Statistical Methods in Medical Research最新文献

{"title":"Modelling extensions for multi-location studies in environmental epidemiology.","authors":"Pierre Masselot, Antonio Gasparrini","doi":"10.1177/09622802241313284","DOIUrl":"10.1177/09622802241313284","url":null,"abstract":"<p><p>Multi-location studies are increasingly used in environmental epidemiology. Their application is supported by designs and statistical techniques developed in the last decades, which however have known limitations. In this contribution, we propose an improved modelling framework that addresses these issues. Specifically, this flexible framework allows the direct modelling of demographic differences across locations, defining geographical variations linked to multiple vulnerability factors, capturing spatial heterogeneity and predicting risks to new locations, and improving the assessment of uncertainty. We illustrate these new developments in an analysis of temperature-mortality associations in Italian cities, providing fully reproducible R code and data.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"615-629"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189642","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}

{"title":"Causal survival embeddings: Non-parametric counterfactual inference under right-censoring.","authors":"Carlos García Meixide, Marcos Matabuena","doi":"10.1177/09622802241311455","DOIUrl":"10.1177/09622802241311455","url":null,"abstract":"<p><p>Counterfactual inference at the distributional level presents new challenges with censored targets, especially in modern healthcare problems. To mitigate selection bias in this context, we exploit the intrinsic structure of reproducing kernel Hilbert spaces (RKHS) harnessing the notion of kernel mean embedding. This enables the development of a non-parametric estimator of counterfactual survival functions. We provide rigorous theoretical guarantees regarding consistency and convergence rates of our new estimator under general hypotheses related to smoothness of the underlying RKHS. We illustrate the practical viability of our methodology through extensive simulations and a relevant case study: The SPRINT trial. Our estimatort presents a distinct perspective compared to existing methods within the literature, which often rely on semi-parametric approaches and confront limitations in causal interpretations of model parameters.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"574-593"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391932","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}

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">High-dimensional, outcome-dependent missing data problems: Models for the human <ns0:math><ns0:mi>K</ns0:mi><ns0:mi>I</ns0:mi><ns0:mi>R</ns0:mi></ns0:math> loci.","authors":"Lars Leonardus Joannes van der Burg, Hein Putter, Henning Baldauf, Jürgen Sauter, Johannes Schetelig, Liesbeth C de Wreede, Stefan Böhringer","doi":"10.1177/09622802241304112","DOIUrl":"10.1177/09622802241304112","url":null,"abstract":"<p><p>Missing data problems are common in biological, high-dimensional data, where data can be partially or completely missing. Algorithms have been developed to reconstruct the missing values by means of imputation or expectation-maximization algorithms. For missing data problems, it has been suggested that the regression model of interest should be incorporated into the imputation procedure to reduce bias of the regression coefficients. We here consider a challenging missing data problem, where diplotypes of the <i>KIR</i> loci are to be reconstructed. These loci are difficult to genotype, resulting in ambiguous genotype calls. We extend a previously proposed expectation-maximization algorithm by incorporating a potentially high-dimensional regression model to model the outcome. Three strategies are evaluated: (1) only allelic predictors, (2) allelic predictors and forward-backward selection on haplotype predictors, and (3) penalized regression on a saturated model. In a simulation study, we compared these strategies with a baseline expectation-maximization algorithm without outcome model. For extreme choices of effect sizes and missingness levels, the outcome-based expectation-maximization algorithms outperformed the no-outcome expectation-maximization algorithm. However, in all other cases, the no-outcome expectation-maximization algorithm performed either superior or comparable to the three strategies, suggesting the outcome model can have a harmful effect. In a data analysis concerning death after allogeneic hematopoietic stem cell transplantation as a function of donor <i>KIR</i> genes, expectation-maximization algorithms with and without outcome showed very similar results. In conclusion, outcome based missing data models in the high-dimensional setting have to be used with care and are likely to lead to biased results.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"440-456"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068018","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}

