Statistical Methods in Medical Research最新文献_第3页

Implementing response-adaptive designs when responses are missing: Impute or ignore? 在缺少响应时实现响应自适应设计：归咎于还是忽略？

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-08-29 DOI: 10.1177/09622802251366843

Mia S Tackney, Sofía S Villar

{"title":"Implementing response-adaptive designs when responses are missing: Impute or ignore?","authors":"Mia S Tackney, Sofía S Villar","doi":"10.1177/09622802251366843","DOIUrl":"https://doi.org/10.1177/09622802251366843","url":null,"abstract":"Missing data is a widespread issue in clinical trials, but is particularly problematic for digital health interventions where disengagement is common and outcomes are likely to be missing not at random (MNAR). Trials that use response-adaptive designs need to handle missingness online and not simply at the end of the trial. We propose a novel online imputation strategy which allows previous imputations to be re-imputed given updated estimates of success probabilities. We additionally consider: (i) truncation of deterministic algorithms to prevent extreme realised treatment imbalance and (ii) changing the random component of semi-randomised algorithms. Through a simulation study based on a trial for a digital smoking cessation intervention, we illustrate how the strategy for handling missing responses can affect the exploration-exploitation tradeoff and the bias of the estimated success probabilities at the end of the trial. In the settings explored, we found that the exploration-exploitation tradeoff is affected particularly when arms have very different rates of missingness and we identified combinations of response-adaptive designs and missingness strategies that are particularly problematic. Further, we show that estimated success probabilities at the end of the trial can be biased not only due to optimistic sampling, but potentially also due to an MNAR missingness mechanism.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251366843"},"PeriodicalIF":1.9,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144969822","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

Imputation of incomplete ordinal and nominal data by predictive mean matching. 不完全有序和标称数据的预测均值匹配。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-08-17 DOI: 10.1177/09622802251362642

Peter C Austin, Stef van Buuren

{"title":"Imputation of incomplete ordinal and nominal data by predictive mean matching.","authors":"Peter C Austin, Stef van Buuren","doi":"10.1177/09622802251362642","DOIUrl":"https://doi.org/10.1177/09622802251362642","url":null,"abstract":"Multivariate imputation using chained equations is a popular algorithm for imputing missing data that entails specifying multivariable models through conditional distributions. Two standard imputation methods for imputing missing continuous variables are parametric imputation using a linear model and predictive mean matching. The default methods for imputing missing categorical variables are parametric imputation using multinomial logistic regression and ordinal logistic regression for imputing nominal and ordinal categorical variables, respectively. There is a paucity of research into the relative computational burden and the quality of statistical inferences when using predictive mean matching versus parametric imputation for imputing missing non-binary categorical variables. We used simulations to compare the performance of predictive mean matching with that of multinomial logistic regression and ordinal logistic regression for imputing categorical variables when the analysis model of scientific interest was a logistic or linear regression model. We varied the sample size (N = 500, 1000, 2500, and 5000), the rate of missing data (5%-50% in increments of 5%), and the number of levels of the categorical variable (3, 4, 5, and 6). In general, the performance of predictive mean matching compared very favorably to that of multinomial or ordinal logistic regression for imputing categorical variables when the analysis model was a logistic or linear regression model. This was true across a range of scenarios defined by sample size and the rate of missing data. Furthermore, the use of predictive mean matching was substantially faster, by a factor of 2-6. In conclusion, predictive mean matching can be used to impute categorical variables. The use of predictive mean matching to impute missing non-binary categorical variables substantially reduces computer processing time when conducting multiple imputation.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251362642"},"PeriodicalIF":1.9,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144875250","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

Selecting the optimal longitudinal cluster randomized design with a continuous outcome: Parallel-arm, crossover, or stepped-wedge. 选择具有连续结果的最佳纵向聚类随机设计：平行臂、交叉或楔步。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-08-11 DOI: 10.1177/09622802251360409

