Statistics in Medicine最新文献

{"title":"Statistical Inference for Counting Processes Under Shape Heterogeneity.","authors":"Ying Sheng, Yifei Sun","doi":"10.1002/sim.10280","DOIUrl":"10.1002/sim.10280","url":null,"abstract":"<p><p>Proportional rate models are among the most popular methods for analyzing recurrent event data. Although providing a straightforward rate-ratio interpretation of covariate effects, the proportional rate assumption implies that covariates do not modify the shape of the rate function. When the proportionality assumption fails to hold, we propose to characterize covariate effects on the rate function through two types of parameters: the shape parameters and the size parameters. The former allows the covariates to flexibly affect the shape of the rate function, and the latter retains the interpretability of covariate effects on the magnitude of the rate function. To overcome the challenges in simultaneously estimating the two sets of parameters, we propose a conditional pseudolikelihood approach to eliminate the size parameters in shape estimation, followed by an event count projection approach for size estimation. The proposed estimators are asymptotically normal with a root- <math> <semantics><mrow><mi>n</mi></mrow> <annotation>$$ n $$</annotation></semantics> </math> convergence rate. Simulation studies and an analysis of recurrent hospitalizations using SEER-Medicare data are conducted to illustrate the proposed methods.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5849-5861"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142676818","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":"The Win Ratio Approach in Bayesian Monitoring for Two-Arm Phase II Clinical Trial Designs With Multiple Time-To-Event Endpoints.","authors":"Xinran Huang, Jian Wang, Jing Ning","doi":"10.1002/sim.10282","DOIUrl":"10.1002/sim.10282","url":null,"abstract":"<p><p>To assess the preliminary therapeutic impact of a novel treatment, futility monitoring is commonly employed in Phase II clinical trials to facilitate informed decisions regarding the early termination of trials. Given the rapid evolution in cancer treatment development, particularly with new agents like immunotherapeutic agents, the focus has often shifted from objective response to time-to-event endpoints. In trials involving multiple time-to-event endpoints, existing monitoring designs typically select one as the primary endpoint or employ a composite endpoint as the time to the first occurrence of any event. However, relying on a single efficacy endpoint may not adequately evaluate an experimental treatment. Additionally, the time-to-first-event endpoint treats all events equally, ignoring their differences in clinical priorities. To tackle these issues, we propose a Bayesian futility monitoring design for a two-arm randomized Phase II trial, which incorporates the win ratio approach to account for the clinical priority of multiple time-to-event endpoints. A joint lognormal distribution was assumed to model the time-to-event variables for the estimation. We conducted simulation studies to assess the operating characteristics of the proposed monitoring design and compared them to those of conventional methods. The proposed design allows for early termination for futility if the endpoint with higher clinical priority (e.g., death) deteriorates in the treatment arm, compared to the time-to-first-event approach. Meanwhile, it prevents an aggressive early termination if the endpoint with lower clinical priority (e.g., cancer recurrence) shows deterioration in the treatment arm, offering a more tailored approach to decision-making in clinical trials with multiple time-to-event endpoints.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5922-5934"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11645213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710656","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":"Reinforced Borrowing Framework: Leveraging Auxiliary Data for Individualized Inference.","authors":"Ziyu Ji, Julian Wolfson","doi":"10.1002/sim.10267","DOIUrl":"10.1002/sim.10267","url":null,"abstract":"<p><p>Increasingly during the past decade, researchers have sought to leverage auxiliary data for enhancing individualized inference. Many existing methods, such as multisource exchangeability models (MEM), have been developed to borrow information from multiple supplemental sources to support parameter inference in a primary source. MEM and its alternatives decide how much information to borrow based on the exchangeability of the primary and supplemental sources, where exchangeability is defined as equality of the target parameter. Other information that may also help determine the exchangeability of sources is ignored. In this article, we propose a generalized reinforced borrowing framework (RBF) leveraging auxiliary data for enhancing individualized inference using a distance-embedded prior which uses data not only about the target parameter but also uses different types of auxiliary information sources to \"reinforce\" inference on the target parameter. RBF improves inference with minimal additional computational burden. We demonstrate the application of RBF to a study investigating the impact of the COVID-19 pandemic on individual activity and transportation behaviors, where RBF achieves 20%-40% lower MSE compared with existing methods.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5837-5848"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669202","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":"Response-Adaptive Randomization Procedure in Clinical Trials with Surrogate Endpoints.","authors":"Jingya Gao, Feifang Hu, Wei Ma","doi":"10.1002/sim.10286","DOIUrl":"10.1002/sim.10286","url":null,"abstract":"<p><p>In clinical trials, subjects are usually recruited sequentially. According to the outcomes amassed thus far in a trial, the response-adaptive randomization (RAR) design has been shown to be an advantageous treatment assignment procedure that skews the treatment allocation proportion to pre-specified objectives, such as sending more patients to a more promising treatment. Unfortunately, there are circumstances under which very few data of the primary endpoints are collected in the recruitment period, such as circumstances relating to public health emergencies and chronic diseases, and RAR is thus difficult to apply in allocating treatments using available outcomes. To overcome this problem, if an informative surrogate endpoint can be acquired much earlier than the primary endpoint, the surrogate endpoint can be used as a substitute for the primary endpoint in the RAR procedure. In this paper, we propose an RAR procedure that relies only on surrogate endpoints. The validity of the statistical inference on the primary endpoint and the patient benefit of this approach are justified by both theory and simulation. Furthermore, different types of surrogate endpoint and primary endpoint are considered. The results reassure that RAR with surrogate endpoints can be a viable option in some cases for clinical trials when primary endpoints are unavailable for adaptation.