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

Evaluating prognostic biomarkers for survival outcomes subject to informative censoring. 评估受信息普查影响的生存结果的预后生物标志物。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-08-01 Epub Date: 2024-06-06 DOI: 10.1177/09622802241259170

Wei Liu, Danping Liu, Zhiwei Zhang

{"title":"Evaluating prognostic biomarkers for survival outcomes subject to informative censoring.","authors":"Wei Liu, Danping Liu, Zhiwei Zhang","doi":"10.1177/09622802241259170","DOIUrl":"10.1177/09622802241259170","url":null,"abstract":"Prognostic biomarkers for survival outcomes are widely used in clinical research and practice. Such biomarkers are often evaluated using a C-index as well as quantities based on time-dependent receiver operating characteristic curves. Existing methods for their evaluation generally assume that censoring is uninformative in the sense that the censoring time is independent of the failure time with or without conditioning on the biomarker under evaluation. With focus on the C-index and the area under a particular receiver operating characteristic curve, we describe and compare three estimation methods that account for informative censoring based on observed baseline covariates. Two of them are straightforward extensions of existing plug-in and inverse probability weighting methods for uninformative censoring. By appealing to semiparametric theory, we also develop a doubly robust, locally efficient method that is more robust than the plug-in and inverse probability weighting methods and typically more efficient than the inverse probability weighting method. The methods are evaluated and compared in a simulation study, and applied to real data from studies of breast cancer and heart failure.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1342-1354"},"PeriodicalIF":1.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141262800","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 robust regression model for bounded count health data. 有界计数健康数据的稳健回归模型。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-08-01 Epub Date: 2024-06-07 DOI: 10.1177/09622802241259178

Cristian L Bayes, Jorge Luis Bazán, Luis Valdivieso

{"title":"A robust regression model for bounded count health data.","authors":"Cristian L Bayes, Jorge Luis Bazán, Luis Valdivieso","doi":"10.1177/09622802241259178","DOIUrl":"10.1177/09622802241259178","url":null,"abstract":"Bounded count response data arise naturally in health applications. In general, the well-known beta-binomial regression model form the basis for analyzing this data, specially when we have overdispersed data. Little attention, however, has been given to the literature on the possibility of having extreme observations and overdispersed data. We propose in this work an extension of the beta-binomial regression model, named the beta-2-binomial regression model, which provides a rather flexible approach for fitting a regression model with a wide spectrum of bounded count response data sets under the presence of overdispersion, outliers, or excess of extreme observations. This distribution possesses more skewness and kurtosis than the beta-binomial model but preserves the same mean and variance form of the beta-binomial model. Additional properties of the beta-2-binomial distribution are derived including its behavior on the limits of its parametric space. A penalized maximum likelihood approach is considered to estimate parameters of this model and a residual analysis is included to assess departures from model assumptions as well as to detect outlier observations. Simulation studies, considering the robustness to outliers, are presented confirming that the beta-2-binomial regression model is a better robust alternative, in comparison with the binomial and beta-binomial regression models. We also found that the beta-2-binomial regression model outperformed the binomial and beta-binomial regression models in our applications of predicting liver cancer development in mice and the number of inappropriate days a patient spent in a hospital.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1392-1411"},"PeriodicalIF":1.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141284833","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

Unsupervised Liu-type shrinkage estimators for mixture of regression models. 回归模型混合物的无监督刘型收缩估计器。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-08-01 Epub Date: 2024-08-28 DOI: 10.1177/09622802241259175

Elsayed Ghanem, Armin Hatefi, Hamid Usefi

引用次数: 0

Point estimation of the 100p percent lethal dose using a novel penalised likelihood approach. 使用新型惩罚似然法对 100p 百分致死剂量进行点估算。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-08-01 Epub Date: 2024-06-12 DOI: 10.1177/09622802241259174

