Statistics in Medicine最新文献

{"title":"Using Joint Models in Phase I Dose-Finding Designs in Oncology: Considerations for Frequentist Approaches.","authors":"Xijin Chen, Pavel Mozgunov, Richard D Baird, Thomas Jaki","doi":"10.1002/sim.70758","DOIUrl":"10.1002/sim.70758","url":null,"abstract":"<p><p>Dose-finding trials for oncology studies are traditionally designed to assess safety in the early stages of drug development. With the rise of molecularly targeted therapies and immuno-oncology compounds, biomarker-driven approaches are increasingly importance. In this article, we propose a novel approach to dose-finding that incorporates biomarkers which predict for the subsequent development of clinical toxicity. The proposed joint model framework, which incorporates repeated biomarker measurements, is compared to the likelihood-based continual reassessment method (CRM) using only binary toxicity data, across various dose-toxicity relationship scenarios. Our findings highlight a critical limitation of likelihood-based approaches in early-phase dose-finding studies with small sample sizes: estimation challenges that have been previously overlooked in the phase I dose-escalation setting. We explore potential remedies to address these challenges and emphasize the appropriate use of likelihood-based methods. Simulation results demonstrate that the proposed joint model framework, by integrating biomarker information, can alleviate estimation problems in the likelihood-based CRM and improve the proportion of correct selection. However, we highlight that the inherent data limitations in early phase dose-finding studies remain a significant challenge that cannot be fully overcome in the frequentist framework.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70758"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13659646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184311","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}

{"title":"A Max 0-1 Weighted Log-Rank Test for Time-to-Event Data With Random Delayed Treatment Effect.","authors":"Jiarui Liu, Chenghao Chu, Bingming Yi, Ming-Hui Chen","doi":"10.1002/sim.70773","DOIUrl":"10.1002/sim.70773","url":null,"abstract":"<p><p>In a cancer clinical trial with an immunotherapy, the treatment often becomes effective after a certain delay time. It is well known that traditional log-rank test no longer provides optimal power in this case. There has been a great interest to search for a more powerful test to detect a truly effective immunotherapy treatment. In this paper, we propose a new testing method based on the maximum value of a set of 0-1 weighted log-rank test statistics and establish useful theoretical results of the relevant test statistics. Extensive simulation studies are conducted to examine the empirical performance of the proposed method. An analysis of the real data from a non-small-cell lung cancer clinical trial is also carried out to further demonstrate the usefulness of the proposed test method. Trial Registration: ClinicalTrials.gov ID: NCT01903993, NCT02008227.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70773"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149207990","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}

{"title":"Adaptive Trial Designs for Assessing Predictiveness of a Biomarker in Early Phase Drug Development.","authors":"Libby Daniells, Julia Geronimi, Hugo Hadjur, Sandrine Guilleminot, Pavel Mozgunov","doi":"10.1002/sim.70719","DOIUrl":"10.1002/sim.70719","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Biomarker identification plays a crucial role in precision medicine, where treatments are tailored to patients' intrinsic factors. Predictive biomarkers can be used to identify patients who are more likely to benefit from a treatment. However, identifying and validating such biomarkers pose substantial statistical and practical challenges, especially in the small sample setting. Several methods have been proposed for assessing whether a biomarker has a predictive effect, including the Average Kolmogorov-Smirnov Approach (AKSA), which has been shown to have improved operating characteristics for inference compared to alternative approaches. Yet power remains limited when sample sizes are small and unbalanced across treatment arms. Power can be improved through adaptive enrichment of the sample size, especially when treatment arms are small and unbalanced. We propose an adaptive design to improve power for detecting a predictive effect when sample sizes are small. Existing adaptive biomarker designs largely focus on enrichment of the trial, for example, adjusting enrolment criteria based on an assessment of the treatment-biomarker interaction at interim, or refining the biomarker threshold used to segregate the population into biomarker positive or negative (i.e., those patients more or less likely to respond to the treatment). Few designs explicitly address the task of formally declaring a predictive effect of a biomarker using an interim analysis. The interim analysis framework enables a more reliable assessment of a biomarker's predictive effect by introducing a pre-planned evaluation at the originally intended sample size of a study which may otherwise be underpowered due to the limited number of patients. Based on the interim results, the trial may (i) already have sufficient evidence to declare a biomarker as predictive, (ii) be terminated early due to a clear lack of evidence for a predictive effect, or (iii) enter an \"expansion phase\", where additional cohort of patients are recruited to improve the power for final analysis. The proposed framework addresses key methodological challenges, including interim decision rules, determining the magnitude and allocation of post-interim sample size increases, controlling the Type I error rate, and achieving meaningful gains in statistical power at the final analysis. The impact of an unknown biomarker distribution is also considered. These challenges are explored through simulation studies, where properties of the interim and final analyses are presented under various design configurations (where the decision bounds and size of the expansion are altered) and scenarios, in which the true predictive and prognostic effects of the biomarker are varied. Evaluated metrics include statistical power, Type I error control, and interim decision characteristics. The results inform the design of trials incorporating interim analyses to assess the predictive effect of a biomarker, enabling statistically eff","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70719"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148906484","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}

