Statistical Methods in Medical Research最新文献_第6页

Uniformization and bounded Taylor series in Newton-Raphson method improves computational performance for a multistate transition model estimation and inference. 牛顿-拉夫逊方法中的统一化和有界泰勒级数提高了多态过渡模型估计和推理的计算性能。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-23 DOI: 10.1177/09622802241283882

Yuxi Zhu, Guy Brock, Lang Li

引用次数: 0

Joint modelling of longitudinal ordinal and multi-state data. 纵向序数和多状态数据的联合建模。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-11-05 DOI: 10.1177/09622802241281013

Behnaz Alafchi, Leili Tapak, Hossein Mahjub, Elaheh Talebi Ghane, Ghodratollah Roshanaei

引用次数: 0

Statistical methods for clinical trials interrupted by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic: A review. 因严重急性呼吸系统综合征--冠状病毒-2（SARS-CoV-2）大流行而中断的临床试验的统计方法：综述。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-30 DOI: 10.1177/09622802241288350

Joydeep Basu, Nicholas Parsons, Tim Friede, Nigel Stallard

{"title":"Statistical methods for clinical trials interrupted by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic: A review.","authors":"Joydeep Basu, Nicholas Parsons, Tim Friede, Nigel Stallard","doi":"10.1177/09622802241288350","DOIUrl":"10.1177/09622802241288350","url":null,"abstract":"Cancellation or delay of non-essential medical interventions, limitation of face-to-face assessments or outpatient attendance due to lockdown restrictions, illness or fear of hospital or healthcare centre visits, and halting of research to allow diversion of healthcare resources to focus on the pandemic led to the interruption of many clinical trials during the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic. Appropriate analysis approaches are now required for these interrupted trials. In trials with long follow-up and longitudinal outcomes, data may be available on early outcomes for many patients for whom final, primary outcome data were not observed. A natural question is then how these early data can best be used in the trial analysis. Although recommendations are available from regulators, funders, and methodologists, there is a lack of a review of recent work addressing this problem. This article reports a review of recent methods that can be used in the setting of the analysis of interrupted clinical trials with longitudinal outcomes with monotone missingness. A search for methodological papers published during the period 2020-2023 identified 43 relevant publications. We categorised these articles under the four broad themes of missing value imputation, modelling and covariate adjustment, simulation and estimands. Although motivated by the interruption due to SARS-CoV-2 and the resulting disease, the papers reviewed and methods discussed are also relevant to clinical trials interrupted for other reasons, with follow-up discontinued.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2131-2143"},"PeriodicalIF":1.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142547570","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

Bayesian blockwise inference for joint models of longitudinal and multistate data with application to longitudinal multimorbidity analysis. 应用于纵向多疾病分析的纵向和多状态数据联合模型的贝叶斯分块推断。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-21 DOI: 10.1177/09622802241281959

Sida Chen, Danilo Alvares, Christopher Jackson, Tom Marshall, Krish Nirantharakumar, Sylvia Richardson, Catherine L Saunders, Jessica K Barrett

{"title":"Bayesian blockwise inference for joint models of longitudinal and multistate data with application to longitudinal multimorbidity analysis.","authors":"Sida Chen, Danilo Alvares, Christopher Jackson, Tom Marshall, Krish Nirantharakumar, Sylvia Richardson, Catherine L Saunders, Jessica K Barrett","doi":"10.1177/09622802241281959","DOIUrl":"10.1177/09622802241281959","url":null,"abstract":"Multistate models provide a useful framework for modelling complex event history data in clinical settings and have recently been extended to the joint modelling framework to appropriately handle endogenous longitudinal covariates, such as repeatedly measured biomarkers, which are informative about health status and disease progression. However, the practical application of such joint models faces considerable computational challenges. Motivated by a longitudinal multimorbidity analysis of large-scale UK health records, we introduce novel Bayesian inference approaches for these models that are capable of handling complex multistate processes and large datasets with straightforward implementation. These approaches decompose the original estimation task into smaller inference blocks, leveraging parallel computing and facilitating flexible model specification and comparison. Using extensive simulation studies, we show that the proposed approaches achieve satisfactory estimation accuracy, with notable gains in computational efficiency compared to the standard Bayesian estimation strategy. We illustrate our approaches by analysing the coevolution of routinely measured systolic blood pressure and the progression of three important chronic conditions, using a large dataset from the Clinical Practice Research Datalink Aurum database. Our analysis reveals distinct and previously lesser-known association structures between systolic blood pressure and different disease transitions.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2027-2042"},"PeriodicalIF":1.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475113","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

Comparison of statistical methods for the analysis of patient-reported outcomes in randomised controlled trials: A simulation study. 随机对照试验中患者报告结果分析统计方法的比较：模拟研究。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-23 DOI: 10.1177/09622802241275361

