Analysis of Treatment Crossover in Survival Trials

Multiple surrogates in the meta-analytic setting for normally distributed endpoints

Distribution of a P-Value When the Alternative Hypothesis is True for Binary Outcomes

Bias-corrected group sequential design in the presence of surrogate endpoints with application to PALM trial

A Unified Three-State Model Framework for Analysis of Treatment Crossover in Survival Trials

Random Effects Meta-Analysis of Contingency Tables with Complete and Partially Complete Data, with Application to COVID-19 Research

Multiple surrogates in the meta-analytic setting for normally distributed endpoints