Statistical Methods in Medical Research最新文献_第8页

Multiplicative versus additive modelling of causal effects using instrumental variables for survival outcomes - a comparison. 使用工具变量对生存结果进行因果效应的乘法与加性建模-比较。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-01 Epub Date: 2024-12-10 DOI: 10.1177/09622802241293765

Eleanor R John, Michael J Crowther, Vanessa Didelez, Nuala A Sheehan

{"title":"Multiplicative versus additive modelling of causal effects using instrumental variables for survival outcomes - a comparison.","authors":"Eleanor R John, Michael J Crowther, Vanessa Didelez, Nuala A Sheehan","doi":"10.1177/09622802241293765","DOIUrl":"10.1177/09622802241293765","url":null,"abstract":"Instrumental variables (IVs) methods have recently gained popularity since, under certain assumptions, they may yield consistent causal effect estimators in the presence of unmeasured confounding. Existing simulation studies that evaluate the performance of IV approaches for time-to-event outcomes tend to consider either an additive or a multiplicative data-generating mechanism (DGM) and have been limited to an exponential constant baseline hazard model. In particular, the relative merits of additive versus multiplicative IV models have not been fully explored. All IV methods produce less biased estimators than naïve estimators that ignore unmeasured confounding, unless the IV is very weak and there is very little unmeasured confounding. However, the mean squared error of IV estimators may be higher than that of the naïve, biased but more stable estimators, especially when the IV is weak, the sample size is small to moderate, and the unmeasured confounding is strong. In addition, the sensitivity of IV methods to departures from their assumed DGMs differ substantially. Additive IV methods yield clearly biased effect estimators under a multiplicative DGM whereas multiplicative approaches appear less sensitive. All can be extremely variable. We would recommend that survival probabilities should always be reported alongside the relevant hazard contrasts as these can be more reliable and circumvent some of the known issues with causal interpretation of hazard contrasts. In summary, both additive IV and Cox IV methods can perform well in some circumstances but an awareness of their limitations is required in analyses of real data where the true underlying DGM is unknown.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"3-25"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808104","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

Approximation to the optimal allocation for response adaptive designs. 响应自适应设计的最优分配逼近。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-12-12 DOI: 10.1177/09622802241293750

Yanqing Yi, Xikui Wang

{"title":"Approximation to the optimal allocation for response adaptive designs.","authors":"Yanqing Yi, Xikui Wang","doi":"10.1177/09622802241293750","DOIUrl":"https://doi.org/10.1177/09622802241293750","url":null,"abstract":"We investigate the optimal allocation design for response adaptive clinical trials, under the average reward criterion. The treatment randomization process is formatted as a Markov decision process and the Bayesian method is used to summarize the information on treatment effects. A span-contraction operator is introduced and the average reward generated by the policy identified by the operator is shown to converge to the optimal value. We propose an algorithm to approximate the optimal treatment allocation using the Thompson sampling and the contraction operator. For the scenario of two treatments with binary responses and a sample size of 200 patients, simulation results demonstrate efficient learning features of the proposed method. It allocates a high proportion of patients to the better treatment while retaining a good statistical power and having a small probability for a trial going in the undesired direction. When the difference in success probability to detect is 0.2, the probability for a trial going in the unfavorable direction is < 1.5%, which decreases further to < 0.9% when the difference to detect is 0.3. For normally distribution responses, with a sample size of 100 patients, the proposed method assigns 13% more patients to the better treatment than the traditional complete randomization in detecting an effect size of difference 0.8, with a good statistical power and a < 0.7% probability for the trial to go in the undesired direction.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241293750"},"PeriodicalIF":1.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819217","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

Covariate-adjusted response-adaptive designs for semiparametric survival models. 半参数生存模型的协变量调整响应自适应设计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-25 DOI: 10.1177/09622802241287704

Ayon Mukherjee, Sayantee Jana, Stephen Coad

{"title":"Covariate-adjusted response-adaptive designs for semiparametric survival models.","authors":"Ayon Mukherjee, Sayantee Jana, Stephen Coad","doi":"10.1177/09622802241287704","DOIUrl":"https://doi.org/10.1177/09622802241287704","url":null,"abstract":"Covariate-adjusted response adaptive (CARA) designs are effective in increasing the expected number of patients receiving superior treatment in an ongoing clinical trial, given a patient's covariate profile. There has recently been extensive research on CARA designs with parametric distributional assumptions on patient responses. However, the range of applications for such designs becomes limited in real clinical trials. Sverdlov et al. have pointed out that irrespective of a specific parametric form of the survival outcomes, their proposed CARA designs based on the exponential model provide valid statistical inference, provided the final analysis is performed using the appropriate accelerated failure time (AFT) model. In real survival trials, however, the planned primary analysis is rarely conducted using an AFT model. The proposed CARA designs are developed obviating any distributional assumptions about the survival responses, relying only on the proportional hazards assumption between the two treatment arms. To meet the multiple experimental objectives of a clinical trial, the proposed designs are developed based on an optimal allocation approach. The covariate-adjusted doubly adaptive biased coin design and the covariate-adjusted efficient-randomized adaptive design are used to randomize the patients to achieve the derived targets on expectation. These expected targets are functions of the Cox regression coefficients that are estimated sequentially with the arrival of every new patient into the trial. The merits of the proposed designs are assessed using extensive simulation studies of their operating characteristics and then have been implemented to re-design a real-life confirmatory clinical trial.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241287704"},"PeriodicalIF":1.6,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717323","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

Model-based optimal randomization procedure for treatment-covariate interaction tests. 基于模型的治疗-共变因素交互检验最佳随机化程序。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-25 DOI: 10.1177/09622802241298703

Zhongqiang Liu

{"title":"Model-based optimal randomization procedure for treatment-covariate interaction tests.","authors":"Zhongqiang Liu","doi":"10.1177/09622802241298703","DOIUrl":"https://doi.org/10.1177/09622802241298703","url":null,"abstract":"Linear models are extensively used in the analysis of clinical trials. However, required model assumptions (e.g. homoscedasticity) may not be satisfied in practice, resulting in low power of treatment-covariate interaction tests. Various interaction tests have been proposed to improve the efficiency of detecting differences in treatment-covariate interactions. Aiming to fundamentally improve the power of treatment-covariate interaction tests, for heteroscedasticity of treatment responses, we develop a model-based optimal randomization procedure, referred to as model-based Neyman allocation (MNA) in this article. The derived limiting allocation proportion indicates that the procedure MNA is a generalization of response-adaptive randomization targeting Neyman allocation (RAR-NA). In theory, we demonstrate that the procedure MNA can maximize the power of treatment-covariate interaction tests. The issue of sample size estimation is also addressed. Simulation studies show, in the framework of the heteroscedastic linear model, compared with Pocock and Simon's minimization method and RAR-NA, the procedure MNA has the greatest power of tests for both systematic effects and treatment-covariate interactions, even under model misspecification. Finally, the efficiency of the procedure MNA is illustrated by a hypothetical case study based on a real schizophrenia clinical trial.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241298703"},"PeriodicalIF":1.6,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717324","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

Sensitivity analysis for unmeasured confounding in estimating the difference in restricted mean survival time. 在估算受限平均存活时间差异时对未测量混杂因素的敏感性分析。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-07 DOI: 10.1177/09622802241280782

Seungjae Lee, Ji Hoon Park, Woojoo Lee

引用次数: 0

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