Statistics in Medicine最新文献

{"title":"Improving Efficiency and Robustness of the Prognostic Accuracy of Biomarkers With Partial Incomplete Failure-Time Data and Auxiliary Outcome: Application to Prostate Cancer Active Surveillance Study.","authors":"Yunro Chung, Tianxi Cai, Lisa Newcomb, Daniel W Lin, Yingye Zheng","doi":"10.1002/sim.70072","DOIUrl":"https://doi.org/10.1002/sim.70072","url":null,"abstract":"<p><p>When novel biomarkers are developed for the clinical management of patients diagnosed with cancer, it is critical to quantify the accuracy of a biomarker-based decision tool. The evaluation can be challenging when the definite outcome <math> <semantics><mrow><mi>T</mi></mrow> <annotation>$$ T $$</annotation></semantics> </math> , such as time to disease progression, is only partially ascertained on a limited set of study patients. Under settings where <math> <semantics><mrow><mi>T</mi></mrow> <annotation>$$ T $$</annotation></semantics> </math> is only observed on a subset but an auxiliary outcome correlated with <math> <semantics><mrow><mi>T</mi></mrow> <annotation>$$ T $$</annotation></semantics> </math> is available on all subjects, we propose an augmented estimation procedure for commonly used time-dependent accuracy measures. The augmented estimators are easy to implement without imposing modeling assumptions between the two types of time-to-event outcomes and are more efficient than the complete-case estimator. When the ascertainment of the outcome is non-random and subject to informative censoring, we further augment our proposed method with inverse probability weighting to improve robustness. Results from simulation studies confirm the robustness and efficiency properties of the proposed estimators. The method is illustrated with data from the Canary Prostate Active Surveillance Study.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 8-9","pages":"e70072"},"PeriodicalIF":1.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049632","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 Joint Model for (Un)Bounded Longitudinal Markers, Competing Risks, and Recurrent Events Using Patient Registry Data.","authors":"Pedro Miranda Afonso, Dimitris Rizopoulos, Anushka K Palipana, Emrah Gecili, Cole Brokamp, John P Clancy, Rhonda D Szczesniak, Eleni-Rosalina Andrinopoulou","doi":"10.1002/sim.70057","DOIUrl":"https://doi.org/10.1002/sim.70057","url":null,"abstract":"<p><p>Joint models for longitudinal and survival data have become a popular framework for studying the association between repeatedly measured biomarkers and clinical events. Nevertheless, addressing complex survival data structures, especially handling both recurrent and competing event times within a single model, remains a challenge. This causes important information to be disregarded. Moreover, existing frameworks rely on a Gaussian distribution for continuous markers, which may be unsuitable for bounded biomarkers, resulting in biased estimates of associations. To address these limitations, we propose a Bayesian shared-parameter joint model that simultaneously accommodates multiple (possibly bounded) longitudinal markers, a recurrent event process, and competing risks. We use the beta distribution to model responses bounded within any interval <math> <semantics><mrow><mo>(</mo> <mi>a</mi> <mo>,</mo> <mi>b</mi> <mo>)</mo></mrow> <annotation>$$ left(a,bright) $$</annotation></semantics> </math> without sacrificing the interpretability of the association. The model offers various forms of association, discontinuous risk intervals, and both gap and calendar timescales. A simulation study shows that it outperforms simpler joint models. We utilize the US Cystic Fibrosis Foundation Patient Registry to study the associations between changes in lung function and body mass index, and the risk of recurrent pulmonary exacerbations, while accounting for the competing risks of death and lung transplantation. Our efficient implementation allows fast fitting of the model despite its complexity and the large sample size from this patient registry. Our comprehensive approach provides new insights into cystic fibrosis disease progression by quantifying the relationship between the most important clinical markers and events more precisely than has been possible before. The model implementation is available in the R package JMbayes2.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 8-9","pages":"e70057"},"PeriodicalIF":1.