Statistics in Medicine最新文献

Analysis of Covariance in General Factorial Designs Through Multiple Contrast Tests Under Variance Heteroscedasticity. 方差异方差条件下多重对比检验一般析因设计的协方差分析。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-30 DOI: 10.1002/sim.70018

Matthias Becher, Ludwig A Hothorn, Frank Konietschke

{"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":"https://doi.org/10.1002/sim.70018","url":null,"abstract":"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.","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":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Robust Score Test in G-Computation for Covariate Adjustment in Randomized Clinical Trials Leveraging Different Variance Estimators via Influence Functions. 通过影响函数利用不同方差估计量的随机临床试验中协变量调整的g计算的稳健性得分检验。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-30 DOI: 10.1002/sim.70080

Xin Zhang, Haitao Chu, Lin Liu, Satrajit Roychoudhury

{"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":"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.","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}

引用次数: 0

Estimation of Cancer Incidence Trends Adjusted for Changes in Screening and Detection Processes. 根据筛查和检测过程的变化调整癌症发病率趋势的估计。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-30 DOI: 10.1002/sim.70063

Bastien Trächsel, Valentin Rousson, Jean-Luc Bulliard, Isabella Locatelli

{"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":"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.","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}

引用次数: 0

BayTetra: A Bayesian Semiparametric Approach for Testing Trajectory Differences. BayTetra：一种测试轨迹差异的贝叶斯半参数方法。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-30 DOI: 10.1002/sim.70071

Wei Jin, Qiuxin Gao, Yanxun Xu

{"title":"BayTetra: A Bayesian Semiparametric Approach for Testing Trajectory Differences.","authors":"Wei Jin, Qiuxin Gao, Yanxun Xu","doi":"10.1002/sim.70071","DOIUrl":"https://doi.org/10.1002/sim.70071","url":null,"abstract":"Testing differences in longitudinal trajectories among distinct groups of population is an important task in many biomedical applications. Motivated by an application in Alzheimer's disease, we develop BayTetra, an innovative Bayesian semiparametric approach for estimating and testing group differences in multivariate longitudinal trajectories. BayTetra jointly models multivariate longitudinal data by directly accounting for correlations among different responses, and uses a semiparametric framework based on B-splines to capture the non-linear trajectories with great flexibility. To avoid overfitting, BayTetra encourages parsimonious trajectory estimation by imposing penalties on the smoothness of the spline functions. The proposed method converts the challenging task of hypothesis testing for longitudinal trajectories into a more manageable equivalent form based on hypothesis testing for spline coefficients. More importantly, by leveraging posterior inference with natural uncertainty quantification, our Bayesian method offers a more robust and straightforward hypothesis testing procedure than frequentist methods. Extensive simulations demonstrate BayTetra's superior performance over alternatives. Applications to the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study yield interpretable and valuable clinical insights. A major contribution of this paper is that we have developed an R package BayTetra, which implements the proposed Bayesian semiparametric approach and is the first publicly available software for hypothesis testing on trajectory differences based on a flexible modeling framework.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70071"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812285","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

Causal Multistate Models to Evaluate Treatment Delay. 评价治疗延迟的因果多状态模型。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-30 DOI: 10.1002/sim.70061

Ilaria Prosepe, Saskia le Cessie, Hein Putter, Nan van Geloven

引用次数: 0

Bayesian Additive Regression Trees for Group Testing Data. 组测试数据的贝叶斯加性回归树。

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

A New Semiparametric Power-Law Regression Model With Long-Term Survival, Change-Point Detection and Regularization. 具有长期生存、变化点检测和正则化的半参数幂律回归模型。

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

Preservation of Type I Error for Partially-Unblinded Sample Size Re-Estimation. 部分非盲法样本量重估计的I型误差保存。

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. 认知与痴呆风险联合建模中不可忽略缺失的多重归算模式混合敏感性分析。

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. 克服模型不确定性-等效测试如何从模型平均中受益。

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