Statistical Methods in Medical Research最新文献_第8页

When randomization is not random: Allocation bias in small sample, group sequential randomized clinical trials. 当随机化不是随机化时：在小样本、组序随机临床试验中的分配偏倚。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-04-29 DOI: 10.1177/09622802261442914

Daniel Bodden, Ralf-Dieter Hilgers, Franz König

{"title":"When randomization is not random: Allocation bias in small sample, group sequential randomized clinical trials.","authors":"Daniel Bodden, Ralf-Dieter Hilgers, Franz König","doi":"10.1177/09622802261442914","DOIUrl":"10.1177/09622802261442914","url":null,"abstract":"Even in rare diseases, where the sample size is limited and blinding is less frequently implemented, randomized controlled trials are considered the gold standard to prove efficacy. Randomization is used to mitigate bias and regulatory guidance recommend the investigation of the impact of bias on the test decision. We quantified how allocation bias affects the test decision in small-sample two-arm group sequential trials under a biasing policy based on the Blackwell-Hodges convergence strategy. Type I error and power were evaluated under Lan-DeMets spending (Pocock-, O'Brien-Fleming-, Wang-Tsiatis-type functions), with and without futility (non-binding, binding), varying interim timing, number of looks and stage-wise restarting of randomization. Allocation bias inflated type I error most for more restrictive randomization procedures, especially permuted blocks with small block sizes. Spending more alpha at interim reduced inflation. Non-binding futility reduced type I error, while binding increased type I error inflation for more aggressive stopping boundaries. Stage-wise restarting modestly reduced inflation for most procedures. Overall, group sequential choices had secondary effect and did not rescue a predictable randomization scheme. When allocation bias cannot be ruled out (e.g. open-label trials), we recommend less restrictive randomization procedures (e.g. big stick design) or, if using permuted blocks, large block sizes.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1295-1309"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs. 使用基于神经网络的建模和复杂的调查设计在美国人群中筛查糖尿病。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-05-07 DOI: 10.1177/09622802261442893

Marcos Matabuena, Juan C Vidal, Rahul Ghosal, Jukka-Pekka Onnela

{"title":"Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs.","authors":"Marcos Matabuena, Juan C Vidal, Rahul Ghosal, Jukka-Pekka Onnela","doi":"10.1177/09622802261442893","DOIUrl":"10.1177/09622802261442893","url":null,"abstract":"Complex survey designs are widely used in medical cohort studies. Developing risk score models that adequately account for the sampling design is essential to minimize selection bias and obtain representative population estimates. This work addresses three complementary objectives. First, we propose a general predictive framework for regression and classification tasks that utilizes neural networks to incorporate survey weights into the model estimation process. Second, we introduce a procedure for quantifying prediction uncertainty based on conformal inference, adapted to the characteristics of complex survey data. Third, we demonstrate the application of the proposed methodology in a case study assessing the risk of diabetes mellitus in the US population, using the NHANES 2011-2014 cohort. The empirical results show that models of varying complexity, each using different sets of predictors, achieve different trade-offs between predictive performance and economic cost while maintaining generalizability at the population level. Although the case study focuses on diabetes, the proposed framework is directly applicable to the development of clinical prediction models for other diseases and complex survey datasets. All software and data used in this study are publicly available on GitHub.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1257-1280"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A quantile cure model with partially functional covariate effects. 具有部分泛函协变量效应的分位数治愈模型。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-05-23 DOI: 10.1177/09622802261445414