{"title":"A Group Structure Guided Ultra-High Dimensional Feature Screening for Survival Outcome.","authors":"Jie He, Yiyu Li, Yan Hou","doi":"10.1002/sim.70725","DOIUrl":"10.1002/sim.70725","url":null,"abstract":"<p><p>With the rapid advancement of high-throughput technologies, feature screening methods have attracted increasing attention for analyzing ultra-high-dimensional data. Motivated by the widespread availability of meaningful grouping structures in biomedical applications, such as brain imaging and gene expression studies, we propose a novel group-structure-guided (GSG) feature screening method for survival outcomes. The proposed approach incorporates prior grouping information among predictors and accommodates both disjoint and overlapping group structures. Furthermore, a combined GSG (C-GSG) extension is developed to integrate multiple grouping schemes. We establish the sure screening properties of the proposed procedures and demonstrate through extensive simulation studies that incorporating informative group structures can improve screening performance, particularly under challenging censoring scenarios. The proposed method is further applied to a TCGA breast cancer dataset, where pathway information is utilized to identify genes associated with patient survival outcomes.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70725"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13547442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148901253","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}

{"title":"Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference.","authors":"Andy A Shen, Eli Ben-Michael, Avi Feller, Luke Keele, Jared Murray","doi":"10.1002/sim.70720","DOIUrl":"10.1002/sim.70720","url":null,"abstract":"<p><p>While balancing covariates between groups is central for observational causal inference, selecting which features to balance remains a challenging problem. Kernel balancing is a promising approach that first estimates a kernel that captures similarity across units and then balances a (possibly low-dimensional) summary of that kernel, indirectly learning important features to balance. In this paper, we propose forest kernel balancing, which leverages the underappreciated fact that tree-based machine learning models, namely random forests and Bayesian additive regression trees (BART), implicitly estimate a kernel based on the co-occurrence of observations in the same terminal leaf node. Thus, even though the resulting kernel is solely a function of baseline features, the selected nonlinearities and other interactions are important for predicting the outcome-and therefore are important for addressing confounding. Through simulations and applied illustrations, we show that forest kernel balancing leads to meaningful computational and statistical improvement relative to standard kernel methods, which do not incorporate outcome information when learning features.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70720"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13520828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148832175","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}