Yilei Ma, Youpeng Su, Peng Wang, Ping Yin

{"title":"Point estimation of the 100p percent lethal dose using a novel penalised likelihood approach.","authors":"Yilei Ma, Youpeng Su, Peng Wang, Ping Yin","doi":"10.1177/09622802241259174","DOIUrl":"10.1177/09622802241259174","url":null,"abstract":"Estimation of the 100p percent lethal dose (<math><msub><mtext>LD</mtext><mrow><mn>100</mn><mi>p</mi></mrow></msub></math>) is of great interest to pharmacologists for assessing the toxicity of certain compounds. However, most existing literature focuses on the interval estimation of <math><msub><mtext>LD</mtext><mrow><mn>100</mn><mi>p</mi></mrow></msub></math> and little attention has been paid to its point estimation. Currently, the most commonly used method for estimating the <math><msub><mtext>LD</mtext><mrow><mn>100</mn><mi>p</mi></mrow></msub></math> is the maximum likelihood estimator (MLE), which can be represented as a ratio estimator, with the denominator being the slope estimated from the logistic regression model. However, the MLE can be seriously biased when the sample size is small, a common nature in such studies, or when the dose-response curve is relatively flat (i.e. the slope approaches zero). In this study, we address these issues by developing a novel penalised maximum likelihood estimator (PMLE) that can prevent the denominator of the ratio from being close to zero. Similar to the MLE, the PMLE is computationally simple and thus can be conveniently used in practice. Moreover, with a suitable penalty parameter, we show that the PMLE can (a) reduce the bias to the second order with respect to the sample size and (b) avoid extreme estimates. Through simulation studies and real data applications, we show that the PMLE generally outperforms the existing methods in terms of bias and root mean square error.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1331-1341"},"PeriodicalIF":1.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141306885","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

Analyzing heterogeneity in biomarker discriminative performance through partial time-dependent receiver operating characteristic curve modeling. 通过部分时间依赖性接收器工作特征曲线建模分析生物标记物鉴别性能的异质性。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-08-01 Epub Date: 2024-07-25 DOI: 10.1177/09622802241262521

Xinyang Jiang, Wen Li, Kang Wang, Ruosha Li, Jing Ning

{"title":"Analyzing heterogeneity in biomarker discriminative performance through partial time-dependent receiver operating characteristic curve modeling.","authors":"Xinyang Jiang, Wen Li, Kang Wang, Ruosha Li, Jing Ning","doi":"10.1177/09622802241262521","DOIUrl":"10.1177/09622802241262521","url":null,"abstract":"This study investigates the heterogeneity of a biomarker's discriminative performance for predicting subsequent time-to-event outcomes across different patient subgroups. While the area under the curve (AUC) for the time-dependent receiver operating characteristic curve is commonly used to assess biomarker performance, the partial time-dependent AUC (PAUC) provides insights that are often more pertinent for population screening and diagnostic testing. To achieve this objective, we propose a regression model tailored for PAUC and develop two distinct estimation procedures for discrete and continuous covariates, employing a pseudo-partial likelihood method. Simulation studies are conducted to assess the performance of these procedures across various scenarios. We apply our model and inference procedure to the Alzheimer's Disease Neuroimaging Initiative data set to evaluate potential heterogeneities in the discriminative performance of biomarkers for early Alzheimer's disease diagnosis based on patients' characteristics.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1424-1436"},"PeriodicalIF":1.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11449645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760988","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

Minimizing confounding in comparative observational studies with time-to-event outcomes: An extensive comparison of covariate balancing methods using Monte Carlo simulation. 尽量减少具有时间到事件结果的比较观察研究中的混杂因素：使用蒙特卡洛模拟对协变量平衡方法进行广泛比较。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-08-01 Epub Date: 2024-07-25 DOI: 10.1177/09622802241262527