Chyong-Mei Chen, Yingwei Peng

{"title":"A quantile cure model with partially functional covariate effects.","authors":"Chyong-Mei Chen, Yingwei Peng","doi":"10.1177/09622802261445414","DOIUrl":"10.1177/09622802261445414","url":null,"abstract":"Quantile regressions offer several attractive features, including the ability to allow covariate effects to vary at different quantile levels and to effectively handle heteroscedasticity in data, which makes it a viable alternative for analyzing data with continuous outcomes in recent years. It has been used in modeling survival data with and without a cured fraction. In this work, we propose novel estimating equation approaches to estimate a mixture cure model where the latency survival time is modeled using a quantile regression. Our proposed estimation methods provide double robustness, meaning that a misspecification in one part of the mixture cure model will not affect the estimation in the other part. The methods relax the restrictive global log-linear assumption that is typically found in existing quantile regressions, and they allow for both quantile-varying and quantile-invariant effects in the regression when the log-linear assumption holds within a certain range of quantile levels. We established the asymptotic properties of the proposed estimators, and our simulation studies demonstrated their double robustness and efficiency gains. An application of the proposed model and methods to data from a lung cancer study revealed that uncured patients with adenocarcinoma have significantly longer quantiles in the survival time than uncured patients with squamous cell carcinoma, which had not been reported in previous analyses of the data due to the limitations of the existing methods.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1310-1324"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13283496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148006753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Sensitivity bounds for bias in hazard ratios: A causal hazard perspective. 风险比偏倚的敏感性界限：因果风险视角。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-04-02 DOI: 10.1177/09622802261436811

Yan-Lin Chen, Hsiang-Hsi Hung, Sheng-Hsuan Lin

{"title":"Sensitivity bounds for bias in hazard ratios: A causal hazard perspective.","authors":"Yan-Lin Chen, Hsiang-Hsi Hung, Sheng-Hsuan Lin","doi":"10.1177/09622802261436811","DOIUrl":"10.1177/09622802261436811","url":null,"abstract":"The Cox proportional hazards model has popularized the conventional hazard ratio as a standard measure for assessing the effect of exposure on time-to-event outcomes. However, as noted in Hernán's influential critique, interpreting the hazard ratio as a causal effect can introduce substantial selection bias. While Hernán's work discusses this issue qualitatively, it lacks a method to quantify the extent of bias or the strength an unmeasured confounder would need to account for it. Our study fills this gap by proposing a nonparametric sensitivity analysis framework, inspired by Ding and VanderWeele, which quantifies \"how hazardous the hazard ratio is.\" Using the causal hazard ratio proposed by Aalen as the \"true hazard ratio,\" we derive an upper bound for the bias in the conventional hazard ratio (termed HR bias) in both discrete and continuous time settings, demonstrating that this bias accumulates over time. Additionally, we derive the E-value for HR bias, allowing researchers to evaluate whether unmeasured confounding alone could fully explain the conventional hazard ratio under a certain level of the causal hazard ratio. This approach provides researchers with a robust sensitivity analysis framework, reducing reliance on restrictive assumptions and enhancing the reliability of causal interpretations in survival analysis.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1166-1176"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147610052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Beyond weighting: Propensity score modeling for causal inference. 超越加权：因果推理的倾向得分模型。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-04-08 DOI: 10.1177/09622802261436960

Rong J B Zhu

{"title":"Beyond weighting: Propensity score modeling for causal inference.","authors":"Rong J B Zhu","doi":"10.1177/09622802261436960","DOIUrl":"10.1177/09622802261436960","url":null,"abstract":"Propensity score weighting is a common method in causal inference methods. However, this approach faces two well-known challenges: (i) high variance due to small probability values in the denominator, and (ii) sensitivity to model specification errors when estimating propensity scores in observational studies. In this article, we establish that the expected potential outcomes, conditional on the propensity score, can be identified as a function of the propensity score. Based on this identification result, we utilize spline regression to estimate the function. Treatment effect estimation and inference are derived from the asymptotic normality of the spline regression. Our approach preserves the low bias of inverse probability weighting (IPW), benefiting from the flexibility of nonparametric models, while achieving significantly lower variance due to its model-based stability. Furthermore, we extend this method to regression-based adjustment for improved efficiency in causal inference. Extensive simulations show that our approach achieves lower variance than IPW-based methods while maintaining low bias and robustness to propensity score misspecification. A real-data application further demonstrates its reliability in inference.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1199-1214"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

The applicability to systematic reviews of common effect, random effects and fixed effects approaches to meta-analysis. 在meta分析中对共同效应、随机效应和固定效应方法的系统评价的适用性。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-04-16 DOI: 10.1177/09622802261439260