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5911-5921"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710726","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":"Powerful Test of Heterogeneity in Two-Sample Summary-Data Mendelian Randomization.","authors":"Kai Wang, Steven Y Alberding","doi":"10.1002/sim.10279","DOIUrl":"10.1002/sim.10279","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The success of a Mendelian randomization (MR) study critically depends on the validity of the assumptions underlying MR. We focus on detecting heterogeneity (also known as horizontal pleiotropy) in two-sample summary-data MR. A popular approach is to apply Cochran's &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ Q $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; statistic method, developed for meta-analysis. However, Cochran's &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ Q $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; statistic, including its modifications, is known to lack power when its degrees of freedom are large. Furthermore, there is no theoretical justification for the claimed null distribution of the minimum of the modified Cochran's &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ Q $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; statistic with exact weighting ( &lt;math&gt; &lt;semantics&gt; &lt;mrow&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;min&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {Q}_{mathrm{min}} $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ), although it seems to perform well in simulation studies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Method: &lt;/strong&gt;The principle of our proposed method is straightforward: if a set of variables are valid instruments, then any linear combination of these variables is still a valid instrument. Specifically, this principle holds when these linear combinations are formed using eigenvectors derived from a variance matrix. Each linear combination follows a known normal distribution from which a &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ p $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; value can be calculated. We use the minimum &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ p $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; value for these eigenvector-based linear combinations as the test statistic. Additionally, we explore a modification of the modified Cochran's &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ Q $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; statistic by replacing the weighting matrix with a truncated singular value decomposition.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Extensive simulation studies reveal that the proposed methods outperform Cochran's &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ Q $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; statistic, including those with modified weights, and MR-PRESSO, another popular method for detecting heterogeneity, in cases where the number of instruments is not large or the Wald ratios take two values. We also demonstrate these methods using empirical examples. Furthermore, we show that &lt;math&gt; &lt;semantics&gt; &lt;mrow&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;min&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {Q}_{mathrm{min}} $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; does not follow, but is dominated by, the claimed null chi-square distribution. The proposed methods are implemented in an R package iGasso.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Dimension reduction techniques are useful ","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5791-5802"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649176","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 Nonparametric Regression Calibration for the Accelerated Failure Time Model With Measurement Error.","authors":"Yih-Huei Huang, Chien-Ying Wu","doi":"10.1002/sim.10299","DOIUrl":"10.1002/sim.10299","url":null,"abstract":"<p><p>Accelerated failure time models are appealing due to their intuitive interpretation. However, when covariates are subject to measurement errors, naive estimation becomes severely biased. To address this issue, the regression calibration (RC) approach is a widely applicable and effective method. Traditionally, the RC method requires a good predictor for the true covariate, which can be obtained through parametric distribution assumptions or validation datasets. Consequently, the performance of the estimator depends on the plausibility of these assumptions. In this work, we propose a novel method that utilizes error augmentation to duplicate covariates, facilitating nonparametric estimation. Our approach does not require a validation set or parametric distribution assumptions for the true covariate. Through simulation studies, we demonstrate that our approach is more robust and less impacted by heavy censoring rates compared to conventional analyses. Additionally, an analysis of a subset of a real dataset suggests that the conventional RC method may have a tendency to overcorrect the attenuation effect of measurement error.