Biometrical Journal最新文献

{"title":"A Principled Approach to Adjust for Unmeasured Time-Stable Confounding of Supervised Treatment","authors":"Jeppe Ekstrand Halkjær Madsen,&nbsp;Thomas Delvin,&nbsp;Thomas Scheike,&nbsp;Christian Pipper","doi":"10.1002/bimj.70026","DOIUrl":"10.1002/bimj.70026","url":null,"abstract":"<div>\u0000 \u0000 <p>We propose a novel method to adjust for unmeasured time-stable confounding when the time between consecutive treatment administrations is fixed. We achieve this by focusing on a new-user cohort. Furthermore, we envisage that all time-stable confounding goes through the potential time on treatment as dictated by the disease condition at the initiation of treatment. Following this logic, we may eliminate all unmeasured time-stable confounding by adjusting for the potential time on treatment. A challenge with this approach is that right censoring of the potential time on treatment occurs when treatment is terminated at the time of the event of interest, for example, if the event of interest is death. We show how this challenge may be solved by means of the expectation-maximization algorithm without imposing any further assumptions on the distribution of the potential time on treatment. The usefulness of the methodology is illustrated in a simulation study. We also apply the methodology to investigate the effect of depression/anxiety drugs on subsequent poisoning by other medications in the Danish population by means of national registries. We find a protective effect of treatment with selective serotonin reuptake inhibitors on the risk of poisoning by various medications (1- year risk difference of approximately <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>3</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$-3%$</annotation>\u0000 </semantics></math>) and a standard Cox model analysis shows a harming effect (1-year risk difference of approximately <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$2%$</annotation>\u0000 </semantics></math>), which is consistent with what we would expect due to confounding by indication. Unmeasured time-stable confounding can be entirely adjusted for when the time between consecutive treatment administrations is fixed.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840159","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}

{"title":"Assessing Balance of Baseline Time-Dependent Covariates via the Fréchet Distance","authors":"Mireya Díaz","doi":"10.1002/bimj.70024","DOIUrl":"10.1002/bimj.70024","url":null,"abstract":"<div>\u0000 \u0000 <p>Assessment of covariate balance is a key step when performing comparisons between groups particularly in real-world data. We generally evaluate it on baseline covariates, but rarely on longitudinal ones prior to a management decision. We could use pointwise standardized mean differences, standardized differences of slopes, or weights from the model for such purpose. Pointwise differences could be cumbersome for densely sampled longitudinal markers and/or measured at different points. Slopes are suitable for linear or transformable models but not for more complex curves. Weights do not identify the specific covariate(s) responsible for imbalances. This work presents the Fréchet distance as a viable alternative to assess balance of time-dependent covariates. A set of linear and nonlinear curves for which their standardized difference or differences in functional parameters were within 10% sought to identify the Fréchet distance equivalent to this threshold. This threshold is dependent on the level of noise present and thus within group heterogeneity and error variance are needed for its interpretation. Applied to a set of real curves representing the monthly trajectory of hemoglobin A1c from diabetic patients showed that the curves in the two groups were not balanced at the 10% mark. A Beta distribution represents the Fréchet distance distribution reasonably well in most scenarios. This assessment of covariate balance provides the following advantages: It can handle curves of different lengths, shapes, and arbitrary time points. Future work includes examining the utility of this measure under within-series missingness, within-group heterogeneity, its comparison with other approaches, and asymptotics.</p>\u0000 </div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840161","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}

{"title":"Oncology Clinical Trial Design Planning Based on a Multistate Model That Jointly Models Progression-Free and Overall Survival Endpoints","authors":"Alexandra Erdmann,&nbsp;Jan Beyersmann,&nbsp;Kaspar Rufibach","doi":"10.1002/bimj.70017","DOIUrl":"10.1002/bimj.70017","url":null,"abstract":"<p>When planning an oncology clinical trial, the usual approach is to assume proportional hazards and even an exponential distribution for time-to-event endpoints. Often, besides the gold-standard endpoint overall survival (OS), progression-free survival (PFS) is considered as a second confirmatory endpoint. We use a survival multistate model to jointly model these two endpoints and find that neither exponential distribution nor proportional hazards will typically hold for both endpoints simultaneously. The multistate model provides a stochastic process approach to model the dependency of such endpoints neither requiring latent failure times nor explicit dependency modeling such as copulae. We use the multistate model framework to simulate clinical trials with endpoints OS and PFS and show how design planning questions can be answered using this approach. In particular, nonproportional hazards for at least one of the endpoints are a consequence of OS and PFS being dependent and are naturally modeled to improve planning. We then illustrate how clinical trial design can be based on simulations from a multistate model. Key applications are coprimary endpoints and group-sequential designs. Simulations for these applications show that the standard simplifying approach may very well lead to underpowered or overpowered clinical trials. Our approach is quite general and can be extended to more complex trial designs, further endpoints, and other therapeutic areas. An R package is available on CRAN.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840153","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}

