Statistical Methods in Medical Research最新文献_第10页

Extension of Fisher's least significant difference method to multi-armed group-sequential response-adaptive designs. Fisher最小显著差异法在多臂群序列响应自适应设计中的推广。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-24 DOI: 10.1177/09622802251319896

Wenyu Liu, D Stephen Coad

{"title":"Extension of Fisher's least significant difference method to multi-armed group-sequential response-adaptive designs.","authors":"Wenyu Liu, D Stephen Coad","doi":"10.1177/09622802251319896","DOIUrl":"https://doi.org/10.1177/09622802251319896","url":null,"abstract":"Multi-armed multi-stage designs evaluate experimental treatments using a control arm at interim analyses. Incorporating response-adaptive randomisation in these designs allows early stopping, faster treatment selection and more patients to be assigned to the more promising treatments. Existing frequentist multi-armed multi-stage designs demonstrate that the family-wise error rate is strongly controlled, but they may be too conservative and lack power when the experimental treatments are very different therapies rather than doses of the same drug. Moreover, the designs use a fixed allocation ratio. In this article, Fisher's least significant difference method extended to group-sequential response-adaptive designs is investigated. It is shown mathematically that the information time continues after dropping inferior arms, and hence the error-spending approach can be used to control the family-wise error rate. Two optimal allocations were considered. One ensures efficient estimation of the treatment effects and the other maximises the power subject to a fixed total sample size. Operating characteristics of the group-sequential response-adaptive design for normal and censored survival outcomes based on simulation and redesigning the NeoSphere trial were compared with those of a fixed-sample design. Results show that the adaptive design attains efficient and ethical advantages, and that the family-wise error rate is well controlled.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251319896"},"PeriodicalIF":1.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Estimating target population treatment effects in meta-analysis with individual participant-level data. 在个体参与者水平数据的meta分析中估计目标人群的治疗效果。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2025-01-19 DOI: 10.1177/09622802241307642

Hwanhee Hong, Lu Liu, Elizabeth A Stuart

{"title":"Estimating target population treatment effects in meta-analysis with individual participant-level data.","authors":"Hwanhee Hong, Lu Liu, Elizabeth A Stuart","doi":"10.1177/09622802241307642","DOIUrl":"10.1177/09622802241307642","url":null,"abstract":"Meta-analysis of randomized controlled trials is commonly used to evaluate treatments and inform policy decisions because it provides comprehensive summaries of all available evidence. However, meta-analyses are limited to draw population inference of treatment effects because they usually do not define target populations of interest specifically, and results of the individual randomized controlled trials in those meta-analyses may not generalize to the target populations. To leverage evidence from multiple randomized controlled trials in the generalizability context, we bridge the ideas from meta-analysis and causal inference. We integrate meta-analysis with causal inference approaches estimating target population average treatment effect. We evaluate the performance of the methods via simulation studies and apply the methods to generalize meta-analysis results from randomized controlled trials of treatments on schizophrenia to adults with schizophrenia who present to usual care settings in the United States. Our simulation results show that all methods perform comparably and well across different settings. The data analysis results show that the treatment effect in the target population is meaningful, although the effect size is smaller than the sample average treatment effect. We recommend applying multiple methods and comparing the results to ensure robustness, rather than relying on a single method.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"355-368"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Graphical methods to illustrate the nature of the relation between a continuous variable and the outcome when using restricted cubic splines with a Cox proportional hazards model. 在使用限制性三次样条和 Cox 比例危险模型时，用图形方法说明连续变量和结果之间关系的性质。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2024-10-21 DOI: 10.1177/09622802241287707

