Statistics in Medicine最新文献

A New Semiparametric Power-Law Regression Model With Long-Term Survival, Change-Point Detection and Regularization.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70043

Nixon Jerez-Lillo, Alejandra Tapia, Victor Hugo Lachos, Pedro Luiz Ramos

{"title":"A New Semiparametric Power-Law Regression Model With Long-Term Survival, Change-Point Detection and Regularization.","authors":"Nixon Jerez-Lillo, Alejandra Tapia, Victor Hugo Lachos, Pedro Luiz Ramos","doi":"10.1002/sim.70043","DOIUrl":"https://doi.org/10.1002/sim.70043","url":null,"abstract":"Kidney cancer, a potentially life-threatening malignancy affecting the kidneys, demands early detection and proactive intervention to enhance prognosis and survival. Advancements in medical and health sciences and the emergence of novel treatments are expected to lead to a favorable response in a subset of patients. This, in turn, is anticipated to enhance overall survival and disease-free survival rates. Cure fraction models have become essential for estimating the proportion of individuals considered cured and free from adverse events. This article presents a novel piecewise power-law cure fraction model with a piecewise decreasing hazard function, deviating from the traditional piecewise constant hazard assumption. By analyzing real medical data, we evaluate various factors to explain the survival of individuals. Consistently, positive outcomes are observed, affirming the significant potential of our approach. Furthermore, we use a local influence analysis to detect potentially influential individuals and perform a postdeletion analysis to analyze their impact on our inferences.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 6","pages":"e70043"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Additive Regression Trees for Group Testing Data.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70052

Madeleine E St Ville, Christopher S McMahan, Joe D Bible, Joshua M Tebbs, Christopher R Bilder

{"title":"Bayesian Additive Regression Trees for Group Testing Data.","authors":"Madeleine E St Ville, Christopher S McMahan, Joe D Bible, Joshua M Tebbs, Christopher R Bilder","doi":"10.1002/sim.70052","DOIUrl":"10.1002/sim.70052","url":null,"abstract":"When screening for low-prevalence diseases, pooling specimens (e.g., blood, urine, swabs, etc.) through group testing has the potential to substantially reduce costs when compared to testing specimens individually. A common goal in group testing applications is to estimate the relationship between an individual's true disease status and their individual-level covariate information. However, estimating such a relationship is a non-trivial problem because true individual disease statuses are unknown due to the group testing protocol and the possibility of imperfect testing. While several regression methods have been developed in recent years to accommodate the complexity of group testing data, the functional form of covariate effects is typically assumed to be known. To avoid model misspecification and to provide a more flexible approach, we propose a Bayesian additive regression trees framework to model the individual-level probability of disease with potentially misclassified group testing data. Our methods can be used to analyze data arising from any group testing protocol with the goal of estimating unknown functions of covariates and assay classification accuracy probabilities.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 6","pages":"e70052"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11907685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preservation of Type I Error for Partially-Unblinded Sample Size Re-Estimation.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70030

Ann Marie K Weideman, Kevin J Anstrom, Gary G Koch, Xianming Tan

{"title":"Preservation of Type I Error for Partially-Unblinded Sample Size Re-Estimation.","authors":"Ann Marie K Weideman, Kevin J Anstrom, Gary G Koch, Xianming Tan","doi":"10.1002/sim.70030","DOIUrl":"https://doi.org/10.1002/sim.70030","url":null,"abstract":"Sample size re-estimation (SSR) at an interim analysis allows for adjustments based on accrued data. Existing strategies rely on either blinded or unblinded methods to inform such adjustments and, ideally, perform these adjustments in a way that preserves Type I error at the nominal level. Here, we propose an approach that uses partially-unblinded methods for SSR for both binary and continuous endpoints. Although this approach has operational unblinding, its partial use of the unblinded information for SSR does not include the interim effect size, hence the term 'partially-unblinded.' Through proof-of-concept and simulation studies, we demonstrate that these adjustments can be made without compromising the Type I error rate. We also investigate different mathematical expressions for SSR under different variance scenarios: homogeneity, heterogeneity, and a combination of both. Of particular interest is the third form of dual variance, for which we provide additional clarifications for binary outcomes and derive an analogous form for continuous outcomes. We show that the corresponding mathematical expressions for the dual variance method are a compromise between those for variance homogeneity and heterogeneity, resulting in sample size estimates that are bounded between those produced by the other expressions, and extend their applicability to adaptive trial design.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 6","pages":"e70030"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pattern Mixture Sensitivity Analyses via Multiple Imputations for Non-Ignorable Dropout in Joint Modeling of Cognition and Risk of Dementia.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70040