Yirui Qian, Stephen J Walters, Richard M Jacques, Laura Flight

{"title":"Comparison of statistical methods for the analysis of patient-reported outcomes in randomised controlled trials: A simulation study.","authors":"Yirui Qian, Stephen J Walters, Richard M Jacques, Laura Flight","doi":"10.1177/09622802241275361","DOIUrl":"10.1177/09622802241275361","url":null,"abstract":"Patient-reported outcomes (PROs) that aim to measure patients' subjective attitudes towards their health or health-related conditions in various fields have been increasingly used in randomised controlled trials (RCTs). PRO data is likely to be bounded, discrete, and skewed. Although various statistical methods are available for the analysis of PROs in RCT settings, there is no consensus on what statistical methods are the most appropriate for use. This study aims to use simulation methods to compare the performance (in terms of bias, empirical standard error, coverage of the confidence interval, Type I error, and power) of three different statistical methods, multiple linear regression (MLR), Tobit regression (Tobit), and median regression (Median), to estimate a range of predefined treatment effects for a PRO in a two-arm balanced RCT. We assumed there was an underlying latent continuous outcome that the PRO was measuring, but the actual scores observed were equally spaced and discrete. This study found that MLR was associated with little bias of the estimated treatment effect, small standard errors, and appropriate coverage of the confidence interval under most scenarios. Tobit performed worse than MLR for analysing PROs with a small number of levels, but it had better performance when analysing PROs with more discrete values. Median showed extremely large bias and errors, associated with low power and coverage for most scenarios especially when the number of possible discrete values was small. We recommend MLR as a simple and universal statistical method for the analysis of PROs in RCT settings.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1920-1938"},"PeriodicalIF":1.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142508324","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

Instrumental variable analysis with categorical treatment. 分类处理的工具变量分析。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-30 DOI: 10.1177/09622802241281960

Amir Aamodt Kazemi, Inge Christoffer Olsen

{"title":"Instrumental variable analysis with categorical treatment.","authors":"Amir Aamodt Kazemi, Inge Christoffer Olsen","doi":"10.1177/09622802241281960","DOIUrl":"10.1177/09622802241281960","url":null,"abstract":"Current instrumental variable methodology focuses mainly on estimating causal effects for a dichotomous or an ordinal treatment variable. Situations with more than two unordered treatments are less explored. The challenge is that assumptions needed to derive point-estimators become increasingly stronger with the number of relevant treatment alternatives. In this article, we aim at deriving causal point-estimators for head-to-head comparisons of the effect of multiple relevant treatments or interventions. We will achieve this with a set of plausible and well-defined rationality assumptions while only considering ordinal instruments. We demonstrate that our methodology provides asymptotically unbiased estimators in the presence of unobserved confounding effects in a simulation study. We then apply the method to compare the effectiveness of five anti-inflammatory drugs in the treatment of rheumatoid arthritis. For this, we use a clinical data set from an observational study in Norway, where price is the primary determinant of the preferred drug and can therefore be considered as an instrument. The developed methodology provides an important addition to the toolbox for causal inference when comparing more than two interventions influenced by an instrumental variable.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2043-2061"},"PeriodicalIF":1.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142547569","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

Analysis of recurrent event data with spatial random effects using a Bayesian approach. 使用贝叶斯方法分析具有空间随机效应的重复事件数据。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-07 DOI: 10.1177/09622802241281027

Jin Jin, Liuquan Sun, Huang-Tz Ou, Pei-Fang Su

{"title":"Analysis of recurrent event data with spatial random effects using a Bayesian approach.","authors":"Jin Jin, Liuquan Sun, Huang-Tz Ou, Pei-Fang Su","doi":"10.1177/09622802241281027","DOIUrl":"10.1177/09622802241281027","url":null,"abstract":"Recurrent event data, which represent the occurrence of repeated incidences, are common in observational studies. Furthermore, collecting possible spatial correlations in health and environmental data is likely to provide more information for risk prediction. This article proposes a comprehensive proportional intensity model considering spatial random effects for recurrent event data using a Bayesian approach. The spatial information for areal data (where the spatial location is known up to a geographic unit such as a county) and georeferenced data (where the location is exactly observed) is examined. A traditional constant baseline intensity function, as well as a flexible piecewise constant baseline intensity function, are both under consideration. To estimate the parameters, a Markov chain Monte Carlo method with the Metropolis-Hastings algorithm and the adaptive Metropolis algorithm are applied. To assess the performance of model fitting, the deviance information criterion and log pseudo marginal likelihood are proposed. Overall, simulation studies demonstrate that the proposed model is significantly better than models that do not consider spatial effects if spatial correlations exist. Finally, our approach is implemented using a dataset related to the recurrence of cardiovascular diseases, which incorporates spatial information.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1993-2006"},"PeriodicalIF":1.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142381664","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 method for adverse effects estimation in observational studies with truncation by death. 用贝叶斯方法估算死亡截断的观察性研究中的不良反应。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-11-05 DOI: 10.1177/09622802241283170