Peter C Austin

{"title":"Graphical methods to illustrate the nature of the relation between a continuous variable and the outcome when using restricted cubic splines with a Cox proportional hazards model.","authors":"Peter C Austin","doi":"10.1177/09622802241287707","DOIUrl":"10.1177/09622802241287707","url":null,"abstract":"Restricted cubic splines (RCS) allow analysts to model nonlinear relations between continuous covariates and the outcome in a regression model. When using RCS with the Cox proportional hazards model, there is no longer a single hazard ratio for the continuous variable. Instead, the hazard ratio depends on the values of the covariate for the two individuals being compared. Thus, using age as an example, when one assumes a linear relation between age and the log-hazard of the outcome there is a single hazard ratio comparing any two individuals whose age differs by 1 year. However, when allowing for a nonlinear relation between age and the log-hazard of the outcome, the hazard ratio comparing the hazard of the outcome between a 31- and a 30-year-old may differ from the hazard ratio comparing the hazard of the outcome between an 81- and an 80-year-old. We describe four methods to describe graphically the relation between a continuous variable and the outcome when using RCS with a Cox model. These graphical methods are based on plots of relative hazard ratios, cumulative incidence, hazards, and cumulative hazards against the continuous variable. Using a case study of patients presenting to hospital with heart failure and a series of mathematical derivations, we illustrate that the four methods will produce qualitatively similar conclusions about the nature of the relation between a continuous variable and the outcome. Use of these methods will allow for an intuitive communication of the nature of the relation between the variable and the outcome.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"277-285"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Generalized Bayesian kernel machine regression. 广义贝叶斯核机器回归

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2024-12-12 DOI: 10.1177/09622802241280784

Xichen Mou, Hongmei Zhang, S Hasan Arshad

引用次数: 0

LASSO-type instrumental variable selection methods with an application to Mendelian randomization. 应用于孟德尔随机化的 LASSO 型工具变量选择方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2024-11-15 DOI: 10.1177/09622802241281035

Muhammad Qasim, Kristofer Månsson, Narayanaswamy Balakrishnan

{"title":"LASSO-type instrumental variable selection methods with an application to Mendelian randomization.","authors":"Muhammad Qasim, Kristofer Månsson, Narayanaswamy Balakrishnan","doi":"10.1177/09622802241281035","DOIUrl":"10.1177/09622802241281035","url":null,"abstract":"Valid instrumental variables (IVs) must not directly impact the outcome variable and must also be uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid. The existing methods can lead to large bias relative to standard errors in situations with many weak and invalid instruments. In this paper, we derive a LASSO procedure for the k-class IV estimation methods in the linear IV model. In addition, we propose the jackknife IV method by using LASSO to address the problem of many weak invalid instruments in the case of heteroscedastic data. The proposed methods are robust for estimating causal effects in the presence of many invalid and valid instruments, with theoretical assurances of their execution. In addition, two-step numerical algorithms are developed for the estimation of causal effects. The performance of the proposed estimators is demonstrated via Monte Carlo simulations as well as an empirical application. We use Mendelian randomization as an application, wherein we estimate the causal effect of body mass index on the health-related quality of life index using single nucleotide polymorphisms as instruments for body mass index.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"201-223"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142628529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 contaminated regression model for count health data. 计数健康数据的污染回归模型。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2025-01-19 DOI: 10.1177/09622802241307613

Arnoldus F Otto, Johannes T Ferreira, Salvatore Daniele Tomarchio, Andriëtte Bekker, Antonio Punzo

{"title":"A contaminated regression model for count health data.","authors":"Arnoldus F Otto, Johannes T Ferreira, Salvatore Daniele Tomarchio, Andriëtte Bekker, Antonio Punzo","doi":"10.1177/09622802241307613","DOIUrl":"10.1177/09622802241307613","url":null,"abstract":"In medical and health research, investigators are often interested in countable quantities such as hospital length of stay (e.g., in days) or the number of doctor visits. Poisson regression is commonly used to model such count data, but this approach can't accommodate overdispersion-when the variance exceeds the mean. To address this issue, the negative binomial (NB) distribution (NB-D) and, by extension, NB regression provide a well-documented alternative. However, real-data applications present additional challenges that must be considered. Two such challenges are (i) the presence of (mild) outliers that can influence the performance of the NB-D and (ii) the availability of covariates that can enhance inference about the mean of the count variable of interest. To jointly address these issues, we propose the contaminated NB (cNB) distribution that exhibits the necessary flexibility to accommodate mild outliers. This model is shown to be simple and intuitive in interpretation. In addition to the parameters of the NB-D, our proposed model has a parameter describing the proportion of mild outliers and one specifying the degree of contamination. To allow available covariates to improve the estimation of the mean of the cNB distribution, we propose the cNB regression model. An expectation-maximization algorithm is outlined for parameter estimation, and its performance is evaluated through a parameter recovery study. The effectiveness of our model is demonstrated via a sensitivity analysis and on two health datasets, where it outperforms well-known count models. The methodology proposed is implemented in an R package which is available at https://github.com/arnootto/cNB.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"369-389"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143011863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Efficient estimation of the marginal mean of recurrent events in randomized controlled trials. 随机对照试验中复发事件边际均值的有效估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2025-01-19 DOI: 10.1177/09622802241289557