Guy Cafri, Stephen Fortin, Peter C Austin

{"title":"Minimizing confounding in comparative observational studies with time-to-event outcomes: An extensive comparison of covariate balancing methods using Monte Carlo simulation.","authors":"Guy Cafri, Stephen Fortin, Peter C Austin","doi":"10.1177/09622802241262527","DOIUrl":"10.1177/09622802241262527","url":null,"abstract":"Observational studies are frequently used in clinical research to estimate the effects of treatments or exposures on outcomes. To reduce the effects of confounding when estimating treatment effects, covariate balancing methods are frequently implemented. This study evaluated, using extensive Monte Carlo simulation, several methods of covariate balancing, and two methods for propensity score estimation, for estimating the average treatment effect on the treated using a hazard ratio from a Cox proportional hazards model. With respect to minimizing bias and maximizing accuracy (as measured by the mean square error) of the treatment effect, the average treatment effect on the treated weighting, fine stratification, and optimal full matching with a conventional logistic regression model for the propensity score performed best across all simulated conditions. Other methods performed well in specific circumstances, such as pair matching when sample sizes were large (n = 5000) and the proportion treated was < 0.25. Statistical power was generally higher for weighting methods than matching methods, and Type I error rates were at or below the nominal level for balancing methods with unbiased treatment effect estimates. There was also a decreasing effective sample size with an increasing number of strata, therefore for stratification-based weighting methods, it may be important to consider fewer strata. Generally, we recommend methods that performed well in our simulations, although the identification of methods that performed well is necessarily limited by the specific features of our simulation. The methods are illustrated using a real-world example comparing beta blockers and angiotensin-converting enzyme inhibitors among hypertensive patients at risk for incident stroke.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1437-1460"},"PeriodicalIF":1.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760992","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

Group lasso priors for Bayesian accelerated failure time models with left-truncated and interval-censored data. 贝叶斯加速故障时间模型左截断和区间校验数据的组套索先验。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-08-01 Epub Date: 2024-07-25 DOI: 10.1177/09622802241262523

Harrison T Reeder, Sebastien Haneuse, Kyu Ha Lee

{"title":"Group lasso priors for Bayesian accelerated failure time models with left-truncated and interval-censored data.","authors":"Harrison T Reeder, Sebastien Haneuse, Kyu Ha Lee","doi":"10.1177/09622802241262523","DOIUrl":"10.1177/09622802241262523","url":null,"abstract":"An important task in health research is to characterize time-to-event outcomes such as disease onset or mortality in terms of a potentially high-dimensional set of risk factors. For example, prospective cohort studies of Alzheimer's disease (AD) typically enroll older adults for observation over several decades to assess the long-term impact of genetic and other factors on cognitive decline and mortality. The accelerated failure time model is particularly well-suited to such studies, structuring covariate effects as \"horizontal\" changes to the survival quantiles that conceptually reflect shifts in the outcome distribution due to lifelong exposures. However, this modeling task is complicated by the enrollment of adults at differing ages, and intermittent follow-up visits leading to interval-censored outcome information. Moreover, genetic and clinical risk factors are not only high-dimensional, but characterized by underlying grouping structures, such as by function or gene location. Such grouped high-dimensional covariates require shrinkage methods that directly acknowledge this structure to facilitate variable selection and estimation. In this paper, we address these considerations directly by proposing a Bayesian accelerated failure time model with a group-structured lasso penalty, designed for left-truncated and interval-censored time-to-event data. We develop an R package with a Markov chain Monte Carlo sampler for estimation. We present a simulation study examining the performance of this method relative to an ordinary lasso penalty and apply the proposed method to identify groups of predictive genetic and clinical risk factors for AD in the Religious Orders Study and Memory and Aging Project prospective cohort studies of AD and dementia.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1412-1423"},"PeriodicalIF":1.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760991","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 dose-effect network meta-analysis model with application in antidepressants using restricted cubic splines. 使用受限三次样条的剂量效应网络荟萃分析模型在抗抑郁药物中的应用。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-08-01 Epub Date: 2022-02-24 DOI: 10.1177/09622802211070256

Tasnim Hamza, Toshi A Furukawa, Nicola Orsini, Andrea Cipriani, Cynthia P Iglesias, Georgia Salanti

