Statistical Methods in Medical Research最新文献

{"title":"Interval estimation in three-class receiver operating characteristic analysis: A fairly general approach based on the empirical likelihood.","authors":"Duc-Khanh To, Gianfranco Adimari, Monica Chiogna","doi":"10.1177/09622802241238998","DOIUrl":"10.1177/09622802241238998","url":null,"abstract":"<p><p>The empirical likelihood is a powerful nonparametric tool, that emulates its parametric counterpart-the parametric likelihood-preserving many of its large-sample properties. This article tackles the problem of assessing the discriminatory power of three-class diagnostic tests from an empirical likelihood perspective. In particular, we concentrate on interval estimation in a three-class receiver operating characteristic analysis, where a variety of inferential tasks could be of interest. We present novel theoretical results and tailored techniques studied to efficiently solve some of such tasks. Extensive simulation experiments are provided in a supporting role, with our novel proposals compared to existing competitors, when possible. It emerges that our new proposals are extremely flexible, being able to compete with contestants and appearing suited to accommodating several distributions, such, for example, mixtures, for target populations. We illustrate the application of the novel proposals with a real data example. The article ends with a discussion and a presentation of some directions for future research.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"875-893"},"PeriodicalIF":2.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159036","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 matching-based machine learning approach to estimating optimal dynamic treatment regimes with time-to-event outcomes.","authors":"Xuechen Wang, Hyejung Lee, Benjamin Haaland, Kathleen Kerrigan, Sonam Puri, Wallace Akerley, Jincheng Shen","doi":"10.1177/09622802241236954","DOIUrl":"10.1177/09622802241236954","url":null,"abstract":"<p><p>Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history. It is of great interest for clinicians and statisticians to identify an optimal dynamic treatment regime that can produce the best expected clinical outcome for each individual and thus maximize the treatment benefit over the population. Observational data impose various challenges for using statistical tools to estimate optimal dynamic treatment regimes. Notably, the task becomes more sophisticated when the clinical outcome of primary interest is time-to-event. Here, we propose a matching-based machine learning method to identify the optimal dynamic treatment regime with time-to-event outcomes subject to right-censoring using electronic health record data. In contrast to the established inverse probability weighting-based dynamic treatment regime methods, our proposed approach provides better protection against model misspecification and extreme weights in the context of treatment sequences, effectively addressing a prevalent challenge in the longitudinal analysis of electronic health record data. In simulations, the proposed method demonstrates robust performance across a range of scenarios. In addition, we illustrate the method with an application to estimate optimal dynamic treatment regimes for patients with advanced non-small cell lung cancer using a real-world, nationwide electronic health record database from Flatiron Health.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"794-806"},"PeriodicalIF":2.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159034","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":"Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs","authors":"Xueqi Wang, Xinyuan Chen, Keith S Goldfeld, Monica Taljaard, Fan Li","doi":"10.1177/09622802241247736","DOIUrl":"https://doi.org/10.1177/09622802241247736","url":null,"abstract":"The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel-arm cluster randomized design. While statistical methods have been developed for designing cluster randomized crossover trials, they have exclusively focused on testing the overall average treatment effect, with little attention to differential treatment effects across subpopulations. Recently, interest has grown in understanding whether treatment effects may vary across pre-specified patient subpopulations, such as those defined by demographic or clinical characteristics. In this article, we consider the two-treatment two-period cluster randomized crossover design under either a cross-sectional or closed-cohort sampling scheme, where it is of interest to detect the heterogeneity of treatment effect via an interaction test. Assuming a patterned correlation structure for both the covariate and the outcome, we derive new sample size formulas for testing the heterogeneity of treatment effect with continuous outcomes based on linear mixed models. Our formulas also address unequal cluster sizes and therefore allow us to analytically assess the impact of unequal cluster sizes on the power of the interaction test in cluster randomized crossover designs. We conduct simulations to confirm the accuracy of the proposed methods, and illustrate their application in two real cluster randomized crossover trials.