Statistical Methods in Medical Research最新文献

{"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":"https://doi.org/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":"9622802241304112"},"PeriodicalIF":1.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","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":"https://doi.org/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":"9622802241309349"},"PeriodicalIF":1.6,"publicationDate":"2025-01-31","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":"Semiparametric estimator for the covariate-specific receiver operating characteristic curve.","authors":"Pablo Martínez-Camblor, Juan Carlos Pardo-Fernández","doi":"10.1177/09622802241311458","DOIUrl":"https://doi.org/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":"9622802241311458"},"PeriodicalIF":1.6,"publicationDate":"2025-01-23","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":"https://doi.org/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":"9622802241310328"},"PeriodicalIF":1.6,"publicationDate":"2025-01-23","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":"Marginal semiparametric accelerated failure time cure model for clustered survival data.","authors":"Yi Niu, Duze Fan, Jie Ding, Yingwei Peng","doi":"10.1177/09622802241295335","DOIUrl":"10.1177/09622802241295335","url":null,"abstract":"<p><p>The semiparametric accelerated failure time mixture cure model is an appealing alternative to the proportional hazards mixture cure model in analyzing failure time data with long-term survivors. However, this model was only proposed for independent survival data and it has not been extended to clustered or correlated survival data, partly due to the complexity of the estimation method for the model. In this paper, we consider a marginal semiparametric accelerated failure time mixture cure model for clustered right-censored failure time data with a potential cure fraction. We overcome the complexity of the existing semiparametric method by proposing a generalized estimating equations approach based on the expectation-maximization algorithm to estimate the regression parameters in the model. The correlation structures within clusters are modeled by working correlation matrices in the proposed generalized estimating equations. The large sample properties of the regression estimators are established. Numerical studies demonstrate that the proposed estimation method is easy to use and robust to the misspecification of working matrices and that higher efficiency is achieved when the working correlation structure is closer to the true correlation structure. We apply the proposed model and estimation method to a contralateral breast cancer study and reveal new insights when the potential correlation between patients is taken into account.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"150-169"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808103","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":"A Bayesian latent class approach to causal inference with longitudinal data.","authors":"Kuan Liu, Olli Saarela, George Tomlinson, Brian M Feldman, Eleanor Pullenayegum","doi":"10.1177/09622802241298704","DOIUrl":"10.1177/09622802241298704","url":null,"abstract":"<p><p>Bayesian methods are becoming increasingly in demand in clinical and public health comparative effectiveness research. Limited literature has explored parametric Bayesian causal approaches to handle time-dependent treatment and time-dependent covariates. In this article, building on to the work on Bayesian g-computation, we propose a fully Bayesian causal approach, implemented using latent confounder classes which represent the patient's disease and health status. Our setting is suitable when the latent class represents a true disease state that the physician is able to infer without misclassification based on manifest variables. We consider a causal effect that is confounded by the visit-specific latent class in a longitudinal setting and formulate the joint likelihood of the treatment, outcome and latent class models conditionally on the class indicators. The proposed causal structure with latent classes features dimension reduction of time-dependent confounders. We examine the performance of the proposed method using simulation studies and compare the proposed method to other causal methods for longitudinal data with time-dependent treatment and time-dependent confounding. Our approach is illustrated through a study of the effectiveness of intravenous immunoglobulin in treating newly diagnosed juvenile dermatomyositis.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"55-68"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819216","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":"Quantile outcome adaptive lasso: Covariate selection for inverse probability weighting estimator of quantile treatment effects.","authors":"Takehiro Shoji, Jun Tsuchida, Hiroshi Yadohisa","doi":"10.1177/09622802241299410","DOIUrl":"10.1177/09622802241299410","url":null,"abstract":"<p><p>When using the propensity score method to estimate the treatment effects, it is important to select the covariates to be included in the propensity score model. The inclusion of covariates unrelated to the outcome in the propensity score model led to bias and large variance in the estimator of treatment effects. Many data-driven covariate selection methods have been proposed for selecting covariates related to outcomes. However, most of them assume an average treatment effect estimation and may not be designed to estimate quantile treatment effects (QTEs), which are the effects of treatment on the quantiles of outcome distribution. In QTE estimation, we consider two relation types with the outcome as the expected value and quantile point. To achieve this, we propose a data-driven covariate selection method for propensity score models that allows for the selection of covariates related to the expected value and quantile of the outcome for QTE estimation. Assuming the quantile regression model as an outcome regression model, covariate selection was performed using a regularization method with the partial regression coefficients of the quantile regression model as weights. The proposed method was applied to artificial data and a dataset of mothers and children born in King County, Washington, to compare the performance of existing methods and QTE estimators. As a result, the proposed method performs well in the presence of covariates related to both the expected value and quantile of the outcome.