{"title":"Test Statistics and Statistical Inference for Data With Informative Cluster Sizes","authors":"Soyoung Kim,&nbsp;Michael J. Martens,&nbsp;Kwang Woo Ahn","doi":"10.1002/bimj.70021","DOIUrl":"10.1002/bimj.70021","url":null,"abstract":"<div>\u0000 \u0000 <p>In biomedical studies, investigators often encounter clustered data. The cluster sizes are said to be informative if the outcome depends on the cluster size. Ignoring informative cluster sizes in the analysis leads to biased parameter estimation in marginal and mixed-effect regression models. Several methods to analyze data with informative cluster sizes have been proposed; however, methods to test the informativeness of the cluster sizes are limited, particularly for the marginal model. In this paper, we propose a score test and a Wald test to examine the informativeness of the cluster sizes for a generalized linear model, a Cox model, and a proportional subdistribution hazards model. Statistical inference can be conducted through weighted estimating equations. The simulation results show that both tests control Type I error rates well, but the score test has higher power than the Wald test for right-censored data while the power of the Wald test is generally higher than the score test for the binary outcome. We apply the Wald and score tests to hematopoietic cell transplant data and compare regression analysis results with/without adjusting for informative cluster sizes.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840154","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}

{"title":"Best Subset Solution Path for Linear Dimension Reduction Models Using Continuous Optimization","authors":"Benoit Liquet,&nbsp;Sarat Moka,&nbsp;Samuel Muller","doi":"10.1002/bimj.70015","DOIUrl":"10.1002/bimj.70015","url":null,"abstract":"<div>\u0000 \u0000 <p>The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high-dimensional contexts where the number of variables is usually much larger than the number of observations. In this paper, we focus on two multivariate statistical methods: principal components analysis and partial least squares. Both approaches are popular linear dimension-reduction methods with numerous applications in several fields including in genomics, biology, environmental science, and engineering. In particular, these approaches build principal components, new variables that are combinations of all the original variables. A main drawback of principal components is the difficulty to interpret them when the number of variables is large. To define principal components from the most relevant variables, we propose to cast the best subset solution path method into principal component analysis and partial least square frameworks. We offer a new alternative by exploiting a continuous optimization algorithm for best subset solution path. Empirical studies show the efficacy of our approach for providing the best subset solution path. The usage of our algorithm is further exposed through the analysis of two real data sets. The first data set is analyzed using the principle component analysis while the analysis of the second data set is based on partial least square framework.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840149","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}

{"title":"Goodness-of-Fit Testing for a Regression Model With a Doubly Truncated Response","authors":"Jacobo de Uña-Álvarez","doi":"10.1002/bimj.70022","DOIUrl":"10.1002/bimj.70022","url":null,"abstract":"<p>In survival analysis and epidemiology, among other fields, interval sampling is often employed. With interval sampling, the individuals undergoing the event of interest within a calendar time interval are recruited. This results in doubly truncated event times. Double truncation, which may appear with other sampling designs too, induces a selection bias, so ordinary statistical methods are generally inconsistent. In this paper, we introduce goodness-of-fit procedures for a regression model when the response variable is doubly truncated. With this purpose, a marked empirical process based on weighted residuals is constructed and its weak convergence is established. Kolmogorov–Smirnov– and Cramér–von Mises–type tests are consequently derived from such core process, and a bootstrap approximation for their practical implementation is given. The performance of the proposed tests is investigated through simulations. An application to model selection for AIDS incubation time as depending on age at infection is provided.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840151","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}

