STATISTICAL METHODS IN MEDICAL RESEARCH 是一份同行评审的学术期刊，是医学统计学所有主要领域文章的主要载体，也是所有医学统计学家的重要参考资料。这本独特的期刊只专注于统计和医学，旨在让专业人士了解医学界现在可以使用的许多强大的统计技术。该期刊是出版伦理委员会 (COPE) 的成员

Statistical Methods in Medical Research is a peer-reviewed academic journal that serves as a primary platform for articles in all major areas of medical statistics, making it an essential reference for all medical statisticians. This unique journal focuses exclusively on the intersection of statistics and medicine, aiming to inform professionals about the many powerful statistical techniques currently available in the medical field. Furthermore, the journal is a member of the Committee on Publication Ethics (COPE).

《Statistical Methods in Medical Research》是一本经过同行评审的学术期刊，涵盖医学统计学的所有主要领域，成为医学统计学家们的重要参考资料。这本独特的期刊专注于统计学与医学的结合，旨在帮助专业人士了解医学界当前可用的多种强大统计技术。此外，该期刊还是出版伦理委员会（COPE）的成员。

Statistical Methods in Medical Research

There is increasing interest in estimating the causal effects of treatments using observational data. Propensity-score matching methods are frequently used to adjust for differences in observed characteristics between treated and control individuals in observational studies. Survival or time-to-event outcomes occur frequently in the medical literature, but the use of propensity score methods in survival analysis has not been thoroughly investigated. This paper compares two approaches for estimating the Average Treatment Effect (ATE) on survival outcomes: Inverse Probability of Treatment Weighting (IPTW) and full matching. The performance of these methods was compared in an extensive set of simulations that varied the extent of confounding and the amount of misspecification of the propensity score model. We found that both IPTW and full matching resulted in estimation of marginal hazard ratios with negligible bias when the ATE was the target estimand and the treatment-selection process was weak to moderate. However, when the treatment-selection process was strong, both methods resulted in biased estimation of the true marginal hazard ratio, even when the propensity score model was correctly specified. When the propensity score model was correctly specified, bias tended to be lower for full matching than for IPTW. The reasons for these biases and for the differences between the two methods appeared to be due to some extreme weights generated for each method. Both methods tended to produce more extreme weights as the magnitude of the effects of covariates on treatment selection increased. Furthermore, more extreme weights were observed for IPTW than for full matching. However, the poorer performance of both methods in the presence of a strong treatment-selection process was mitigated by the use of IPTW with restriction and full matching with a caliper restriction when the propensity score model was correctly specified.

Peter C Austin

Elizabeth A Stuart

The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes

在估计治疗对生存结果的影响时，存在模型误设情况下逆概率治疗加权和倾向评分完全匹配的表现

In an ultra-high dimensional setting with a huge number of covariates, variable screening is useful for dimension reduction before applying a more refined method for model selection and statistical analysis. This paper proposes a new sure joint screening procedure for right-censored time-to-event data based on a sparsity-restricted semiparametric accelerated failure time model. Our method, referred to as Buckley-James assisted sure screening (BJASS), consists of an initial screening step using a sparsity-restricted least-squares estimate based on a synthetic time variable and a refinement screening step using a sparsity-restricted least-squares estimate with the Buckley-James imputed event times. The refinement step may be repeated several times to obtain more stable results. We show that with any fixed number of refinement steps, the BJASS procedure retains all important variables with probability tending to 1. Simulation results are presented to illustrate its performance in comparison with some marginal screening methods. Real data examples are provided using a diffuse large-B-cell lymphoma (DLBCL) data and a breast cancer data. We have implemented the BJASS method using Matlab and made it available to readers through Github https://github.com/yiucla/BJASS .

Yi Liu

Xiaolin Chen

Gang Li

A new joint screening method for right-censored time-to-event data with ultra-high dimensional covariates

一种针对具有超高维协变量的右删失时间事件数据的新联合筛选方法

Identifying and monitoring multiple disease biomarkers and other clinically important factors affecting the course of a disease, behavior or health status is of great clinical relevance. Yet conventional statistical practice generally falls far short of taking full advantage of the information available in multivariate longitudinal data for tracking the course of the outcome of interest. We demonstrate a method called multi-trajectory modeling that is designed to overcome this limitation. The method is a generalization of group-based trajectory modeling. Group-based trajectory modeling is designed to identify clusters of individuals who are following similar trajectories of a single indicator of interest such as post-operative fever or body mass index. Multi-trajectory modeling identifies latent clusters of individuals following similar trajectories across multiple indicators of an outcome of interest (e.g., the health status of chronic kidney disease patients as measured by their eGFR, hemoglobin, blood CO2 levels). Multi-trajectory modeling is an application of finite mixture modeling. We lay out the underlying likelihood function of the multi-trajectory model and demonstrate its use with two examples.