Tetiana Gorbach, James R Carpenter, Chris Frost, Maria Josefsson, Jennifer Nicholas, Lars Nyberg

{"title":"Pattern Mixture Sensitivity Analyses via Multiple Imputations for Non-Ignorable Dropout in Joint Modeling of Cognition and Risk of Dementia.","authors":"Tetiana Gorbach, James R Carpenter, Chris Frost, Maria Josefsson, Jennifer Nicholas, Lars Nyberg","doi":"10.1002/sim.70040","DOIUrl":"10.1002/sim.70040","url":null,"abstract":"Motivated by the Swedish Betula study, we consider the joint modeling of longitudinal memory assessments and the hazard of dementia. In the Betula data, the time-to-dementia onset or its absence is available for all participants, while some memory measurements are missing. In longitudinal studies of aging, one cannot rule out the possibility of dropout due to health issues resulting in missing not at random longitudinal measurements. We, therefore, propose a pattern-mixture sensitivity analysis for missing not-at-random data in the joint modeling framework. The sensitivity analysis is implemented via multiple imputation as follows: (i) multiply impute missing not at random longitudinal measurements under a set of plausible pattern-mixture imputation models that allow for acceleration of memory decline after dropout, (ii) fit the joint model to each imputed longitudinal memory and time-to-dementia dataset, and (iii) combine the results of step (ii). Our work illustrates that sensitivity analyses via multiple imputations are an accessible, pragmatic method to evaluate the consequences of missing not at-random data on inference and prediction. This flexible approach can accommodate a range of models for the longitudinal and event-time processes. In particular, the pattern-mixture modeling approach provides an accessible way to frame plausible missing not at random assumptions for different missing data patterns. Applying our approach to the Betula study shows that worse memory levels and steeper memory decline were associated with a higher risk of dementia for all considered scenarios.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 6","pages":"e70040"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Overcoming Model Uncertainty - How Equivalence Tests Can Benefit From Model Averaging.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.10309

Niklas Hagemann, Kathrin Möllenhoff

{"title":"Overcoming Model Uncertainty - How Equivalence Tests Can Benefit From Model Averaging.","authors":"Niklas Hagemann, Kathrin Möllenhoff","doi":"10.1002/sim.10309","DOIUrl":"10.1002/sim.10309","url":null,"abstract":"A common problem in numerous research areas, particularly in clinical trials, is to test whether the effect of an explanatory variable on an outcome variable is equivalent across different groups. In practice, these tests are frequently used to compare the effect between patient groups, for example, based on gender, age, or treatments. Equivalence is usually assessed by testing whether the difference between the groups does not exceed a pre-specified equivalence threshold. Classical approaches are based on testing the equivalence of single quantities, for example, the mean, the area under the curve or other values of interest. However, when differences depending on a particular covariate are observed, these approaches can turn out to be not very accurate. Instead, whole regression curves over the entire covariate range, describing for instance the time window or a dose range, are considered and tests are based on a suitable distance measure of two such curves, as, for example, the maximum absolute distance between them. In this regard, a key assumption is that the true underlying regression models are known, which is rarely the case in practice. However, misspecification can lead to severe problems as inflated type I errors or, on the other hand, conservative test procedures. In this paper, we propose a solution to this problem by introducing a flexible extension of such an equivalence test using model averaging in order to overcome this assumption and making the test applicable under model uncertainty. Precisely, we introduce model averaging based on smooth Bayesian information criterion weights and we propose a testing procedure which makes use of the duality between confidence intervals and hypothesis testing. We demonstrate the validity of our approach by means of a simulation study and illustrate its practical relevance considering a time-response case study with toxicological gene expression data.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 6","pages":"e10309"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11923417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Weight Selection for Time-To-Event Data Under Non-Proportional Hazards.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70045