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12023843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143982966","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 Variance Estimator for Marginal Cox Regression Models Fit to Non-Nested Multilevel Data.","authors":"Peter C Austin","doi":"10.1002/sim.70074","DOIUrl":"https://doi.org/10.1002/sim.70074","url":null,"abstract":"<p><p>In health services research, researchers often use clustered data to estimate the independent association between individual outcomes and cluster-level covariates after adjusting for individual-level characteristics. Marginal generalized linear models estimated using generalized estimating equation (GEE) methods or hierarchical (or multilevel) regression models can be used when there is a single source of clustering (e.g., patients nested within hospitals). Hierarchical regression models can also be used when there are multiple sources of clustering (e.g., patients nested within surgeons who in turn are nested within hospitals). Methods for estimating marginal regression models are less well-developed when there are multiple sources of non-nested clustering (e.g., patients are clustered both within hospitals and within in neighborhoods, but neither neighborhoods or hospitals are nested in the other). Miglioretti and Heagerty developed a GEE-type variance estimator for use when fitting marginal generalized linear models to non-nested multilevel data. We propose a variance estimator for a marginal Cox regression model fit to non-nested multilevel data that combined their approach with Lin and Wei's robust variance estimator for the Cox model. We evaluated the performance of the proposed variance estimator using an extensive set of Monte Carlo simulations. We illustrated the use of the variance estimator in a case study consisting of patients hospitalized with an acute myocardial infarction who were clustered within hospitals and who were also clustered in neighborhoods. In summary, a variance estimator motivated by that proposed by Miglioretti and Heagerty can be used with marginal Cox regression models fit to non-nested multilevel data.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 8-9","pages":"e70074"},"PeriodicalIF":1.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12023713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014010","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":"Identifying Less Burdensome and More Cost-Efficient Incomplete Stepped Wedge Designs for Continuous Outcomes Collected via Repeated Cross-Sections.","authors":"Ehsan Rezaei-Darzi, Jessica Kasza, Anisa R Assifi, Danielle Mazza, Andrew B Forbes, Kelsey L Grantham","doi":"10.1002/sim.70067","DOIUrl":"https://doi.org/10.1002/sim.70067","url":null,"abstract":"<p><p>Stepped wedge trials can be costly and burdensome. Recent work has investigated the iterative removal of cluster-period cells from stepped wedge designs, producing a series of candidate incomplete designs that are less burdensome. We propose a novel way to explore the space of incomplete stepped wedge designs, by considering their cost efficiency, seeking to identify designs that retain high power while limiting the total trial cost. We define the cost efficiency of a design as the ratio of the precision of the treatment effect estimator to the total trial cost. Total trial cost incorporates the costs per cluster, costs per participant in intervention and control conditions, and the costs of restarting data collection in a cluster under intervention and control conditions following a pause. We consider linear mixed models for continuous outcomes with a repeated cross-sectional sampling scheme and use an iterative procedure to remove individual cells with the lowest contribution to the cost efficiency metric, producing a series of progressively reduced designs. We define the optimal design within this design space as that which maximizes the cost efficiency relative to the complete design, subject to a minimum acceptable power constraint. We illustrate our methods with an example motivated by a real-world trial. Our methods enable trialists to identify incomplete stepped wedge designs that are less burdensome and more cost-efficient than complete designs. We find that \"staircase\"-type designs, where clusters only contribute measurements immediately before and after the treatment switch, are often particularly cost-efficient variants of the stepped wedge design.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 8-9","pages":"e70067"},"PeriodicalIF":1.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12023839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047643","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":"Evaluation of Rolling Surveillance Methods in Context of Prior Aberrations: A Simulation Study With Routine Data From Low- and Middle-Income Countries.","authors":"Anuraag Gopaluni, Nicholas B Link, Emma Boley, Isabel Fulcher, Muhammed Semakula, Bethany Hedt-Gauthier","doi":"10.1002/sim.70075","DOIUrl":"https://doi.org/10.1002/sim.70075","url":null,"abstract":"<p><p>Syndromic surveillance integrated into routine health management information systems could improve timely detection of disease outbreaks, particularly in low- and middle-income countries that have limited diagnostic data. This study evaluates the impact of prior anomalies referred to as \"aberrations,\" such as historical outbreaks, that can distort \"baseline data\" on the accuracy of rolling surveillance methods that track ongoing disease trends. We assessed five widely used outbreak detection algorithms-EARS, Farrington, Holt-Winters, and two versions of the Weinberger-Fulcher model (negative binomial (WF NB) and quasipoisson (WF QP))-under simulation scenarios motivated by 5 years of acute respiratory infection data from Liberia. We evaluated seven data-generating mechanisms that cover a wide range of temporal and seasonal patterns. We assessed the accuracy of the outbreak detection algorithms