{"title":"A Neutral Comparison of Multivariate Hypothesis Tests for Parallel Group Designs in Rare Disease Settings.","authors":"Martin Geroldinger, Martin Laimer, Verena Wally, Johann W Bauer, Georg Zimmermann","doi":"10.1002/sim.70735","DOIUrl":"10.1002/sim.70735","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Multivariate endpoints are increasingly used in clinical studies on rare diseases, where sample sizes tend to be small and distributional assumptions are often violated. In such settings, commonly applied parametric multivariate methods, such as classical MANOVA approaches, may suffer from inflated type-I error or reduced statistical power. Therefore, a neutral simulation-based comparison of four methodologically different approaches for the analysis of multivariate outcomes in parallel group designs was conducted. The four approaches included the nonparametric R package npmv (using permutation and randomization resampling for small samples), a semiparametric MANOVA approach from the R package MANOVA.RM with both a Wald-type statistics (WTS) and a modified ANOVA-type statistics (MATS) using all available variants including also bootstrap approaches, a classical parametric MANOVA, and multiple univariate Mann-Whitney tests with Holm correction. The simulation framework was informed by empirical distributions derived from anonymized clinical data and clinical expert guidance. Two clinically realistic effect types were mainly evaluated: (i) percent-responder effects for wound size reduction and (ii) additional location shifts in pain and pruritus visual analog scales (VAS) scores. In addition, scenarios are examined in which classical assumptions such as multivariate normality and homogeneity of covariance matrices are violated. The sample sizes considered ranged from 12 to 360 subjects. Both balanced and unbalanced designs were evaluated, with the objective of illustrating potential limitations of methods when applied to small samples. Outcomes were assessed in terms of type-I error control and statistical power. Overall, npmv, the MATS using especially parametric bootstrap within the semiparametric MANOVA.RM package maintained reliable type-I error control, whereas the semiparametric MANOVA.RM approach with the WTS using its asymptotic approximation showed very liberal type-I error behavior to almost no type-I error control in small sample scenarios, and especially in unbalanced settings. Bootstrap approaches especially of the WTS substantially improved type-I error control compared with the asymptotic WTS. The nonparametric npmv approach achieved good power values, especially in small samples, as did the modified ATS approach using parametric bootstrap from the MANOVA.RM package, which achieved very similar results. The univariate Mann-Whitney U test with Holm correction tended to yield conservative results for small samples; however, despite the univariate nature of this approach, it achieved a high and comparable level of statistical power in many scenarios. Classical MANOVA also yielded good results in certain scenarios; however, it exhibited rather liberal type-I error rates, primarily in heteroscedastic scenarios and unbalanced designs, which can be explained by a violation of the assumption of homoscedastic covariance matrices. These ","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70735"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13593806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149017404","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}

{"title":"An Assessment of Reliability Estimation Methods for Binomial Health Care Quality Measures.","authors":"Laura Aume, William White, Po-Hsu Chen, Jeffrey Geppert","doi":"10.1002/sim.70729","DOIUrl":"10.1002/sim.70729","url":null,"abstract":"<p><p>We evaluated the performance of commonly used methods for estimating the reliability of binomial health care quality measures using simulated datasets spanning a range of performance score means and variances, numbers of entities, and patient sample sizes. For each simulation, reliability was estimated for all selected methods and compared with the known true reliability derived from the simulation parameters, with methods assessed on their accuracy and precision. Logistic regression with reliability estimated on the outcome scale demonstrated the highest accuracy and precision among all methods evaluated. The widely used Adams beta-binomial method performed poorly, although a modification recommended by Nieser and Harris substantially improved its performance. These approaches are applicable only to binomial measures. Among methods that can be applied to both binomial and continuous measures, permutation resampling of the Spearman rank correlation coefficient was the most accurate and precise, outperforming other commonly used approaches. Overall, for binomial quality measures, logistic regression on the outcome scale is the preferred method for reliability estimation, followed closely by the modified beta-binomial approach, while for non-binomial measures, permutation-based Spearman rank correlation appears to be the most suitable method.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70729"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13507660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148819465","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}