Richard J Stevens

{"title":"The applicability to systematic reviews of common effect, random effects and fixed effects approaches to meta-analysis.","authors":"Richard J Stevens","doi":"10.1177/09622802261439260","DOIUrl":"10.1177/09622802261439260","url":null,"abstract":"Systematic reviewers planning quantitative meta-analysis usually choose between fixed effect meta-analysis, and random effects meta-analysis. An alternative method is called fixed effects (note the s in the name). This method has the unique property that the target estimand is defined by the variances of the studies found by the systematic review. This article considers each in relation to the quantitative analysis of data to be obtained by systematic review. For clarity, I refer to the traditional fixed effect method as the common effect method and the newer approach as fixed effects (plural). Case studies illustrate the post hoc nature of the fixed effects (plural) method, in which the population under study is determined by the data rather than by the protocol. Mathematical analysis shows that unlike common effect and random effects methods, the fixed effects (plural) method requires an additional, and unrealistic, assumption about the data obtained in systematic reviews. A simulation study demonstrates that confidence intervals from fixed effects (plural) meta-analysis do not account for the post hoc nature of the method. Fixed effects (plural) meta-analysis is neither a slot-in replacement for the common effect method nor for the random effects method of meta-analysis. Of the three methods considered here, the common effect method and the random effects method are potentially valid for the quantitative analysis of systematic reviews.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1243-1256"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13283507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

PRO-ADD: Patient-empowered dose-finding trials integrating safety, preliminary efficacy and patient-reported outcomes for optimal dose selection. PRO-ADD：患者授权的剂量寻找试验，综合安全性、初步疗效和患者报告的最佳剂量选择结果。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-04-29 DOI: 10.1177/09622802261435969

Emily Alger, Sumithra J Mandrekar, Jun Yin, Christina Yap

{"title":"PRO-ADD: Patient-empowered dose-finding trials integrating safety, preliminary efficacy and patient-reported outcomes for optimal dose selection.","authors":"Emily Alger, Sumithra J Mandrekar, Jun Yin, Christina Yap","doi":"10.1177/09622802261435969","DOIUrl":"10.1177/09622802261435969","url":null,"abstract":"Advances in oncology drug development are driving the emergence of novel therapies, challenging traditional dose-efficacy assumptions in dose-finding oncology trials. Traditional trial designs aim to identify a maximum tolerated dose (MTD) by assessing patients' dose-limiting toxicities (DLTs) - adopting traditional dose-efficacy paradigms that efficacy increases with treatment dose. With these new therapies in mind, emphasis should shift toward methodological advancements in trial designs aimed at identifying optimal doses, rather than solely determining MTDs. Incorporating patient-reported outcomes (PROs) within dose-finding oncology trials is increasingly recommended to better understand treatments' tolerability profiles, especially given the extended tolerability assessment windows for novel immunotherapies and targeted therapies. This article introduces PRO-ADD (Patient-Reported Outcomes Aided Dose-optimisation Design), a modular trial design framework for dose-optimisation. We leverage this framework to optimise dosage with respect to three key outcomes - clinician-assessed DLTs, PROs and preliminary efficacy. PRO-ADD performs well at identifying the optimal dose (both efficacious and tolerable) - successfully identifying the most tolerable effective dose and avoiding escalation to larger, safe doses offering no additional efficacy benefit. As the field evolves, patient-centric dose-finding approaches incorporating PROs are crucial in advancing our understanding of treatment tolerability, and in turn, shaping the future landscape of dose-finding oncology trials.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1177-1198"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13283501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A flexible semiparametric approach for robust causal inference with invalid instruments and unmeasured confounder. 一种灵活的半参数方法，用于具有无效仪器和未测量混杂因素的稳健因果推理。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-04-12 DOI: 10.1177/09622802261439252