{"title":"Adjusted Inference for Multiple Testing Procedure in Group-Sequential Designs","authors":"Yujie Zhao,&nbsp;Qi Liu,&nbsp;Linda Z. Sun,&nbsp;Keaven M. Anderson","doi":"10.1002/bimj.70020","DOIUrl":"10.1002/bimj.70020","url":null,"abstract":"<div>\u0000 \u0000 <p>Adjustment of statistical significance levels for repeated analysis in group-sequential trials has been understood for some time. Adjustment accounting for testing multiple hypotheses is also well understood. There is limited research on simultaneously adjusting for both multiple hypothesis testing and repeated analyses of one or more hypotheses. We address this gap by proposing <i>adjusted-sequential p-values</i> that reject when they are less than or equal to the family-wise Type I error rate (FWER). We also propose sequential <span></span><math>\u0000 <semantics>\u0000 <mi>p</mi>\u0000 <annotation>$p$</annotation>\u0000 </semantics></math>-values for intersection hypotheses to compute adjusted-sequential <span></span><math>\u0000 <semantics>\u0000 <mi>p</mi>\u0000 <annotation>$p$</annotation>\u0000 </semantics></math>-values for elementary hypotheses. We demonstrate the application using weighted Bonferroni tests and weighted parametric tests for inference on each elementary hypothesis tested.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840160","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}

{"title":"Issue Information: Biometrical Journal 1'25","authors":"","doi":"10.1002/bimj.70027","DOIUrl":"https://doi.org/10.1002/bimj.70027","url":null,"abstract":"","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868580","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}

{"title":"Detecting Interactions in High-Dimensional Data Using Cross Leverage Scores","authors":"Sven Teschke,&nbsp;Katja Ickstadt,&nbsp;Alexander Munteanu","doi":"10.1002/bimj.70014","DOIUrl":"https://doi.org/10.1002/bimj.70014","url":null,"abstract":"<p>We develop a variable selection method for interactions in regression models on large data in the context of genetics. The method is intended for investigating the influence of single-nucleotide polymorphisms (SNPs) and their interactions on health outcomes, which is a <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 <mo>≫</mo>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 <annotation>$pgg n$</annotation>\u0000 </semantics></math> problem. We introduce cross leverage scores (CLSs) to detect interactions of variables while maintaining interpretability. Using this method, it is not necessary to consider every possible interaction between variables individually, which would be very time-consuming even for moderate amounts of variables. Instead, we calculate the CLS for each variable and obtain a measure of importance for this variable. Calculating the scores remains time-consuming for large data sets. The key idea for scaling to large data is to divide the data into smaller random batches or consecutive windows of variables. This avoids complex and time-consuming computations on high-dimensional matrices by performing the computations only for small subsets of the data, which is less costly. We compare these methods to provable approximations of CLS based on sketching, which aims at summarizing data succinctly. In a simulation study, we show that the CLSs are directly linked to the importance of a variable in the sense of an interaction effect. We further show that the approximation approaches are appropriate for performing the calculations efficiently on arbitrarily large data while preserving the interaction detection effect of the CLS. This underlines their scalability to genome wide data. In addition, we evaluate the methods on real data from the HapMap project.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142749303","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}

{"title":"Model Selection for Ordinary Differential Equations: A Statistical Testing Approach","authors":"Itai Dattner,&nbsp;Shota Gugushvili,&nbsp;Oleksandr Laskorunskyi","doi":"10.1002/bimj.70013","DOIUrl":"10.1002/bimj.70013","url":null,"abstract":"<p>Ordinary differential equations (ODEs) are foundational tools in modeling intricate dynamics across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon through multiple ODE models, driven by different understandings of nuances in internal mechanisms or abstraction levels, presents a model selection challenge. This study introduces a testing-based approach for ODE model selection amidst statistical noise. Rooted in the model misspecification framework, we adapt classical statistical paradigms (Vuong and Hotelling) to the ODE context, allowing for the comparison and ranking of diverse causal explanations without the constraints of nested models. Our simulation studies numerically investigate the statistical properties of the test, demonstrating its attainment of the nominal size and power across various settings. Real-world data examples further underscore the algorithm's applicability in practice. To foster accessibility and encourage real-world applications, we provide a user-friendly Python implementation of our model selection algorithm, bridging theoretical advancements with hands-on tools for the scientific community.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741437","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}