Statistics in Medicine最新文献_第4页

Group Sequential Trial Design Using Stepwise Monte Carlo for Increased Flexibility and Robustness. 采用逐步蒙特卡罗方法的组序贯试验设计提高了灵活性和稳健性。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70249

Amitay Kamber, Elad Berkman, Tzviel Frostig, Raviv Pryluk, Bradley P Carlin

引用次数: 0

Causal Machine Learning Methods and Use of Cross-Fitting in Settings With High-Dimensional Confounding. 因果机器学习方法及交叉拟合在高维混杂环境中的应用。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70272

Susan Ellul, Stijn Vansteelandt, John B Carlin, Margarita Moreno-Betancur

{"title":"Causal Machine Learning Methods and Use of Cross-Fitting in Settings With High-Dimensional Confounding.","authors":"Susan Ellul, Stijn Vansteelandt, John B Carlin, Margarita Moreno-Betancur","doi":"10.1002/sim.70272","DOIUrl":"10.1002/sim.70272","url":null,"abstract":"Observational epidemiological studies commonly seek to estimate the causal effect of an exposure on an outcome. Adjustment for potential confounding bias in modern studies is challenging due to the presence of high-dimensional confounding, which occurs when there are many confounders relative to sample size or complex relationships between continuous confounders and exposure and outcome. Doubly robust methods such as Augmented Inverse Probability Weighting (AIPW) and Targeted Maximum Likelihood Estimation (TMLE) have the potential to address these challenges, using data-adaptive approaches and cross-fitting, but despite recent advances, limited evaluation and guidance are available on their implementation in realistic settings where high-dimensional confounding is present. Motivated by an early-life cohort study, we conducted an extensive simulation study to compare the relative performance of AIPW and TMLE using data-adaptive approaches for estimating the average causal effect (ACE). We evaluated the benefits of using cross-fitting with a varying number of folds, as well as the impact of using a reduced versus full (larger, more diverse) library in the Super Learner ensemble learning approach used for implementation. We found that AIPW and TMLE performed similarly in most cases for estimating the ACE, but TMLE was more stable. Cross-fitting improved the performance of both methods, but was more important for variance estimation and coverage than for point estimates, with the number of folds a less important consideration. Using a full Super Learner library was important to reduce bias and variance in complex scenarios typical of modern health research studies.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70272"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12457817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

What Is Fair? Defining Fairness in Machine Learning for Health. 什么是公平？定义健康机器学习中的公平性。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70234

Jianhui Gao, Benson Chou, Zachary R McCaw, Hilary Thurston, Paul Varghese, Chuan Hong, Jessica Gronsbell

引用次数: 0

Impact of Endpoint Delay on the Efficiency of Multi Arm Multi Stage Trials. 终点延迟对多臂多期试验效率的影响。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70245

Aritra Mukherjee, James M S Wason

{"title":"Impact of Endpoint Delay on the Efficiency of Multi Arm Multi Stage Trials.","authors":"Aritra Mukherjee, James M S Wason","doi":"10.1002/sim.70245","DOIUrl":"10.1002/sim.70245","url":null,"abstract":"Multi-arm multi-stage (MAMS) is an efficient class of trial designs that helps to assess multiple treatment strategies at the same time using an adaptive design. These designs can substantially reduce the average number of samples required compared to an equivalent single stage multi-arm trial. However, if patient recruitment is continued while we await treatment outcomes, a long-term primary outcome leads to a number of 'pipeline' patients getting recruited in the trial, who do not benefit from the early termination of a futile arm. This study focuses on quantifying the efficiency loss a MAMS design undergoes, in terms of the expected sample size (ESS), because of outcome delay. We first estimate the number of 'pipeline' patients (recruited during the interim analysis (IA) while awaiting outcome data) analytically through different recruitment models, given the total recruitment time. We then compute the ESS accounting for delay and assess the Efficiency Loss (EL). The results indicate that more than 50% of the expected efficiency gain is typically lost due to delay when the delay is more than <math> <semantics><mrow><mn>1</mn> <mo>/</mo> <mn>3</mn> <mtext>rd</mtext></mrow> <annotation>$$ 1/3mathrm{rd} $$</annotation></semantics> </math> of the total recruitment length. Although the number of stages have little influence on the efficiency loss, the timing of the IA can impact the efficiency of MAMS designs with delayed outcomes; in particular, conducting the IAs earlier than an equally-spaced design can be harmful for the design. Finally, we conclude that, in order to gain maximum benefit of MAMS in terms of a reduced sample size in multi-arm trials, the outcome delay should be less than a third of the total recruitment length.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70245"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12436084/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Letter to the Editors. 给编辑们的信。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70119

Saralees Nadarajah, Tibor K Pogány

引用次数: 0

Incorporating Heterogeneity in Mixed Hidden Markov Models With an Application to the Sleep-Wake Cycle. 混合隐马尔可夫模型的异质性及其在睡眠-觉醒周期中的应用。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70197