Luca Genetti, Giuliana Cortese, Henrik Ravn, Thomas Scheike

{"title":"Efficient estimation of the marginal mean of recurrent events in randomized controlled trials.","authors":"Luca Genetti, Giuliana Cortese, Henrik Ravn, Thomas Scheike","doi":"10.1177/09622802241289557","DOIUrl":"10.1177/09622802241289557","url":null,"abstract":"Recurrent events data are often encountered in biomedical settings, where individuals may also experience a terminal event such as death. A useful estimand to summarize such data is the marginal mean of the cumulative number of recurrent events up to a specific time horizon, allowing also for the possible presence of a terminal event. Recently, it was found that augmented estimators can estimate this quantity efficiently, providing improved inference. Improvement in efficiency by the use of covariate adjustment is increasing in popularity as the methods get further developed, and is supported by regulatory agencies EMA (2015) and FDA (2023). Motivated by these arguments, this article presents novel efficient estimators for clinical data from randomized controlled trials, accounting for additional information from auxiliary covariates. Moreover, in randomized studies when both right censoring and competing risks are present, we propose a novel doubly augmented estimator of the marginal mean , which has two optimal augmentation components due to censoring and randomization. We provide theoretical and asymptotic details for the novel estimators, also confirmed by simulation studies. Then, we discuss how to improve efficiency, both theoretically by computing the expected amount of variance reduction, and practically by showing the performance of different working regression models that are needed in the augmentation, when they are correctly specified or misspecified. The methods are applied to the LEADER study, a randomized controlled trial that studied cardiovascular safety of treatments in type 2 diabetes patients.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"258-276"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143011949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Adjusting for switches to multiple treatments: Should switches be handled separately or combined? 调整开关到多种处理：开关应该单独处理还是组合处理？

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2025-01-17 DOI: 10.1177/09622802241300049

Helen Bell Gorrod, Shahrul Mt-Isa, Jingyi Xuan, Kristel Vandormael, William Malbecq, Victoria Yorke-Edwards, Ian R White, Nicholas Latimer

{"title":"Adjusting for switches to multiple treatments: Should switches be handled separately or combined?","authors":"Helen Bell Gorrod, Shahrul Mt-Isa, Jingyi Xuan, Kristel Vandormael, William Malbecq, Victoria Yorke-Edwards, Ian R White, Nicholas Latimer","doi":"10.1177/09622802241300049","DOIUrl":"10.1177/09622802241300049","url":null,"abstract":"Treatment switching is common in randomised controlled trials (RCTs). Participants may switch onto a variety of different treatments, all of which may have different treatment effects. Adjustment analyses that target hypothetical estimands - estimating outcomes that would have been observed in the absence of treatment switching - have focused primarily on a single type of switch. In this study, we assess the performance of applications of inverse probability of censoring weights (IPCW) and two-stage estimation (TSE) which adjust for multiple switches by either (i) adjusting for each type of switching separately ('treatments separate') or (ii) adjusting for switches combined without differentiating between switched-to treatments ('treatments combined'). We simulate 48 scenarios in which RCT participants may switch to multiple treatments. Switch proportions, treatment effects, number of switched-to treatments and censoring proportions were varied. Method performance measures included mean percentage bias in restricted mean survival time and the frequency of model convergence. Similar levels of bias were produced by treatments combined and treatments separate in both TSE and IPCW applications. In the scenarios examined, there was no demonstrable advantage associated with adjusting for each type of switch separately, compared with adjusting for all switches together.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"322-335"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143011900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 design using restricted mean survival time as the primary endpoint in clinical trials. 采用限制平均生存时间作为临床试验主要终点的组序贯设计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2025-01-19 DOI: 10.1177/09622802241304111