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"6073-6085"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142786596","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":"A Modified Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization.","authors":"Youpeng Su, Siqi Xu, Yilei Ma, Ping Yin, Xingjie Hao, Jiyuan Zhou, Wing Kam Fung, Hongwei Jiang, Peng Wang","doi":"10.1002/sim.10245","DOIUrl":"10.1002/sim.10245","url":null,"abstract":"<p><p>Mendelian randomization uses genetic variants as instrumental variables to estimate the causal effect of exposure on outcome from observational data. A common challenge in Mendelian randomization is that many genetic variants are only modestly or even weakly associated with the exposure of interest, a setting known as many weak instruments. Conventional methods, such as the popular inverse-variance weighted (IVW) estimator, could be heavily biased toward zero when the instrument strength is weak. To address this issue, the debiased IVW (dIVW) estimator and the penalized IVW (pIVW) estimator, which are shown to be robust to many weak instruments, were recently proposed. However, we find that the dIVW estimator tends to produce an exaggerated estimate of the causal effect, especially when the effective sample size is small. Although the pIVW estimator has better statistical properties, it is slightly more complex, and the idea behind this method is also a bit less intuitive. Therefore, we propose a modified debiased IVW (mdIVW) estimator that directly multiplies a shrinkage factor with the original dIVW estimator. After this simple modification, we prove that the mdIVW estimator not only has second-order bias with respect to the effective sample size, but also has smaller variance and mean squared error than the preceding two estimators. We then extend the proposed method to account for the presence of instrumental variable selection and balanced horizontal pleiotropy. We demonstrate the improvement of the mdIVW estimator over the competing ones through extensive simulation studies and real data analysis.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5484-5496"},"PeriodicalIF":1.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142508366","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":"Permutation Test for Image-on-Scalar Regression With an Application to Breast Cancer.","authors":"Shu Jiang, Graham A Colditz","doi":"10.1002/sim.10242","DOIUrl":"10.1002/sim.10242","url":null,"abstract":"<p><p>Image based screening is now routinely available for early detection of cancer and other diseases. Quantitative analysis for effects of risk factors on digital images is important to extract biological insights for modifiable factors in prevention studies and understand pathways for targets in preventive drugs. However, current approaches are restricted to summary measures within the image with the assumption that all relevant features needed to characterize an image can be identified and appropriately quantified. Motivated by data challenges in breast cancer, we propose a nonparametric statistical framework for risk factor screening that uses the whole mammogram image as outcome. The proposed permutation test allows assessment of whether a set of scalar risk factors is associated with the whole image in the presence of correlated residuals across the spatial domain. We provide extensive simulation studies and illustrate an application to the Joanne Knight Breast Health Cohort using the mammogram imaging data.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5596-5604"},"PeriodicalIF":1.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584332","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":"Estimands and Cumulative Incidence Function Regression in Clinical Trials: Some New Results on Interpretability and Robustness.","authors":"Alexandra Bühler, Richard J Cook, Jerald F Lawless","doi":"10.1002/sim.10236","DOIUrl":"10.1002/sim.10236","url":null,"abstract":"<p><p>Regression analyses based on transformations of cumulative incidence functions are often adopted when modeling and testing for treatment effects in clinical trial settings involving competing and semi-competing risks. Common frameworks include the Fine-Gray model and models based on direct binomial regression. Using large sample theory we derive the limiting values of treatment effect estimators based on such models when the data are generated according to multiplicative intensity-based models, and show that the estimand is sensitive to several process features. The rejection rates of hypothesis tests based on cumulative incidence function regression models are also examined for null hypotheses of different types, based on which a robustness property is established. In such settings supportive secondary analyses of treatment effects are essential to ensure a full understanding of the nature of treatment effects. An application to a palliative study of individuals with breast cancer metastatic to bone is provided for illustration.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5513-5533"},"PeriodicalIF":1.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11589047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523102","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":"Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach.","authors":"Jiayi Tong, Lu Li, Jenna Marie Reps, Vitaly Lorman, Naimin Jing, Mackenzie Edmondson, Xiwei Lou, Ravi Jhaveri, Kelly J Kelleher, Nathan M Pajor, Christopher B Forrest, Jiang Bian, Haitao Chu, Yong Chen","doi":"10.1002/sim.10250","DOIUrl":"10.1002/sim.10250","url":null,"abstract":"<p><p>Sparse data bias, where there is a lack of sufficient cases, is a common problem in data analysis, particularly when studying rare binary outcomes. Although a two-step meta-analysis approach may be used to lessen the bias by combining the summary statistics to increase the number of cases from multiple studies, this method does not completely eliminate bias in effect estimation. In this paper, we propose a one-shot distributed algorithm for estimating relative risk using a modified Poisson regression for binary data, named ODAP-B. We evaluate the performance of our method through both simulation studies and real-world case analyses of postacute sequelae of SARS-CoV-2 infection in children using data from 184 501 children across eight national academic medical centers. Compared with the meta-analysis method, our method provides closer estimates of the relative risk for all outcomes considered including syndromic and systemic outcomes. Our method is communication-efficient and privacy-preserving, requiring only aggregated data to obtain relatively unbiased effect estimates compared with two-step meta-analysis methods. Overall, ODAP-B is an effective distributed learning algorithm for Poisson regression to study rare binary outcomes. The method provides inference on adjusted relative risk with a robust variance estimator.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5573-5582"},"PeriodicalIF":1.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11588971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142569400","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}