Anthony Sisti, Andrew Zullo, Roee Gutman

{"title":"A Bayesian method for adverse effects estimation in observational studies with truncation by death.","authors":"Anthony Sisti, Andrew Zullo, Roee Gutman","doi":"10.1177/09622802241283170","DOIUrl":"10.1177/09622802241283170","url":null,"abstract":"Death among subjects is common in observational studies evaluating the causal effects of interventions among geriatric or severely ill patients. High mortality rates complicate the comparison of the prevalence of adverse events between interventions. This problem is often referred to as outcome \"truncation\" by death. A possible solution is to estimate the survivor average causal effect, an estimand that evaluates the effects of interventions among those who would have survived under both treatment assignments. However, because the survivor average causal effect does not include subjects who would have died under one or both arms, it does not consider the relationship between adverse events and death. We propose a Bayesian method which imputes the unobserved mortality and adverse event outcomes for each participant under the intervention they did not receive. Using the imputed outcomes we define a composite ordinal outcome for each patient, combining the occurrence of death and the adverse event in an increasing scale of severity. This allows for the comparison of the effects of the interventions on death and the adverse event simultaneously among the entire sample. We implement the procedure to analyze the incidence of heart failure among geriatric patients being treated for Type II diabetes with sulfonylureas or dipeptidyl peptidase-4 inhibitors.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2079-2097"},"PeriodicalIF":1.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584354","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

Enhancing DHA supplementation adherence: A Bayesian approach with finite mixture models and irregular interim schedules in adaptive trial designs. 提高 DHA 补充剂的依从性：在适应性试验设计中使用有限混合模型和不规则临时时间表的贝叶斯方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-04 DOI: 10.1177/09622802241283165

Sreejata Dutta, Samuel Boyd, Susan E Carlson, Danielle N Christifano, Gene T Lee, Sharla A Smith, Byron J Gajewski

{"title":"Enhancing DHA supplementation adherence: A Bayesian approach with finite mixture models and irregular interim schedules in adaptive trial designs.","authors":"Sreejata Dutta, Samuel Boyd, Susan E Carlson, Danielle N Christifano, Gene T Lee, Sharla A Smith, Byron J Gajewski","doi":"10.1177/09622802241283165","DOIUrl":"10.1177/09622802241283165","url":null,"abstract":"Docosahexaenoic acid (DHA) supplementation has proven beneficial in reducing preterm births. However, the challenge lies in addressing nonadherence to prescribed supplementation regimens-a hurdle that significantly impacts clinical trial outcomes. Conventional methods of adherence estimation, such as pill counts and questionnaires, usually fall short when estimating adherence within a specific dosage group. Thus, we propose a Bayesian finite mixture model to estimate adherence among women with low baseline red blood cell phospholipid DHA levels (<6%) receiving higher DHA doses. In our model, adherence is defined as the proportion of participants classified into one of the two distinct components in a normal mixture distribution. Subsequently, based on the estimands from the adherence model, we introduce a novel Bayesian adaptive trial design. Unlike conventional adaptive trials that employ regularly spaced interim schedules, the novelty of our proposed trial design lies in its adaptability to adherence percentages across the treatment arm through irregular interims. The irregular interims in the proposed trial are based on the effect size estimation informed by the finite mixture model. In summary, this study presents innovative methods for leveraging the capabilities of Bayesian finite mixture models in adherence analysis and the design of adaptive clinical trials.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2062-2078"},"PeriodicalIF":1.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576245/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142372913","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

Adaptive enrichment trial designs using joint modelling of longitudinal and time-to-event data. 利用纵向数据和事件时间数据联合建模的适应性强化试验设计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-16 DOI: 10.1177/09622802241287711

Abigail J Burdon, Richard D Baird, Thomas Jaki

{"title":"Adaptive enrichment trial designs using joint modelling of longitudinal and time-to-event data.","authors":"Abigail J Burdon, Richard D Baird, Thomas Jaki","doi":"10.1177/09622802241287711","DOIUrl":"10.1177/09622802241287711","url":null,"abstract":"Adaptive enrichment allows for pre-defined patient subgroups of interest to be investigated throughout the course of a clinical trial. These designs have gained attention in recent years because of their potential to shorten the trial's duration and identify effective therapies tailored to specific patient groups. We describe enrichment trials which consider long-term time-to-event outcomes but also incorporate additional short-term information from routinely collected longitudinal biomarkers. These methods are suitable for use in the setting where the trajectory of the biomarker may differ between subgroups and it is believed that the long-term endpoint is influenced by treatment, subgroup and biomarker. Methods are most promising when the majority of patients have biomarker measurements for at least two time points. We implement joint modelling of longitudinal and time-to-event data to define subgroup selection and stopping criteria and we show that the familywise error rate is protected in the strong sense. To assess the results, we perform a simulation study and find that, compared to the study where longitudinal biomarker observations are ignored, incorporating biomarker information leads to increases in power and the (sub)population which truly benefits from the experimental treatment being enriched with higher probability at the interim analysis. The investigations are motivated by a trial for the treatment of metastatic breast cancer and the parameter values for the simulation study are informed using real-world data where repeated circulating tumour DNA measurements and HER2 statuses are available for each patient and are used as our longitudinal data and subgroup identifiers, respectively.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2098-2114"},"PeriodicalIF":1.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475112","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