Zhaojin Li, Xiang Geng, Yawen Hou, Zheng Chen

{"title":"Group sequential design using restricted mean survival time as the primary endpoint in clinical trials.","authors":"Zhaojin Li, Xiang Geng, Yawen Hou, Zheng Chen","doi":"10.1177/09622802241304111","DOIUrl":"10.1177/09622802241304111","url":null,"abstract":"The proportional hazards (PH) assumption is often violated in clinical trials. If the most commonly used Log-rank test is used for trial design in non-proportional hazard (NPH) cases, it will result in power loss or inflation, and the effect measures hazard ratio will become difficult to interpret. To circumvent the issue caused by the NPH for trial design and to make the effect measures easy to interpret and communicate, two simulation-free methods about restricted mean survival time group sequential (GS-RMST) design are introduced in this study: the independent increment GS-RMST (GS-RMSTi) design and the non-independent increment GS-RMST (GS-RMSTn) design. For the above two designs, the corresponding analytic expression of the variance-covariance matrix, the calculations of the stopping boundaries and sample size are given in the study. Simulation studies show that both designs can achieve the corresponding nominal type I error and nominal power. The GS-RMSTn simulation studies show that the Max-Combo test group sequential design is robust in different NPH scenarios and is suitable for discovering whether there is a treatment effect difference. However, it does not have a corresponding easy-to-interpret effect measure indicating effect difference magnitude. GS-RMST performs well in both PH and NPH scenarios, and it can obtain time-scale effect measures that are easy to understand by both physicians and patients. Examples of both GS-RMST designs are also illustrated.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"336-354"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143011973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects. 一种贝叶斯分层模型，用于疾病制图，该模型考虑了尺度和重尾潜在效应。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2024-12-10 DOI: 10.1177/09622802241293776

Victoire Michal, Alexandra M Schmidt, Laís Picinini Freitas, Oswaldo Gonçalves Cruz

{"title":"A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects.","authors":"Victoire Michal, Alexandra M Schmidt, Laís Picinini Freitas, Oswaldo Gonçalves Cruz","doi":"10.1177/09622802241293776","DOIUrl":"10.1177/09622802241293776","url":null,"abstract":"In disease mapping, the relative risk of a disease is commonly estimated across different areas within a region of interest. The number of cases in an area is often assumed to follow a Poisson distribution whose mean is decomposed as the product between an offset and the logarithm of the disease's relative risk. The log risk may be written as the sum of fixed effects and latent random effects. A modified Besag-York-Mollié (BYM2) model decomposes each latent effect into a weighted sum of independent and spatial effects. We build on the BYM2 model to allow for heavy-tailed latent effects and accommodate potentially outlying risks, after accounting for the fixed effects. We assume a scale mixture structure wherein the variance of the latent process changes across areas and allows for outlier identification. We propose two prior specifications for this scale mixture parameter. These are compared through various simulation studies and in the analysis of Zika cases from the first (2015-2016) epidemic in Rio de Janeiro city, Brazil. The simulation studies show that the proposed model always performs at least as well as an alternative available in the literature, and often better, both in terms of widely applicable information criterion, mean squared error and of outlier identification. In particular, the proposed parametrisations are more efficient, in terms of outlier detection, when outliers are neighbours. Our analysis of Zika cases finds 23 out of 160 districts of Rio as potential outliers, after accounting for the socio-development index. Our proposed model may help prioritise interventions and identify potential issues in the recording of cases.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"307-321"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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