Jingxia Liu, Fan Li, Siobhan Sutcliffe, Graham A Colditz

{"title":"Selecting the optimal longitudinal cluster randomized design with a continuous outcome: Parallel-arm, crossover, or stepped-wedge.","authors":"Jingxia Liu, Fan Li, Siobhan Sutcliffe, Graham A Colditz","doi":"10.1177/09622802251360409","DOIUrl":"https://doi.org/10.1177/09622802251360409","url":null,"abstract":"The optimal designs (ODs) for parallel-arm longitudinal cluster randomized trials, multiple-period cluster randomized crossover (CRXO) trials, and stepped wedge cluster randomized trials (SW-CRTs), including closed-cohort and repeat cross-sectional designs, have been studied separately under a cost-efficiency framework based on generalized estimating equations (GEEs). However, whether a global OD exists across longitudinal designs and randomization schedules remains unknown. Therefore, this research addresses a critical gap by comparing OD feature across complete longitudinal cluster randomized trial designs with two treatment conditions and continuous outcomes. We define the OD as the design with either the lowest cost to obtain a desired level of power or the largest power given a fixed budget. For each of these ODs, we obtain the optimal number of clusters and the optimal cluster-period size (number of participants per cluster per period). To ensure equitable comparisons, we consider the GEE treatment effect estimator with the same block exchangeable correlation structure and develop OD algorithms with the lowest cost for each of six study designs. To obtain OD with the largest power, we summarize the previous and propose new OD algorithms and formulae. We suggest using the number of treatment sequences <math><mi>L</mi><mo>=</mo><mi>T</mi><mo>-</mo><mn>1</mn></math>, where T is the number of time-periods, in both the optimal closed-cohort and repeated cross-sectional SW-CRTs to have the lowest cost. This is consistent with our previous findings for ODs with the largest power in SW-CRTs. Comparing all six ODs, we conclude that optimal closed-cohort CRXO trials are global ODs, yielding both the lowest cost and largest power.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251360409"},"PeriodicalIF":1.9,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144817525","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

On prior smoothing with discrete spatial data in the context of disease mapping. 疾病制图中离散空间数据的先验平滑。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-08-08 DOI: 10.1177/09622802251362659

Garazi Retegui, Alan E Gelfand, Jaione Etxeberria, María Dolores Ugarte

{"title":"On prior smoothing with discrete spatial data in the context of disease mapping.","authors":"Garazi Retegui, Alan E Gelfand, Jaione Etxeberria, María Dolores Ugarte","doi":"10.1177/09622802251362659","DOIUrl":"https://doi.org/10.1177/09622802251362659","url":null,"abstract":"Disease mapping attempts to explain observed health event counts across areal units, typically using Markov random field models. These models rely on spatial priors to account for variation in raw relative risk or rate estimates. Spatial priors introduce some degree of smoothing, wherein, for any particular unit, empirical risk or incidence estimates are either adjusted towards a suitable mean or incorporate neighbor-based smoothing. While model explanation may be the primary focus, the literature lacks a comparison of the amount of smoothing introduced by different spatial priors. Additionally, there has been no investigation into how varying the parameters of these priors influences the resulting smoothing. This study examines seven commonly used spatial priors through both simulations and real data analyses. Using areal maps of peninsular Spain and England, we analyze smoothing effects using two datasets with associated populations at risk. We propose empirical metrics to quantify the smoothing achieved by each model and theoretical metrics to calibrate the expected extent of smoothing as a function of model parameters. We employ areal maps in order to quantitatively characterize the extent of smoothing within and across the models as well as to link the theoretical metrics to the empirical metrics.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251362659"},"PeriodicalIF":1.9,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144800318","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 note on response-adaptive randomization from a Bayesian prediction viewpoint. 从贝叶斯预测的观点看响应自适应随机化。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-08-06 DOI: 10.1177/09622802251360413

Alessandra Giovagnoli, Monia Lupparelli

引用次数: 0

Permutation tests for detecting treatment effect heterogeneity in cluster randomized trials. 在聚类随机试验中检测治疗效果异质性的排列检验。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-08-01 Epub Date: 2025-06-17 DOI: 10.1177/09622802251348999