{"title":"Bayesian Additive Regression Trees for Modeling Multiple Exposures With Measurement Error.","authors":"Madeleine E St Ville, Yaeji Lim, Ruijin Lu, Katherine L Grantz, Zhen Chen","doi":"10.1002/sim.70739","DOIUrl":"https://doi.org/10.1002/sim.70739","url":null,"abstract":"<p><p>We develop a Bayesian Additive Regression Trees with Measurement Error (BART-ME) model for flexibly estimating exposure-response functions when multiple covariates are measured with classical error. Unlike existing approaches, BART-ME accommodates nonlinearities, interactions, correlated exposures, and correlated measurement errors, while exploiting replicate measurements to estimate the error variance-covariance structure. Posterior inference is obtained via a Metropolis-within-Gibbs algorithm, yielding estimates of exposure-response functions, variable importance, and measurement reliability. Simulation studies demonstrate that BART-ME reduces bias and improves coverage compared with Naïve BART analyses that ignore measurement error, even with sparse replication. In an application to first-trimester ultrasound data from the NICHD Fetal Growth Studies, BART-ME identified crown-rump length as the most reliable predictor of gestational age at delivery and revealed nonlinear patterns attenuated by Naïve methods. These results illustrate the potential of BART-ME as a general framework for correcting measurement error in complex exposure settings.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70739"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149002577","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}

{"title":"Bias in Meta-Analytic Modeling of Surrogate Endpoints in Cancer Screening Trials.","authors":"James P Long, Abhishikta Roy, Ehsan Irajizad, Kim-Anh Do, Yu Shen","doi":"10.1002/sim.70731","DOIUrl":"10.1002/sim.70731","url":null,"abstract":"<p><p>In meta-analytic modeling, the functional relationship between a primary and surrogate endpoint is estimated using summary data from a set of completed clinical trials. Parameters in the meta-analytic model are used to assess the quality of the proposed surrogate. Recently, meta-analytic models have been employed to evaluate whether late-stage cancer incidence can serve as a surrogate for cancer mortality in cancer screening trials. A major challenge in meta-analytic models is that uncertainty of trial-level estimates affects the evaluation of surrogacy, since each trial provides only estimates of the primary and surrogate endpoints rather than their true parameter values. In this work, we show via simulation and theory that trial-level estimate uncertainty may bias the results of meta-analytic models toward positive findings of the quality of the surrogate. We focus on cancer screening trials and the late-stage incidence surrogate. We reassess correlations between primary and surrogate endpoints in Ovarian cancer screening trials. Our findings indicate that completed trials provide limited information regarding quality of the late-stage incidence surrogate. These results support restricting meta-analytic regression usage to settings where trial-level estimate uncertainty is incorporated into the model.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70731"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13549800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148906425","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}

{"title":"Hybrid Non-Informative and Informative Prior Model-Assisted Designs for Mid-Trial Dose Insertion.","authors":"Masahiro Kojima, Kana Yamada, Hisato Sunami, Kentaro Takeda, Keisuke Hanada","doi":"10.1002/sim.70733","DOIUrl":"10.1002/sim.70733","url":null,"abstract":"<p><p>In oncology phase I trials, model-assisted designs have been increasingly adopted because they enable adaptive yet operationally simple dose adjustment based on accumulating safety data, leading to a paradigm shift in dose-escalation methodology. In practice, a single mid-trial dose insertion may be considered to examine safer doses and/or to collect more informative efficacy data. In this study, we investigate methods to improve dose assignment and the selection of the maximum tolerated dose (MTD) or the optimal biological dose (OBD) when a new dose level is added during an ongoing trial under a model-assisted framework, by assigning informative prior information to the inserted dose. We propose a hybrid design that uses a non-informative model-assisted design at trial initiation and, upon dose insertion, applies an informative-prior extension only to the newly added dose. In addition, to address potential skeleton misspecification, we propose two adaptive extensions: (i) an online-weighting approach that updates the skeleton over time, and (ii) a Bayesian-mixture approach that robustly combines multiple candidate skeletons. We evaluate the proposed methods through simulation studies.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70733"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13555521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148913539","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}