under varied size and timing of outbreaks and aberrations. Accuracy was measured through sensitivity and specificity, with a joint assessment of both metrics using pseudo-ROC curves. Results showed that the introduction of aberrations reduced sensitivity in general, but the algorithms' relative performances were highly context-dependent. EARS and WF models demonstrated high sensitivity for detecting outbreaks when no recent aberrations were present. However, when aberrations occurred within the last year of baseline data, Holt-Winters-unless there was evidence of strong time trends-and WF QP maintained better overall balance between sensitivity and specificity. The Farrington algorithm exhibited strong sensitivity with recent aberrations but at the cost of lower specificity. These findings provide actionable insights and practical recommendations for implementing rolling surveillance in resource-constrained environments, emphasizing the need to consider historical data disturbances and rigorously evaluate sensitivity and specificity jointly.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 8-9","pages":"e70075"},"PeriodicalIF":1.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039544","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":"Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application.","authors":"Jessica Butts, Leif Verace, Christine Wendt, Russell Bowler, Craig P Hersh, Qi Long, Lynn Eberly, Sandra E Safo","doi":"10.1002/sim.70036","DOIUrl":"https://doi.org/10.1002/sim.70036","url":null,"abstract":"<p><p>Multiple data views measured on the same set of participants are becoming more common and have the potential to deepen our understanding of many complex diseases by analyzing these different views simultaneously. Equally important, many of these complex diseases show evidence of subgroup heterogeneity (e.g., by sex or race). HIP (Heterogeneity in Integration and Prediction) is among the first methods proposed to integrate multiple data views while also accounting for subgroup heterogeneity to identify common and subgroup-specific markers of a particular disease. However, HIP is applicable to continuous outcomes and requires programming expertise by the user. Here we propose extensions to HIP that accommodate multi-class, Poisson, and Zero-Inflated Poisson outcomes while retaining the benefits of HIP. Additionally, we introduce an R Shiny application, accessible on shinyapps.io at https://multi-viewlearn.shinyapps.io/HIP_ShinyApp/, that provides an interface with the Python implementation of HIP to allow more researchers to use the method anywhere and on any device. We applied HIP to identify genes and proteins common and specific to males and females that are associated with exacerbation frequency. Although some of the identified genes and proteins show evidence of a relationship with chronic obstructive pulmonary disease (COPD) in existing literature, others may be candidates for future research investigating their relationship with COPD. We demonstrate the use of the Shiny application with publicly available data. An R-package for HIP is available at https://github.com/lasandrall/HIP.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 8-9","pages":"e70036"},"PeriodicalIF":1.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12023842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010257","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":"Analysis of Covariance in General Factorial Designs Through Multiple Contrast Tests Under Variance Heteroscedasticity.","authors":"Matthias Becher, Ludwig A Hothorn, Frank Konietschke","doi":"10.1002/sim.70018","DOIUrl":"10.1002/sim.70018","url":null,"abstract":"<p><p>A common goal in clinical trials is to conduct tests on estimated treatment effects adjusted for covariates such as age or sex. Analysis of Covariance (ANCOVA) is often used in these scenarios to test the global null hypothesis of no treatment effect using an <math> <semantics><mrow><mi>F</mi></mrow> <annotation>$$ F $$</annotation></semantics> </math> -test. However, in several samples, the <math> <semantics><mrow><mi>F</mi></mrow> <annotation>$$ F $$</annotation></semantics> </math> -test does not provide any information about individual null hypotheses and has strict assumptions such as variance homoscedasticity. We extend the method proposed by Konietschke et al. [\"Analysis of Covariance Under Variance Heteroscedasticity in General Factorial Designs,\" Statistics in Medicine 40 (2021): 4732-4749] to a multiple contrast test procedure (MCTP), which allows us to test arbitrary linear hypotheses and provides information about the global- as well as the individual null hypotheses. Further, we can calculate compatible simultaneous confidence intervals for the individual effects. We derive a small sample size approximation of the distribution of the test statistic via a multivariate t-distribution. As an alternative, we introduce a Wild-bootstrap method. Extensive simulations show that our methods are applicable even when sample sizes are small. Their application is further illustrated within a real data example.