Yunlong Cao, Yuquan Wang, Dapeng Shi, Dong Chen, Yue-Qing Hu

{"title":"A flexible semiparametric approach for robust causal inference with invalid instruments and unmeasured confounder.","authors":"Yunlong Cao, Yuquan Wang, Dapeng Shi, Dong Chen, Yue-Qing Hu","doi":"10.1177/09622802261439252","DOIUrl":"10.1177/09622802261439252","url":null,"abstract":"Inferring causal effects with unmeasured confounder is a main challenge in causal inference. Many researchers impose parametric assumptions on the distribution of unmeasured confounder. However, due to the unobservable nature of the unmeasured confounder, it is more reasonable to leave its distribution unrestricted. Another key challenge in causal inference is the involvement of invalid instrumental variables, which may lead to biased inference and potentially deceptive results. To this end, we employ a flexible semiparametric model that allows for possibly invalid instruments without specifying the distribution of unmeasured confounder in this work. A penalized semiparametric estimator for causal effects is constructed and its oracle and asymptotic properties are well established for statistical inference. We evaluate the performance of the estimator through simulation studies, revealing that our proposed estimator exhibits asymptotic unbiasedness and robustness in estimating causal effects, along with consistent selection of invalid instruments. We also demonstrate its application using Atherosclerosis Risk in Communities Study data set, which further validates its robustness in the presence of invalid instruments.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1230-1242"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147676772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Quantile inference for multivariate response regression in joint modeling of longitudinal and survival data. 纵向和生存数据联合建模中多变量响应回归的分位数推断。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-05-16 DOI: 10.1177/09622802261420698

Xiaoyu Niu, Xuejing Zhao

{"title":"Quantile inference for multivariate response regression in joint modeling of longitudinal and survival data.","authors":"Xiaoyu Niu, Xuejing Zhao","doi":"10.1177/09622802261420698","DOIUrl":"10.1177/09622802261420698","url":null,"abstract":"Quantile regression (QR) offers a robust framework for analyzing covariate effects across the outcome distribution, particularly when the response variable exhibits skewness or heavy tails. To jointly model multivariate longitudinal biomarkers and a time-to-event outcome, we propose a novel joint model based on linear QR mixed models. The longitudinal submodel accounts for associations among multivariate responses by assuming a multivariate asymmetric Laplace distribution for the errors, utilizing its location-scale mixture representation for computational efficiency. For the event-time process, a Cox proportional hazards model is employed, which incorporates shared random effects from the longitudinal submodel to link the two processes. Estimation is performed using a Monte Carlo expectation-maximization algorithm, with Metropolis-Hastings sampling to approximate the E-step. Comprehensive simulations under various quantile levels, censoring rates, error distributions, and correlation structures demonstrate that the proposed method yields accurate estimates for the regression parameters in both the longitudinal and survival submodels. Finally, we illustrate the practical utility of the model by analyzing a dataset from a primary biliary cirrhosis study.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1145-1165"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147960122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Empowering classification for multivariate functional data with simultaneous feature selection. 同时进行特征选择，增强多变量功能数据的分类能力。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-15 DOI: 10.1177/09622802261449366

Shuoyang Wang, Guanqun Cao, Yuan Huang

{"title":"Empowering classification for multivariate functional data with simultaneous feature selection.","authors":"Shuoyang Wang, Guanqun Cao, Yuan Huang","doi":"10.1177/09622802261449366","DOIUrl":"https://doi.org/10.1177/09622802261449366","url":null,"abstract":"The opportunity to utilize multivariate functional data types for conducting classification tasks is emerging with the growing availability of imaging data. Inspired by the extensive data provided by the Alzheimer's Disease Neuroimaging Initiative, we introduce a novel classifier tailored for multivariate functional data. Each observation in this framework may be associated with numerous functional processes, varying in dimensions, such as curves and images. Each predictor is a random element in an infinite-dimensional function space, and the number of functional predictors can potentially be much greater than the sample size. By adopting a sparse deep rectified linear unit network architecture and incorporating the LassoNet algorithm, the proposed functional Bayesian information criterion deep neural network performs feature selection and classification simultaneously, in contrast to existing functional data classifiers. This approach addresses the challenge of complex inter-correlation structures among multiple functional processes without requiring distributional assumptions. A simulation study and a real data application demonstrate its favorable performance.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261449366"},"PeriodicalIF":1.9,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147949415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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