{"title":"A dose-effect network meta-analysis model with application in antidepressants using restricted cubic splines.","authors":"Tasnim Hamza, Toshi A Furukawa, Nicola Orsini, Andrea Cipriani, Cynthia P Iglesias, Georgia Salanti","doi":"10.1177/09622802211070256","DOIUrl":"10.1177/09622802211070256","url":null,"abstract":"Network meta-analysis has been used to answer a range of clinical questions about the preferred intervention for a given condition. Although the effectiveness and safety of pharmacological agents depend on the dose administered, network meta-analysis applications typically ignore the role that drugs dosage plays in the results. This leads to more heterogeneity in the network. In this paper, we present a suite of network meta-analysis models that incorporate the dose-effect relationship using restricted cubic splines. We extend existing models into a dose-effect network meta-regression to account for study-level covariates and for groups of agents in a class-effect dose-effect network meta-analysis model. We apply our models to a network of aggregate data about the efficacy of 21 antidepressants and placebo for depression. We find that all antidepressants are more efficacious than placebo after a certain dose. Also, we identify the dose level at which each antidepressant's effect exceeds that of placebo and estimate the dose beyond which the effect of antidepressants no longer increases. When covariates were introduced to the model, we find that studies with small sample size tend to exaggerate antidepressants efficacy for several of the drugs. Our dose-effect network meta-analysis model with restricted cubic splines provides a flexible approach to modelling the dose-effect relationship in multiple interventions. Decision-makers can use our model to inform treatment choice.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1461-1472"},"PeriodicalIF":1.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39824807","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

Proportion of treatment effect explained: An overview of interpretations. 治疗效果比例解释：解释概述。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-07-01 Epub Date: 2024-07-25 DOI: 10.1177/09622802241259177

Florian Stijven, Ariel Alonso, Geert Molenberghs

{"title":"Proportion of treatment effect explained: An overview of interpretations.","authors":"Florian Stijven, Ariel Alonso, Geert Molenberghs","doi":"10.1177/09622802241259177","DOIUrl":"10.1177/09622802241259177","url":null,"abstract":"The selection of the primary endpoint in a clinical trial plays a critical role in determining the trial's success. Ideally, the primary endpoint is the clinically most relevant outcome, also termed the true endpoint. However, practical considerations, like extended follow-up, may complicate this choice, prompting the proposal to replace the true endpoint with so-called surrogate endpoints. Evaluating the validity of these surrogate endpoints is crucial, and a popular evaluation framework is based on the proportion of treatment effect explained (PTE). While methodological advancements in this area have focused primarily on estimation methods, interpretation remains a challenge hindering the practical use of the PTE. We review various ways to interpret the PTE. These interpretations-two causal and one non-causal-reveal connections between the PTE principal surrogacy, causal mediation analysis, and the prediction of trial-level treatment effects. A common limitation across these interpretations is the reliance on unverifiable assumptions. As such, we argue that the PTE is only meaningful when researchers are willing to make very strong assumptions. These challenges are also illustrated in an analysis of three hypothetical vaccine trials.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1278-1296"},"PeriodicalIF":1.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760993","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

Quantifying proportion of treatment effect by surrogate endpoint under heterogeneity. 在异质性条件下通过替代终点量化治疗效果的比例。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-07-01 Epub Date: 2024-05-08 DOI: 10.1177/09622802241247719

Xinzhou Guo, Florence T Bourgeois, Tianxi Cai

{"title":"Quantifying proportion of treatment effect by surrogate endpoint under heterogeneity.","authors":"Xinzhou Guo, Florence T Bourgeois, Tianxi Cai","doi":"10.1177/09622802241247719","DOIUrl":"10.1177/09622802241247719","url":null,"abstract":"When the primary endpoints in randomized clinical trials require long term follow-up or are costly to measure, it is often desirable to assess treatment effects on surrogate instead of clinical endpoints. Prior to adopting a surrogate endpoint for such purposes, the extent of its surrogacy on the primary endpoint must be assessed. There is a rich statistical literature on assessing surrogacy in the overall population, much of which is based on quantifying the proportion of treatment effect on the primary endpoint that is explained by the treatment effect on the surrogate endpoint. However, the surrogacy of an endpoint may vary across different patient subgroups according to baseline demographic characteristics, and limited methods are currently available to assess overall surrogacy in the presence of potential surrogacy heterogeneity. In this paper, we propose methods that incorporate covariates for baseline information, such as age, to improve overall surrogacy assessment. We use flexible semi-non-parametric modeling strategies to adjust for covariate effects and derive a robust estimate for the proportion of treatment effect of the covariate-adjusted surrogate endpoint. Simulation results suggest that the adjusted surrogate endpoint has greater proportion of treatment effect compared to the unadjusted surrogate endpoint. We apply the proposed method to data from a clinical trial of infliximab and assess the adequacy of the surrogate endpoint in the presence of age heterogeneity.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1152-1162"},"PeriodicalIF":1.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140877360","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