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70018"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11979878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812281","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":"Dynamic Treatment Effect Analysis in Crossover Designs Through Repeated Measures.","authors":"Jianping Sun, Peiran Guo, Xiaoyang Chen, Xianming Tan","doi":"10.1002/sim.70070","DOIUrl":"https://doi.org/10.1002/sim.70070","url":null,"abstract":"<p><p>This paper introduces an extended model that harnesses the power of convolution operations to represent time-varying treatment and carry-over effects in a crossover study design. Unlike the traditional model, the proposed approach unifies the treatment and carry-over effects through time-varying response functions, one for each treatment. The model is not only flexible enough to accommodate a variety of treatment plans, including multiple administrations at different doses, but also allows for the inclusion of more treatment periods. The advantages of this approach are accentuated by its ability to be generalized, to avoid prior assumptions about the carry-over effect, and to maintain consistent estimation and hypothesis testing procedures. In this paper, we explore the details of hypothesis testing under this extended model, focusing in particular on the comparison of two response functions within specified intervals. The goal of this work is to improve the modeling of carry-over effects, thereby strengthening the applicability of the model to a variety of experimental settings.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70070"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046569","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 Robust Score Test in G-Computation for Covariate Adjustment in Randomized Clinical Trials Leveraging Different Variance Estimators via Influence Functions.","authors":"Xin Zhang, Haitao Chu, Lin Liu, Satrajit Roychoudhury","doi":"10.1002/sim.70080","DOIUrl":"https://doi.org/10.1002/sim.70080","url":null,"abstract":"<p><p>G-computation has become a widely used robust method for estimating unconditional (marginal) treatment effects with covariate adjustment in the analysis of randomized clinical trials. Statistical inference in this context typically relies on the Wald test or Wald interval, which can be easily implemented using a consistent variance estimator. However, existing literature suggests that when sample sizes are small or when parameters of interest are near boundary values, Wald-based methods may be less reliable due to type I error rate inflation and insufficient interval coverage. In this article, we propose a robust score test for g-computation estimators in the context of two-sample treatment comparisons. The proposed test is asymptotically valid under simple and stratified (biased-coin) randomization schemes, even when regression models are misspecified. These test statistics can be conveniently computed using existing variance estimators, and the corresponding confidence intervals have closed-form expressions, making them convenient to implement. Through extensive simulations, we demonstrate the superior finite-sample performance of the proposed method. Finally, we apply the proposed method to reanalyze a completed randomized clinical trial. The new analysis using our proposed score test achieves statistical significance, whilst reducing the issue of type I error inflation.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70080"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812279","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":"Estimation of Cancer Incidence Trends Adjusted for Changes in Screening and Detection Processes.","authors":"Bastien Trächsel, Valentin Rousson, Jean-Luc Bulliard, Isabella Locatelli","doi":"10.1002/sim.70063","DOIUrl":"https://doi.org/10.1002/sim.70063","url":null,"abstract":"<p><p>Cancer is a major public health issue, and monitoring its incidence is important to suggest and evaluate the impact of preventive interventions. However, estimating trends in cancer incidence is often difficult due to changes in screening or other detection processes over time, which can artificially inflate or deflate the observed incidences. We propose a new method for estimating trends in cancer incidence adjusted for such changes, using a constrained Almon distributed lag model. Unlike other approaches, our method does not rely on any knowledge of cancer progression, or detailed evolution of screening practice over time. It requires the registration of the stages (I-IV) of detected cancers while assuming that the distribution of these stages remains constant in the absence of any change in screening practice. Our method is able to recover the real underlying cancer incidence in simulated data reproducing either no change or a gradual or sudden change in screening practice. For illustration, it is applied to registry data from the canton of Geneva, Switzerland, to estimate breast cancer incidence for the period 1991-2016, where it downwardly corrects the observed incidence when an organized screening program was started.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70063"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812290","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}