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"69-84"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819221","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":"Joint quantile regression of longitudinal continuous proportions and time-to-event data: Application in medication adherence and persistence.","authors":"Divan Aristo Burger, Sean van der Merwe, Janet van Niekerk, Emmanuel Lesaffre, Antoine Pironet","doi":"10.1177/09622802241300845","DOIUrl":"10.1177/09622802241300845","url":null,"abstract":"<p><p>This study introduces a novel joint modeling framework integrating quantile regression for longitudinal continuous proportions data with Cox regression for time-to-event analysis, employing integrated nested Laplace approximation for Bayesian inference. Our approach facilitates an examination across the entire distribution of patient health metrics over time, including the occurrence of key health events and their impact on patient outcomes, particularly in the context of medication adherence and persistence. Integrated nested Laplace approximation's fast computational speed significantly enhances the efficiency of this process, making the model particularly suitable for applications requiring rapid data analysis and updates. Applying this model to a dataset of patients who underwent treatment with atorvastatin, we demonstrate the significant impact of targeted interventions on improving medication adherence and persistence across various patient subgroups. Furthermore, we have developed a dynamic prediction method within this framework that rapidly estimates persistence probabilities based on the latest medication adherence data, demonstrating integrated nested Laplace approximation's quick updates and prediction capability. The simulation study validates the reliability of our modeling approach, evidenced by minimal bias and appropriate credible interval coverage probabilities across different quantile levels.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"111-130"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819220","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":"Hierarchical selection of genetic and gene by environment interaction effects in high-dimensional mixed models.","authors":"Julien St-Pierre, Karim Oualkacha, Sahir Rai Bhatnagar","doi":"10.1177/09622802241293768","DOIUrl":"10.1177/09622802241293768","url":null,"abstract":"<p><p>Interactions between genes and environmental factors may play a key role in the etiology of many common disorders. Several regularized generalized linear models have been proposed for hierarchical selection of gene by environment interaction effects, where a gene-environment interaction effect is selected only if the corresponding genetic main effect is also selected in the model. However, none of these methods allow to include random effects to account for population structure, subject relatedness and shared environmental exposure. In this article, we develop a unified approach based on regularized penalized quasi-likelihood estimation to perform hierarchical selection of gene-environment interaction effects in sparse regularized mixed models. We compare the selection and prediction accuracy of our proposed model with existing methods through simulations under the presence of population structure and shared environmental exposure. We show that for all simulation scenarios, including and additional random effect to account for the shared environmental exposure reduces the false positive rate and false discovery rate of our proposed method for selection of both gene-environment interaction and main effects. Using the <math><msub><mi>F</mi><mn>1</mn></msub></math> score as a balanced measure of the false discovery rate and true positive rate, we further show that in the hierarchical simulation scenarios, our method outperforms other methods for retrieving important gene-environment interaction effects. Finally, we apply our method to a real data application using the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, and found that our method retrieves previously reported significant loci.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"180-198"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808101","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":"Testing the equality of response rate functions for paired binary data with multiple groups.","authors":"Yufei Liu, Zhiming Li, Keyi Mou, Junhong Du","doi":"10.1177/09622802241292672","DOIUrl":"10.1177/09622802241292672","url":null,"abstract":"<p><p>In clinical trials, we often encounter observations from patients' paired organs. In paired correlated data, there exist various measures to evaluate the therapeutic responses, such as risk difference, relative risk ratio, and odds ratio. These measures are essentially some forms of response rate functions. Based on this point, this article aims to test the equality of response rate functions such that the homogeneity tests of the above measures are special cases. Under an interclass correlation model, the global and constrained maximum likelihood estimations are obtained through algorithms. Furthermore, we construct likelihood ratio, score, and Wald-type statistics and provide the explicit expressions of the corresponding tests based on the risk difference, relative risk ratio, and odds ratio. Monte Carlo simulations are conducted to compare the performance of the proposed methods in terms of the empirical type I error rates and powers. The results show that the score tests perform satisfactorily as their type I error rates are close to the specified nominal level, followed by the likelihood ratio test. The Wald-type tests exhibit poor performance, especially for small sample sizes. A real example is given to illustrate the three proposed test statistics.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"131-149"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808105","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}