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"31 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140834015","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":"Using joint models for longitudinal and time-to-event data to investigate the causal effect of salvage therapy after prostatectomy.","authors":"Dimitris Rizopoulos, Jeremy Mg Taylor, Grigorios Papageorgiou, Todd M Morgan","doi":"10.1177/09622802241239003","DOIUrl":"10.1177/09622802241239003","url":null,"abstract":"<p><p>Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen measurements. When prostate-specific antigen levels rise, salvage therapies are recommended in order to decrease the risk of metastasis. However, due to the side effects of these therapies and to avoid over-treatment, it is important to understand which patients and when to initiate these salvage therapies. In this work, we use the University of Michigan Prostatectomy Registry Data to tackle this question. Due to the observational nature of this data, we face the challenge that prostate-specific antigen is simultaneously a time-varying confounder and an intermediate variable for salvage therapy. We define different causal salvage therapy effects defined conditionally on different specifications of the longitudinal prostate-specific antigen history. We then illustrate how these effects can be estimated using the framework of joint models for longitudinal and time-to-event data. All proposed methodology is implemented in the freely-available R package <b>JMbayes2</b>.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"894-908"},"PeriodicalIF":1.6,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159037","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\">Comparisons of various estimates of the <ns0:math><ns0:mrow><ns0:msup><ns0:mi>I</ns0:mi><ns0:mn>2</ns0:mn></ns0:msup></ns0:mrow></ns0:math> statistic for quantifying between-study heterogeneity in meta-analysis.","authors":"Yipeng Wang, Natalie DelRocco, Lifeng Lin","doi":"10.1177/09622802241231496","DOIUrl":"10.1177/09622802241231496","url":null,"abstract":"<p><p>Assessing heterogeneity between studies is a critical step in determining whether studies can be combined and whether the synthesized results are reliable. The <math><mrow><msup><mi>I</mi><mn>2</mn></msup></mrow></math> statistic has been a popular measure for quantifying heterogeneity, but its usage has been challenged from various perspectives in recent years. In particular, it should not be considered an absolute measure of heterogeneity, and it could be subject to large uncertainties. As such, when using <math><mrow><msup><mi>I</mi><mn>2</mn></msup></mrow></math> to interpret the extent of heterogeneity, it is essential to account for its interval estimate. Various point and interval estimators exist for <math><mrow><msup><mi>I</mi><mn>2</mn></msup></mrow></math>. This article summarizes these estimators. In addition, we performed a simulation study under different scenarios to investigate preferable point and interval estimates of <math><mrow><msup><mi>I</mi><mn>2</mn></msup></mrow></math>. We found that the Sidik-Jonkman method gave precise point estimates for <math><mrow><msup><mi>I</mi><mn>2</mn></msup></mrow></math> when the between-study variance was large, while in other cases, the DerSimonian-Laird method was suggested to estimate <math><mrow><msup><mi>I</mi><mn>2</mn></msup></mrow></math>. When the effect measure was the mean difference or the standardized mean difference, the <math><mi>Q</mi></math>-profile method, the Biggerstaff-Jackson method, or the Jackson method was suggested to calculate the interval estimate for <math><mrow><msup><mi>I</mi><mn>2</mn></msup></mrow></math> due to reasonable interval length and more reliable coverage probabilities than various alternatives. For the same reason, the Kulinskaya-Dollinger method was recommended to calculate the interval estimate for <math><mrow><msup><mi>I</mi><mn>2</mn></msup></mrow></math> when the effect measure was the log odds ratio.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"745-764"},"PeriodicalIF":2.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159017","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":"Optimal allocation strategies in platform trials with continuous endpoints.","authors":"Marta Bofill Roig, Ekkehard Glimm, Tobias Mielke, Martin Posch","doi":"10.1177/09622802241239008","DOIUrl":"10.1177/09622802241239008","url":null,"abstract":"<p><p>Platform trials are randomized clinical trials that allow simultaneous comparison of multiple interventions, usually against a common control. Arms to test experimental interventions may enter and leave the platform over time. This implies that the number of experimental intervention arms in the trial may change as the trial progresses. Determining optimal allocation rates to allocate patients to the treatment and control arms in platform trials is challenging because the optimal allocation depends on the number of arms in the platform and the latter typically varies over time. In addition, the optimal allocation depends on the analysis strategy used and the optimality criteria considered. In this article, we derive optimal treatment allocation rates for platform trials with shared controls, assuming that a stratified estimation and a testing procedure based on a regression model are used to adjust for time trends. We consider both, analysis using concurrent controls only as well as analysis methods using concurrent and non-concurrent controls and assume that the total sample size is fixed. The objective function to be minimized is the maximum of the variances of the effect estimators. We show that the optimal solution depends on the entry time of the arms in the trial and, in general, does not correspond to the square root of <math><mi>k</mi></math> allocation rule used in classical multi-arm trials. We illustrate the optimal allocation and evaluate the power and type 1 error rate compared to trials using one-to-one and square root of <math><mi>k</mi></math> allocations by means of a case study.