Jordan Aron, Paul S Albert, Mark B Fiecas

{"title":"Incorporating Heterogeneity in Mixed Hidden Markov Models With an Application to the Sleep-Wake Cycle.","authors":"Jordan Aron, Paul S Albert, Mark B Fiecas","doi":"10.1002/sim.70197","DOIUrl":"https://doi.org/10.1002/sim.70197","url":null,"abstract":"The sleep-wake cycle plays an important and far-reaching role in health. By utilizing personal physical activity monitors (PAMs), inferences about the sleep-wake cycle can be made. Hidden Markov models (HMMs) have been applied in this area as an accurate unsupervised approach. To account for heterogeneity in activity levels, we developed a mixed HMM that allows for individual differences. Through extensive simulations, we quantified the added gains relative to a standard HMM from using a mixed HMM to account for heterogeneity across several realistic scenarios. We found that mixed HMMs are often more accurate than standard HMMs when follow-up times are shorter. In situations with many repeated measurements per individual, a standard and mixed HMM have similar levels of accuracy, although a standard HMM is faster and easier to implement. Afterward, we applied our HMMs to actigraphy data from the National Health and Nutrition Examination Survey. We present results on sleep summary statistics by age and BMI. Summary statistics about the sleep-wake cycle extracted by the standard and mixed HMM were qualitatively similar. Differences in results, however, were likely driven by the heterogeneity in physical activity due to BMI and age, which we identified using a post hoc investigation of the data-driven clusters produced by the mixed HMM.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70197"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70255

Holger Dette, Lukas Koletzko, Frank Bretz

引用次数: 0

Analysis of Stepped-Wedge Cluster Randomized Trials When Treatment Effects Vary by Exposure Time or Calendar Time. 治疗效果随暴露时间或日历时间变化的楔形聚类随机试验分析。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70256

Kenneth M Lee, Elizabeth L Turner, Avi Kenny

{"title":"Analysis of Stepped-Wedge Cluster Randomized Trials When Treatment Effects Vary by Exposure Time or Calendar Time.","authors":"Kenneth M Lee, Elizabeth L Turner, Avi Kenny","doi":"10.1002/sim.70256","DOIUrl":"10.1002/sim.70256","url":null,"abstract":"Stepped-wedge cluster randomized trials (SW-CRTs) are traditionally analyzed with models that assume an immediate and sustained treatment effect. Previous work has shown that making such an assumption in the analysis of SW-CRTs when the true underlying treatment effect varies by exposure time can produce severely misleading estimates. Alternatively, the true underlying treatment effect might vary by calendar time. Comparatively less work has examined treatment effect structure misspecification in this setting. Here, we evaluate the behavior of the linear mixed effects model-based immediate treatment effect, exposure time-averaged treatment effect, and calendar time-averaged treatment effect estimators in different scenarios where these estimators are misspecified for the true underlying treatment effect structure. We show that the immediate treatment effect estimator is relatively robust to bias when estimating a true underlying calendar time-averaged treatment effect estimand. However, when there is a true underlying calendar (exposure) time-varying treatment effect, misspecifying an analysis with an exposure (calendar) time-averaged treatment effect estimator can yield severely misleading estimates which may converge to a value with the opposite sign of the true calendar (exposure) time-averaged treatment effect estimand. In this article, we highlight these two different time scales on which treatment effects can vary in SW-CRTs and clarify potential vulnerabilities that may arise when considering different types of time-varying treatment effects in a SW design. Accordingly, we emphasize the need for researchers to carefully consider whether the treatment effect may vary as a function of exposure time or calendar time in the analysis of SW-CRTs.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70256"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12459233/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Response to Letter to the Editor "Comments on 'Novel Non-Linear Models for Clinical Trial Analysis With Longitudinal Data: A Tutorial Using SAS for Both Frequentist and Bayesian Methods'". 对“纵向数据用于临床试验分析的新型非线性模型：频率和贝叶斯方法同时使用SAS教程”的评论”的回复

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70266

Guoqiao Wang, Guogen Shan, Yan Li, Yijie Liao, Lon Schneider, Gary Cutter

引用次数: 0

A Multi-Phenotype Approach to Joint Testing of Main Genetic and Gene-Environment Interaction Effects. 主要遗传和基因-环境互作效应联合检测的多表型方法。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70253

Saurabh Mishra, Arunabha Majumdar

{"title":"A Multi-Phenotype Approach to Joint Testing of Main Genetic and Gene-Environment Interaction Effects.","authors":"Saurabh Mishra, Arunabha Majumdar","doi":"10.1002/sim.70253","DOIUrl":"https://doi.org/10.1002/sim.70253","url":null,"abstract":"Gene-environment (GxE) interactions crucially contribute to complex phenotypes. The statistical power of a GxE interaction study is limited mainly due to weak GxE interaction effect sizes. Joint tests of the main genetic and GxE effects for a univariate phenotype were proposed to utilize the individually weak GxE effects to improve the discovery of associated genetic loci. We develop a testing procedure to evaluate combined genetic and GxE effects on multiple related phenotypes to enhance the power by merging pleiotropy in the main genetic and GxE effects. We base the approach on a general linear hypothesis testing framework for multivariate regression of continuous phenotypes. We implement the generalized estimating equations (GEE) technique under the seemingly unrelated regressions (SUR) setup for binary or mixed phenotypes. We use extensive simulations to show that the test for joint multi-phenotype genetic and GxE effects outperforms the univariate joint test of genetic and GxE effects and the test for multi-phenotype GxE effect concerning power when there is pleiotropy. The test produces a higher power than the test for the multi-phenotype marginal genetic effect for a weak genetic and substantial GxE effect. For more prominent genetic effects, the latter performs better with a limited increase in power. Overall, the multi-phenotype joint approach offers robust, high power across diverse simulation scenarios. We apply the methods to lipid phenotypes with sleep duration as an environmental factor in the UK Biobank. The proposed approach identified ten independent associated genetic loci missed by other competing methods. In a multi-phenotype analysis of apolipoproteins, ApoA1, and ApoB, our approach discovered two distinct loci considering sleep duration as the environmental factor.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70253"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0