Biometrical Journal最新文献_第9页

Issue Information: Biometrical Journal 5'24 发行信息：生物计量学杂志 5'24

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-06-21 DOI: 10.1002/bimj.202470005

引用次数: 0

Causal inference in the absence of positivity: The role of overlap weights 缺乏正向性的因果推理：重叠权重的作用

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-06-07 DOI: 10.1002/bimj.202300156

Roland A. Matsouaka, Yunji Zhou

{"title":"Causal inference in the absence of positivity: The role of overlap weights","authors":"Roland A. Matsouaka, Yunji Zhou","doi":"10.1002/bimj.202300156","DOIUrl":"10.1002/bimj.202300156","url":null,"abstract":"How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess causal treatment effects in the context where the positivity assumption is violated. We focus on and examine four specific alternative solutions to the inverse probability weighting (IPW) trimming and truncation: matching weight (MW), Shannon's entropy weight (EW), overlap weight (OW), and beta weight (BW) estimators.We first specify their target population, the population of patients for whom clinical equipoise, that is, where we have sufficient PS overlap. Then, we establish the nexus among the different corresponding weights (and estimators); this allows us to highlight the shared properties and theoretical implications of these estimators. Finally, we introduce their augmented estimators that take advantage of estimating both the propensity score and outcome regression models to enhance the treatment effect estimators in terms of bias and efficiency. We also elucidate the role of the OW estimator as the flagship of all these methods that target the overlap population.Our analytic results demonstrate that OW, MW, and EW are preferable to IPW and some cases of BW when there is a moderate or extreme (stochastic or structural) violation of the positivity assumption. We then evaluate, compare, and confirm the finite-sample performance of the aforementioned estimators via Monte Carlo simulations. Finally, we illustrate these methods using two real-world data examples marked by violations of the positivity assumption.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141285482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive predictor-set linear model: An imputation-free method for linear regression prediction on data sets with missing values 自适应预测集线性模型：对有缺失值的数据集进行线性回归预测的免估算方法。

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-05-30 DOI: 10.1002/bimj.202300090

Benjamin Planterose Jiménez, Manfred Kayser, Athina Vidaki, Amke Caliebe

{"title":"Adaptive predictor-set linear model: An imputation-free method for linear regression prediction on data sets with missing values","authors":"Benjamin Planterose Jiménez, Manfred Kayser, Athina Vidaki, Amke Caliebe","doi":"10.1002/bimj.202300090","DOIUrl":"10.1002/bimj.202300090","url":null,"abstract":"Linear regression (LR) is vastly used in data analysis for continuous outcomes in biomedicine and epidemiology. Despite its popularity, LR is incompatible with missing data, which frequently occur in health sciences. For parameter estimation, this shortcoming is usually resolved by complete-case analysis or imputation. Both work-arounds, however, are inadequate for prediction, since they either fail to predict on incomplete records or ignore missingness-induced reduction in prediction accuracy and rely on (unrealistic) assumptions about the missing mechanism. Here, we derive adaptive predictor-set linear model (aps-lm), capable of making predictions for incomplete data without the need for imputation. It is derived by using a predictor-selection operation, the Moore–Penrose pseudoinverse, and the reduced QR decomposition. aps-lm is an LR generalization that inherently handles missing values. It is applied on a reference data set, where complete predictors and outcome are available, and yields a set of privacy-preserving parameters. In a second stage, these are shared for making predictions of the outcome on external data sets with missing entries for predictors without imputation. Moreover, aps-lm computes prediction errors that account for the pattern of missing values even under extreme missingness. We benchmark aps-lm in a simulation study. aps-lm showed greater prediction accuracy and reduced bias compared to popular imputation strategies under a wide range of scenarios including variation of sample size, goodness of fit, missing value type, and covariance structure. Finally, as a proof-of-principle, we apply aps-lm in the context of epigenetic aging clocks, linear models that predict a person's biological age from epigenetic data with promising clinical applications.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","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 Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data 用于聚类和基因选择的贝叶斯分层隐马尔可夫模型：应用于肾癌基因表达数据。

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-05-30 DOI: 10.1002/bimj.202300173

Thierry Chekouo, Himadri Mukherjee

引用次数: 0

Valid instrumental variable selection method using negative control outcomes and constructing efficient estimator 使用负控制结果的有效工具变量选择法和构建高效估计器。

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-05-27 DOI: 10.1002/bimj.202300113

Shunichiro Orihara, Atsushi Goto, Masataka Taguri

引用次数: 0

Predicting class switch recombination in B-cells from antibody repertoire data 从抗体库数据预测 B 细胞中的类开关重组

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-05-24 DOI: 10.1002/bimj.202300171