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"858-874"},"PeriodicalIF":2.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140176567","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":"Bayesian analysis of joint quantile regression for multi-response longitudinal data with application to primary biliary cirrhosis sequential cohort study","authors":"Yu-Zhu Tian, Man-Lai Tang, Catherine Wong, Mao-Zai Tian","doi":"10.1177/09622802241247725","DOIUrl":"https://doi.org/10.1177/09622802241247725","url":null,"abstract":"This article proposes a Bayesian approach for jointly estimating marginal conditional quantiles of multi-response longitudinal data with multivariate mixed effects model. The multivariate asymmetric Laplace distribution is employed to construct the working likelihood of the considered model. Penalization priors on regression parameters are incorporated into the working likelihood to conduct Bayesian high-dimensional inference. Markov chain Monte Carlo algorithm is used to obtain the fully conditional posterior distributions of all parameters and latent variables. Monte Carlo simulations are conducted to evaluate the sample performance of the proposed joint quantile regression approach. Finally, we analyze a longitudinal medical dataset of the primary biliary cirrhosis sequential cohort study to illustrate the real application of the proposed modeling method.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"53 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140810151","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":"Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of prognostic effects","authors":"Mayu Hiraishi, Ke Wan, Kensuke Tanioka, Hiroshi Yadohisa, Toshio Shimokawa","doi":"10.1177/09622802241247728","DOIUrl":"https://doi.org/10.1177/09622802241247728","url":null,"abstract":"We propose a novel framework based on the RuleFit method to estimate heterogeneous treatment effect in randomized clinical trials. The proposed method estimates a rule ensemble comprising a set of prognostic rules, a set of prescriptive rules, as well as the linear effects of the original predictor variables. The prescriptive rules provide an interpretable description of the heterogeneous treatment effect. By including a prognostic term in the proposed model, the selected rule is represented as an heterogeneous treatment effect that excludes other effects. We confirmed that the performance of the proposed method was equivalent to that of other ensemble learning methods through numerical simulations and demonstrated the interpretation of the proposed method using a real data application.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"43 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140810153","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":"The “Why” behind including “Y” in your imputation model","authors":"Lucy D’Agostino McGowan, Sarah C Lotspeich, Staci A Hepler","doi":"10.1177/09622802241244608","DOIUrl":"https://doi.org/10.1177/09622802241244608","url":null,"abstract":"Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation always holds and why this is sometimes true. We examine deterministic imputation (i.e. single imputation with fixed values) and stochastic imputation (i.e. single or multiple imputation with random values) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Moreover, we dispel common misconceptions about deterministic imputation models and demonstrate why the outcome should not be included in these models. This article aims to bridge the gap between imputation in theory and in practice, providing mathematical derivations to explain common statistical recommendations. We offer a better understanding of the considerations involved in imputing missing covariates and emphasize when it is necessary to include the outcome variable in the imputation model.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"219 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140616954","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 simple and robust parametric shared frailty model for recurrent events with the competing risk of death: An application to the Carvedilol Prospective Randomized Cumulative Survival trial","authors":"Jiren Sun, Thomas Cook","doi":"10.1177/09622802241236934","DOIUrl":"https://doi.org/10.1177/09622802241236934","url":null,"abstract":"Many non-fatal events can be considered recurrent in that they can occur repeatedly over time, and some researchers may be interested in the trajectory and relative risk of non-fatal events. With the competing risk of death, the treatment effect on the mean number of recurrent events is non-identifiable since the observed mean is a function of both the recurrent event and terminal event processes. In this paper, we assume independence between the non-fatal and the terminal event process, conditional on the shared frailty, to fit a parametric model that recovers the trajectory of, and identifies the effect of treatment on, the non-fatal event process in the presence of the competing risk of death. Simulation studies are conducted to verify the reliability of our estimators. We illustrate the method and perform model diagnostics using the Carvedilol Prospective Randomized Cumulative Survival trial which involves heart-failure events.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"70 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140617138","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}