Lara Maleyeff, Fan Li, Sebastien Haneuse, Rui Wang

{"title":"Permutation tests for detecting treatment effect heterogeneity in cluster randomized trials.","authors":"Lara Maleyeff, Fan Li, Sebastien Haneuse, Rui Wang","doi":"10.1177/09622802251348999","DOIUrl":"10.1177/09622802251348999","url":null,"abstract":"Cluster randomized trials are widely used in healthcare research for the evaluation of intervention strategies. Beyond estimating the average treatment effect, it is often of interest to assess whether the treatment effect varies across subgroups. While conventional methods based on tests of interaction terms between treatment and covariates can be used to detect treatment effect heterogeneity in cluster randomized trials, they typically rely on parametric assumptions that may not hold in practice. Adapting existing permutation tests from individually randomized trials, however, requires conceptual clarification and modification due to the multiple possible interpretations of treatment effect heterogeneity in the cluster randomized trial context. In this work, we develop variations of permutation tests and clarify key causal definitions in order to assess treatment effect heterogeneity in cluster randomized trials. Our procedure enables investigators to simultaneously test for effect modification across a large number of covariates, while maintaining nominal type I error rates and reasonable power in simulation studies. In the Pain Program for Active Coping and Training (PPACT) study, the proposed methods are able to detect treatment effect heterogeneity that was not identified by conventional methods assessing treatment-covariate interactions.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1617-1632"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12365356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317875","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

Design optimization of longitudinal studies using metaheuristics: Application to lithium pharmacokinetics. 采用元启发式纵向研究的设计优化：锂药代动力学的应用。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-08-01 Epub Date: 2025-06-19 DOI: 10.1177/09622802251350262

Mitchell Aaron Schepps, Jérémy Seurat, France Mentré, Weng Kee Wong

{"title":"Design optimization of longitudinal studies using metaheuristics: Application to lithium pharmacokinetics.","authors":"Mitchell Aaron Schepps, Jérémy Seurat, France Mentré, Weng Kee Wong","doi":"10.1177/09622802251350262","DOIUrl":"10.1177/09622802251350262","url":null,"abstract":"Lithium is recommended as a first line treatment for patients with bipolar disorder. However, only certain patients show a good response to the drug, and the variability and tolerability of lithium response are poorly understood. Greater precision in the early identification of individuals who are likely to respond well to lithium is a significant unmet clinical need. We create optimal designs to better understand the pharmacokinetic exposition of lithium for patients with and without a genetic covariate. From a Fisher information matrix based method, we find different optimal designs for estimating various parameters in a complicated pharmacokinetics/pharmacodynamics nonlinear mixed effects model with multiple physician specified constraints. Our approach uses flexible state-of-the-art metaheuristics to find various types of efficient designs, including multiple-objective optimal designs that can balance the competitiveness of the objectives and deliver higher efficiencies for more important objectives. Results from this article will be used as part of a broader study to implement efficient designs to better understand the exposition of sustained-release lithium in patients with bipolar disorder.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1633-1645"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144326884","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

Population-adjusted unanchored indirect comparisons of cancer therapies with borrowing of pan-tumor information. 人口调整的非锚定癌症治疗与泛肿瘤信息的间接比较。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-08-01 Epub Date: 2025-07-04 DOI: 10.1177/09622802251354922

Dylan Maciel, Shannon Cope, Walter Bouwmeester, Chunlin Qian, Beata Korytowsky, Jeroen P Jansen

{"title":"Population-adjusted unanchored indirect comparisons of cancer therapies with borrowing of pan-tumor information.","authors":"Dylan Maciel, Shannon Cope, Walter Bouwmeester, Chunlin Qian, Beata Korytowsky, Jeroen P Jansen","doi":"10.1177/09622802251354922","DOIUrl":"10.1177/09622802251354922","url":null,"abstract":"In clinical research of cancer therapy for rare mutations, trial designs must be adapted to accommodate the typically small sample sizes, and single-arm and basket trials have gained prominence. In this paper, we apply principles of Bayesian hierarchical methods and multilevel network meta-regression to propose a model for a pairwise population-adjusted unanchored indirect comparison of cancer therapies in different tumor types with borrowing of pan-tumor information. An individual-level regression model is defined for the single-arm trial of the intervention for which we have individual patient data. The aggregate data of the other trial for the competing intervention are fitted by integrating the covariate effects at the individual level over its covariate distribution to form the aggregate likelihood. To improve the estimation of the tumor type-specific relative treatment effects, we assume exchangeability reflecting the belief of a pan-tumor effect. The method is illustrated with a case study of adagrasib versus sotorasib in previously treated KRASG12C-mutated advanced/metastatic tumors: non-small cell lung cancer (NSCLC), colorectal cancer (CRC), and pancreatic ductal adenocarcinoma (PDAC). Adagrasib was associated with a greater tumor response than sotorasib according to the analyses: The odds ratios were 1.87 (1.21-2.84) for NSCLC; 2.08 (1.22-3.93) for CRC; and 2.02 (1.14-4.05) for PDAC. The analysis illustrated that a reasonably conservative assumption about the degree of similarity can result in more meaningful and interpretable findings. The proposed model allows for population adjustment and information sharing across tumor types when performing an unanchored indirect comparison of interventions for which it is believed a pan-tumor effect holds.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1684-1694"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561190","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