{"title":"Using Bayesian evidence synthesis to quantify uncertainty in population trends in smoking behaviour.","authors":"Stephen Wade, Peter Sarich, Pavla Vaneckova, Silvia Behar-Harpaz, Preston J Ngo, Paul B Grogan, Sonya Cressman, Coral E Gartner, John M Murray, Tony Blakely, Emily Banks, Martin C Tammemagi, Karen Canfell, Marianne F Weber, Michael Caruana","doi":"10.1177/09622802241310326","DOIUrl":"10.1177/09622802241310326","url":null,"abstract":"<p><p>Simulation models of smoking behaviour provide vital forecasts of exposure to inform policy targets, estimates of the burden of disease, and impacts of tobacco control interventions. A key element of useful model-based forecasts is a clear picture of uncertainty due to the data used to inform the model, however, assessment of this parameter uncertainty is incomplete in almost all tobacco control models. As a remedy, we demonstrate a Bayesian approach to model calibration that quantifies parameter uncertainty. With a model calibrated to Australian data, we observed that the smoking cessation rate in Australia has increased with calendar year since the late 20th century, and in 2016 people who smoked would quit at a rate of 4.7 quit-events per 100 person-years (90% equal-tailed interval (ETI): 4.5-4.9). We found that those who quit smoking before age 30 years switched to reporting that they never smoked at a rate of approximately 2% annually (90% ETI: 1.9-2.2%). The Bayesian approach demonstrated here can be used as a blueprint to model other population behaviours that are challenging to measure directly, and to provide a clearer picture of uncertainty to decision-makers.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"545-560"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400108","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}

{"title":"Semiparametric estimator for the covariate-specific receiver operating characteristic curve.","authors":"Pablo Martínez-Camblor, Juan Carlos Pardo-Fernández","doi":"10.1177/09622802241311458","DOIUrl":"10.1177/09622802241311458","url":null,"abstract":"<p><p>The study of the predictive ability of a marker is mainly based on the accuracy measures provided by the so-called confusion matrix. Besides, the area under the receiver operating characteristic curve has become a popular index for summarizing the overall accuracy of a marker. However, the nature of the relationship between the marker and the outcome, and the role that potential confounders play in this relationship could be fundamental in order to extrapolate the observed results. Directed acyclic graphs commonly used in epidemiology and in causality, could provide good feedback for learning the possibilities and limits of this extrapolation applied to the binary classification problem. Both the covariate-specific and the covariate-adjusted receiver operating characteristic curves are valuable tools, which can help to a better understanding of the real classification abilities of a marker. Since they are strongly related with the conditional distributions of the marker on the positive (subjects with the studied characteristic) and negative (subjects without the studied characteristic) populations, the use of proportional hazard regression models arises in a very natural way. We explore the use of flexible proportional hazard Cox regression models for estimating the covariate-specific and the covariate-adjusted receiver operating characteristic curves. We study their large- and finite-sample properties and apply the proposed estimators to a real-world problem. The developed code (in R language) is provided on Supplemental Material.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"594-614"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143024802","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}

{"title":"Multicategory matched learning for estimating optimal individualized treatment rules in observational studies with application to a hepatocellular carcinoma study.","authors":"Xuqiao Li, Qiuyan Zhou, Ying Wu, Ying Yan","doi":"10.1177/09622802241310328","DOIUrl":"10.1177/09622802241310328","url":null,"abstract":"<p><p>One primary goal of precision medicine is to estimate the individualized treatment rules that optimize patients' health outcomes based on individual characteristics. Health studies with multiple treatments are commonly seen in practice. However, most existing individualized treatment rule estimation methods were developed for the studies with binary treatments. Many require that the outcomes are fully observed. In this article, we propose a matching-based machine learning method to estimate the optimal individualized treatment rules in observational studies with multiple treatments when the outcomes are fully observed or right-censored. We establish theoretical property for the proposed method. It is compared with the existing competitive methods in simulation studies and a hepatocellular carcinoma study.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"508-522"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143024790","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}

{"title":"Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates.","authors":"Alexandra Blenkinsop, Lysandros Sofocleous, Francesco Di Lauro, Evangelia Georgia Kostaki, Ard van Sighem, Daniela Bezemer, Thijs van de Laar, Peter Reiss, Godelieve de Bree, Nikos Pantazis, Oliver Ratmann","doi":"10.1177/09622802241309750","DOIUrl":"10.1177/09622802241309750","url":null,"abstract":"<p><p>In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection. Most approaches for identifying transmission pairs do not account for the time passing since the divergence of pathogen variants in individuals, which is problematic in viruses with high within-host evolutionary rates. This prompted us to consider possible transmission pairs in terms of phylogenetic data and additional estimates of time since infection derived from clinical biomarkers. We develop Bayesian mixture models with an evolutionary clock as a signal component and additional mixed effects or covariate random functions describing the mixing weights to classify potential pairs into likely and unlikely transmission pairs. We demonstrate that although sources cannot be identified at the individual level with certainty, even with the additional data on time elapsed, inferences into the population-level sources of transmission are possible, and more accurate than using only phylogenetic data without time since infection estimates. We apply the proposed approach to estimate age-specific sources of HIV infection in Amsterdam tranamission networks among men who have sex with men between 2010 and 2021. This study demonstrates that infection time estimates provide informative data to characterize transmission sources, and shows how phylogenetic source attribution can then be done with multi-dimensional mixture models.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"523-544"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400099","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}