Lutecia Servius, Davide Pigoli, Joseph Ng, Franca Fraternali

{"title":"Predicting class switch recombination in B-cells from antibody repertoire data","authors":"Lutecia Servius, Davide Pigoli, Joseph Ng, Franca Fraternali","doi":"10.1002/bimj.202300171","DOIUrl":"10.1002/bimj.202300171","url":null,"abstract":"Statistical and machine learning methods have proved useful in many areas of immunology. In this paper, we address for the first time the problem of predicting the occurrence of class switch recombination (CSR) in B-cells, a problem of interest in understanding antibody response under immunological challenges. We propose a framework to analyze antibody repertoire data, based on clonal (CG) group representation in a way that allows us to predict CSR events using CG level features as input. We assess and compare the performance of several predicting models (logistic regression, LASSO logistic regression, random forest, and support vector machine) in carrying out this task. The proposed approach can obtain an unweighted average recall of <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>71</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$71%$</annotation>\u0000 </semantics></math> with models based on variable region descriptors and measures of CG diversity during an immune challenge and, most notably, before an immune challenge.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141089535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","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 nonparametric proportional risk model to assess a treatment effect in time-to-event data 在时间到事件数据中评估治疗效果的非参数比例风险模型。

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-05-24 DOI: 10.1002/bimj.202300147

Lucia Ameis, Oliver Kuss, Annika Hoyer, Kathrin Möllenhoff

{"title":"A nonparametric proportional risk model to assess a treatment effect in time-to-event data","authors":"Lucia Ameis, Oliver Kuss, Annika Hoyer, Kathrin Möllenhoff","doi":"10.1002/bimj.202300147","DOIUrl":"10.1002/bimj.202300147","url":null,"abstract":"Time-to-event analysis often relies on prior parametric assumptions, or, if a semiparametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk (RR), the ratio of the cumulative distribution functions. In this paper, we introduce an alternative to current methodology for assessing a treatment effect in a two-group situation, not relying on the proportional hazards assumption but assuming proportional risks. Precisely, we propose a new nonparametric model to directly estimate the RR of two groups to experience an event under the assumption that the risk ratio is constant over time. In addition to this relative measure, our model allows for calculating the number needed to treat as an absolute measure, providing the possibility of an easy and holistic interpretation of the data. We demonstrate the validity of the approach by means of a simulation study and present an application to data from a large randomized controlled trial investigating the effect of dapagliflozin on all-cause mortality.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141089524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","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 method for determining groups in cumulative incidence curves in competing risk data 在竞争风险数据的累积发病率曲线中确定组别的方法。

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-05-22 DOI: 10.1002/bimj.202300084

Marta Sestelo, Luís Meira-Machado, Nora M. Villanueva, Javier Roca-Pardiñas

引用次数: 0

Sparse Group Penalties for bi-level variable selection 用于双级变量选择的稀疏组惩罚。

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-05-15 DOI: 10.1002/bimj.202200334

Gregor Buch, Andreas Schulz, Irene Schmidtmann, Konstantin Strauch, Philipp S. Wild

{"title":"Sparse Group Penalties for bi-level variable selection","authors":"Gregor Buch, Andreas Schulz, Irene Schmidtmann, Konstantin Strauch, Philipp S. Wild","doi":"10.1002/bimj.202200334","DOIUrl":"10.1002/bimj.202200334","url":null,"abstract":"Many data sets exhibit a natural group structure due to contextual similarities or high correlations of variables, such as lipid markers that are interrelated based on biochemical principles. Knowledge of such groupings can be used through bi-level selection methods to identify relevant feature groups and highlight their predictive members. One of the best known approaches of this kind combines the classical Least Absolute Shrinkage and Selection Operator (LASSO) with the Group LASSO, resulting in the Sparse Group LASSO. We propose the Sparse Group Penalty (SGP) framework, which allows for a flexible combination of different SGL-style shrinkage conditions. Analogous to SGL, we investigated the combination of the Smoothly Clipped Absolute Deviation (SCAD), the Minimax Concave Penalty (MCP) and the Exponential Penalty (EP) with their group versions, resulting in the Sparse Group SCAD, the Sparse Group MCP, and the novel Sparse Group EP (SGE). Those shrinkage operators provide refined control of the effect of group formation on the selection process through a tuning parameter. In simulation studies, SGPs were compared with other bi-level selection methods (Group Bridge, composite MCP, and Group Exponential LASSO) for variable and group selection evaluated with the Matthews correlation coefficient. We demonstrated the advantages of the new SGE in identifying parsimonious models, but also identified scenarios that highlight the limitations of the approach. The performance of the techniques was further investigated in a real-world use case for the selection of regulated lipids in a randomized clinical trial.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202200334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140924034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative review of novel model-assisted designs for phase I/II clinical trials 对 I/II 期临床试验的新型模型辅助设计进行比较审查。

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-05-13 DOI: 10.1002/bimj.202300398

Haolun Shi, Ruitao Lin, Xiaolei Lin

{"title":"Comparative review of novel model-assisted designs for phase I/II clinical trials","authors":"Haolun Shi, Ruitao Lin, Xiaolei Lin","doi":"10.1002/bimj.202300398","DOIUrl":"10.1002/bimj.202300398","url":null,"abstract":"In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting dose locations.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0