Using circulating tumor DNA as a novel biomarker of efficacy for dose-finding designs in oncology. 利用循环肿瘤DNA作为肿瘤剂量发现设计的新型生物标志物。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-08-01 Epub Date: 2025-07-01 DOI: 10.1177/09622802251350457

Xijin Chen, Pavel Mozgunov, Richard D Baird, Thomas Jaki

{"title":"Using circulating tumor DNA as a novel biomarker of efficacy for dose-finding designs in oncology.","authors":"Xijin Chen, Pavel Mozgunov, Richard D Baird, Thomas Jaki","doi":"10.1177/09622802251350457","DOIUrl":"10.1177/09622802251350457","url":null,"abstract":"Dose-finding trials are designed to identify a safe and potentially effective drug dose and schedule during the early phase of clinical trials. Historically, Bayesian adaptive dose-escalation methods in Phase I trials in cancer have mainly focussed on toxicity endpoints rather than efficacy endpoints. This is partly because efficacy readouts are often not available soon enough for dose escalation decisions. In the last decade, 'liquid biopsy' technologies have been developed, which may provide a readout of treatment response much earlier than conventional endpoints. This paper develops a novel design that uses a biomarker, circulating tumour DNA (ctDNA), with toxicity and activity outcomes in dose-finding studies. We compare the proposed approach based on repeated ctDNA measurement with existing Bayesian adaptive approaches under various scenarios of dose-toxicity, dose-efficacy relationship, and trajectories of regular ctDNA values over time. Simulation results show that the proposed approach can yield significantly shorter trial duration and may improve identification of the target dose. In addition, this approach has the potential to minimise the time individual patients spend on potentially inactive trial therapies. Using two different dose-finding designs, we demonstrate that the way we incorporate biomarker information is broadly applicable across different dose-finding designs and yields notable benefit in both cases.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1665-1683"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12365363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144529545","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

Paired count regressions for modeling the number of doctor consultations and non-prescribed drugs intake. 配对计数回归模型的医生咨询和非处方药摄入的数量。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-08-01 Epub Date: 2025-05-29 DOI: 10.1177/09622802251345332

Jussiane Nader Gonçalves, Wagner Barreto-Souza, Hernando Ombao

{"title":"Paired count regressions for modeling the number of doctor consultations and non-prescribed drugs intake.","authors":"Jussiane Nader Gonçalves, Wagner Barreto-Souza, Hernando Ombao","doi":"10.1177/09622802251345332","DOIUrl":"10.1177/09622802251345332","url":null,"abstract":"There are sundry practical situations in which paired count variables are correlated, thus requiring a joint estimation method. In this article, we introduce a flexible class of bivariate mixed Poisson regression models, which settle into an exponential-family (EF) distributed component for unobserved heterogeneity. The proposed bivariate mixed Poisson models deal with the phenomenon of overdispersion, typical of count data, and have flexibility in terms of the correlation structure. Thus, this novel class of models has a distinct advantage over the most widely used models because it captures both positive and negative correlations in the count data. Under the bivariate mixed Poisson model, inference of the parameters is conducted through the maximum likelihood method. Monte Carlo studies on assessing the finite-sample performance of the estimators of the parameters are presented. Furthermore, we employ a likelihood ratio statistic for testing the significance of certain sources of correlation and evaluate its performance via simulation studies. Moreover, model adequacy is addressed by using simulated envelopes for residual analysis, and also a randomized probability integral transformation for calibration model control. The proposed bivariate mixed Poisson model is considered for analyzing a healthcare dataset from the Australian Health Survey, where our aim is to study the association between the number of consultations with a doctor and the number of non-prescribed drug intake.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1553-1573"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12365358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175031","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