{"title":"Multivariate contaminated normal linear mixed models applied to Alzheimer's disease study with censored and missing data.","authors":"Tsung-I Lin, Wan-Lun Wang","doi":"10.1177/09622802241309349","DOIUrl":"10.1177/09622802241309349","url":null,"abstract":"<p><p>The article proposes a robust approach to jointly modeling multiple repeated clinical measures with intricate features. More specifically, we aim to expand the scope of the multivariate linear mixed model by using the multivariate contaminated normal distribution. The proposed model, called the multivariate contaminated normal linear mixed model with censored and missing responses (MCNLMM-CM), is designed to handle minor outliers effectively, while simultaneously accommodating censored measurements and intermittent missing responses. An expectation conditional maximization either algorithm is developed to estimate the parameters of the proposed model in situations involving missing at random responses. We also provide techniques for approximating the asymptotic standard errors of the parameters, recovering censored data, imputing missing values, and identifying outliers. A simulation study is conducted to evaluate the finite-sample properties of the parameter estimators and demonstrate the superior performance of the proposed model compared to existing models. The proposed methodology is inspired by and applied to data from the Alzheimer's disease neuroimaging initiative cohort study, which involves longitudinal clinical measurements of patients with mild cognitive impairment.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"490-507"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068021","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}

{"title":"A new cure model accounting for longitudinal data and flexible patterns of hazard ratios over time.","authors":"Can Xie, Xuelin Huang, Ruosha Li, Yu Shen, Nicholas J Short, Kapil N Bhalla","doi":"10.1177/09622802251320793","DOIUrl":"10.1177/09622802251320793","url":null,"abstract":"<p><p>With the advancement of medical treatments, many historically incurable diseases have become curable. An accurate estimation of the cure rates is of great interest. When there are no clear biomarker indicators for cure, the estimation of cure rate is intertwined with and influenced by the specification of hazard functions for uncured patients. Consequently, the commonly used proportional hazards (PH) assumption, when violated, may lead to biased cure rate estimation. Meanwhile, longitudinal biomarker measurements for individual patients are usually available. To accommodate non-PH functions and incorporate individual longitudinal biomarker trajectories, we propose a new joint model for cure, survival, and longitudinal data, with hazard ratios between different covariate subgroups flexibly varying over time. The proposed joint model has individual random effects shared between its longitudinal and cure-survival submodels. The regression parameters are estimated by maximization of the non-parametric likelihood via the Monte Carlo expectation-maximization algorithm. The standard error estimation applies a jackknife resampling method. In simulation studies, we consider crossing and non-crossing survival curves, and the proposed model provides unbiased estimates for the cure rates. Our proposed joint cure model is illustrated via a study of chronic myeloid leukemia.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251320793"},"PeriodicalIF":1.6,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143524483","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}

{"title":"Outcome adaptive propensity score methods for handling censoring and high-dimensionality: Application to insurance claims.","authors":"Jiacong Du, Youfei Yu, Min Zhang, Zhenke Wu, Andrew M Ryan, Bhramar Mukherjee","doi":"10.1177/09622802241306856","DOIUrl":"https://doi.org/10.1177/09622802241306856","url":null,"abstract":"<p><p>Propensity scores are commonly used to reduce the confounding bias in non-randomized observational studies for estimating the average treatment effect. An important assumption underlying this approach is that all confounders that are associated with both the treatment and the outcome of interest are measured and included in the propensity score model. In the absence of strong prior knowledge about potential confounders, researchers may agnostically want to adjust for a high-dimensional set of pre-treatment variables. As such, variable selection procedure is needed for propensity score estimation. In addition, studies show that including variables related to treatment only in the propensity score model may inflate the variance of the treatment effect estimators, while including variables that are predictive of only the outcome can improve efficiency. In this article, we propose to incorporate outcome-covariate relationship in the propensity score model by including the predicted binary outcome probability as a covariate. Our approach can be easily adapted to an ensemble of variable selection methods, including regularization methods and modern machine-learning tools based on classification and regression trees. We evaluate our method to estimate the treatment effects on a binary outcome, which is possibly censored, across multiple treatment groups. Simulation studies indicate that incorporating outcome probability for estimating the propensity scores can improve statistical efficiency and protect against model misspecification. The proposed methods are applied to a cohort of advanced-stage prostate cancer patients identified from a private insurance claims database for comparing the adverse effects of four commonly used drugs for treating castration-resistant prostate cancer.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241306856"},"PeriodicalIF":1.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143516792","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}