Moritz Fabian Danzer, Ina Dormuth

{"title":"Adaptive Weight Selection for Time-To-Event Data Under Non-Proportional Hazards.","authors":"Moritz Fabian Danzer, Ina Dormuth","doi":"10.1002/sim.70045","DOIUrl":"10.1002/sim.70045","url":null,"abstract":"When planning a clinical trial for a time-to-event endpoint, we require an estimated effect size and need to consider the type of effect. Usually, an effect of proportional hazards is assumed with the hazard ratio as the corresponding effect measure. Thus, the standard procedure for survival data is generally based on a single-stage log-rank test. Knowing that the assumption of proportional hazards is often violated and sufficient knowledge to derive reasonable effect sizes is usually unavailable, such an approach is relatively rigid. We introduce a more flexible procedure by combining two methods designed to be more robust in case we have little to no prior knowledge. First, we employ a more flexible adaptive multi-stage design instead of a single-stage design. Second, we apply combination-type tests in the first stage of our suggested procedure to benefit from their robustness under uncertainty about the deviation pattern. We can then use the data collected during this period to choose a more specific single-weighted log-rank test for the subsequent stages. In this step, we employ Royston-Parmar spline models to extrapolate the survival curves to make a reasonable decision. Based on a real-world data example, we show that our approach can save a trial that would otherwise end with an inconclusive result. Additionally, our simulation studies demonstrate a sufficient power performance while maintaining more flexibility.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 6","pages":"e70045"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Group Sequential Test for Two-Sample Ordinal Outcome Measures.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70053

Yuan Wu, Ryan A Simmons, Baoshan Zhang, Jesse D Troy

引用次数: 0

Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials With Non-Parametric Approaches.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70042

Xiaotian Chen, Jun Yu, Hongjian Zhu, Li Wang

{"title":"Sequential Monitoring of Covariate-Adaptive Randomized Clinical Trials With Non-Parametric Approaches.","authors":"Xiaotian Chen, Jun Yu, Hongjian Zhu, Li Wang","doi":"10.1002/sim.70042","DOIUrl":"https://doi.org/10.1002/sim.70042","url":null,"abstract":"The importance of covariate adjustment in clinical trials has been underscored by the U.S. FDA's guidance. Inference, with or without covariates, after implementing covariate adaptive randomization (CAR), is garnering increased interest. This paper investigates the sequential monitoring of covariate-adaptive randomized clinical trials through non-parametric methods, a critical advancement for enhancing the precision and efficiency of medical research. CAR, which incorporates baseline patient characteristics into the randomization process, aims to mitigate the risk of confounding and improve the balance of covariates across treatment groups, thereby addressing patients' heterogeneity. Although CAR is known for its benefits in reducing biases and enhancing statistical power, its integration into sequentially monitored clinical trials-a standard practice-poses methodological challenges, particularly in controlling the type I error rate. By employing a non-parametric approach, we demonstrate through theoretical proofs and numerical analyses that our methods effectively control the type I error rate and surpass traditional randomization and analysis methods. This paper not only fills a gap in the literature on sequential monitoring of CAR without model misspecification but also proposes practical solutions for enhancing trial design and analysis, thereby contributing significantly to the field of clinical research.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 6","pages":"e70042"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble of Sequential Learning Models With Distributed Data Centers and Its Applications.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70002

Zhanfeng Wang, Jingyu Huang, Yuan-Chin Ivan Chang

引用次数: 0

Identification and Estimation of Causal Effects Using Non-Concurrent Controls in Platform Trials.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70017

Michele Santacatterina, Federico Macchiavelli Giron, Xinyi Zhang, Iván Díaz

{"title":"Identification and Estimation of Causal Effects Using Non-Concurrent Controls in Platform Trials.","authors":"Michele Santacatterina, Federico Macchiavelli Giron, Xinyi Zhang, Iván Díaz","doi":"10.1002/sim.70017","DOIUrl":"https://doi.org/10.1002/sim.70017","url":null,"abstract":"Platform trials are multi-arm designs that simultaneously evaluate multiple treatments for a single disease within the same overall trial structure. Unlike traditional randomized controlled trials, they allow treatment arms to enter and exit the trial at distinct times while maintaining a control arm throughout. This control arm comprises both concurrent controls, where participants are randomized concurrently to either the treatment or control arm, and non-concurrent controls, who enter the trial when the treatment arm under study is unavailable. While flexible, platform trials introduce the challenge of using non-concurrent controls, raising questions about estimating treatment effects. Specifically, which estimands should be targeted? Under what assumptions can these estimands be identified and estimated? Are there any efficiency gains? In this article, we discuss issues related to the identification and estimation assumptions of common choices of estimand. We conclude that the most robust strategy to increase efficiency without imposing unwarranted assumptions is to target the concurrent average treatment effect (cATE), the ATE among only concurrent units, using a covariate-adjusted doubly robust estimator. Our studies suggest that, for the purpose of obtaining efficiency gains, collecting important prognostic variables is more important than relying on non-concurrent controls. We also discuss the perils of targeting ATE due to an untestable extrapolation assumption that will often be invalid. We provide simulations illustrating our points and an application to the ACTT platform trial, resulting in a 20% improvement in precision compared to the naive estimator that ignores non-concurrent controls and prognostic variables.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 6","pages":"e70017"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0