Daniel S Nagin

Bobby L Jones

Valéria Lima Passos

Richard E Tremblay

Stat Methods Med Res

Group-based multi-trajectory modeling

基于群体的多轨迹建模

The NonParametric Combination methodology (NPC) of dependent permutation tests allows the experimenter to face many complex multivariate testing problems and represents a convincing and powerful alternative to standard parametric methods. The main advantage of this approach lies in its flexibility in handling any type of variable (categorical and quantitative, with or without missing values) while at the same time taking dependencies among those variables into account without the need of modelling them. NPC methodology enables to deal with repeated measures, paired data, restricted alternative hypotheses, missing data (completely at random or not), high-dimensional and small sample size data. Hence, NPC methodology can offer a significant contribution to successful research in biomedical studies with several endpoints, since it provides reasonably efficient solutions and clear interpretations of inferential results. Pesarin F. Multivariate permutation tests: with application in biostatistics. Chichester-New York: John Wiley &amp;Sons, 2001; Pesarin F, Salmaso L. Permutation tests for complex data: theory, applications and software. Chichester, UK: John Wiley &amp;Sons, 2010. We focus on non-parametric permutation solutions to two real-case studies in ophthalmology, concerning complex-repeated measures problems. For each data set, different analyses are presented, thus highlighting characteristic aspects of the data structure itself. Our goal is to present different solutions to multivariate complex case studies, guiding researchers/readers to choose, from various possible interpretations of a problem, the one that has the highest flexibility and statistical power under a set of less stringent assumptions. MATLAB code has been implemented to carry out the analyses.

Chiara Brombin

Edoardo Midena

Luigi Salmaso

Robust non-parametric tests for complex-repeated measures problems in ophthalmology

眼科复杂重复测量问题的稳健非参数检验

Youden index, a linear function of sensitivity and specificity, provides a direct measurement of the highest diagnostic accuracy achievable by a biomarker. It is maximized at the cut-off point that optimizes the biomarker's overall classification rate while assigning equal weight to sensitivity and specificity. In this paper, we consider the problem of estimating the Youden index when only group-tested data are available. The unavailability of individual disease statuses poses a challenge, especially when there is differential false positives and negatives in disease screening. We propose both parametric and nonparametric procedures for estimation of the Youden index, and exemplify our methods by utilizing data from the National Health and Nutrition Examination Survey (NHANES) to evaluate the diagnostic ability of monocyte for predicting chlamydia.

Jin Yang

Aiyi Liu

Neil Perkins

Zhen Chen

Youden index estimation based on group-tested data

基于分组检测数据的Youden指数估计

We investigate the optimal allocation design for response adaptive clinical trials, under the average reward criterion. The treatment randomization process is formatted as a Markov decision process and the Bayesian method is used to summarize the information on treatment effects. A span-contraction operator is introduced and the average reward generated by the policy identified by the operator is shown to converge to the optimal value. We propose an algorithm to approximate the optimal treatment allocation using the Thompson sampling and the contraction operator. For the scenario of two treatments with binary responses and a sample size of 200 patients, simulation results demonstrate efficient learning features of the proposed method. It allocates a high proportion of patients to the better treatment while retaining a good statistical power and having a small probability for a trial going in the undesired direction. When the difference in success probability to detect is 0.2, the probability for a trial going in the unfavorable direction is &lt; 1.5%, which decreases further to &lt; 0.9% when the difference to detect is 0.3. For normally distribution responses, with a sample size of 100 patients, the proposed method assigns 13% more patients to the better treatment than the traditional complete randomization in detecting an effect size of difference 0.8, with a good statistical power and a &lt; 0.7% probability for the trial to go in the undesired direction.

Yanqing Yi

Xikui Wang

Approximation to the optimal allocation for response adaptive designs

响应自适应设计最优分配的近似方法

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.

Divan Aristo Burger

Sean van der Merwe

Janet van Niekerk

Emmanuel Lesaffre

Antoine Pironet

Joint quantile regression of longitudinal continuous proportions and time-to-event data: Application in medication adherence and persistence

纵向连续比例与时间-事件数据的联合分位数回归：药物依从性与持久性应用

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.

Takehiro Shoji

Jun Tsuchida

Hiroshi Yadohisa

Quantile outcome adaptive lasso: Covariate selection for inverse probability weighting estimator of quantile treatment effects

分位数结果自适应Lasso：分位数处理效应逆概率加权估计的协变量选择

Kernel machine regression is a nonparametric regression method widely applied in biomedical and environmental health research. It employs a kernel function to measure the similarities between sample pairs, effectively identifying significant exposures and assessing their nonlinear impacts on outcomes. This article introduces an enhanced framework, the generalized Bayesian kernel machine regression. In comparison to traditional kernel machine regression, generalized Bayesian kernel machine regression provides substantial flexibility to accommodate a broader array of outcome variables, ranging from continuous to binary and count data. Simulations show generalized Bayesian kernel machine regression can successfully identify the nonlinear relationships between independent variables and outcomes of various types. In the real data analysis, we applied generalized Bayesian kernel machine regression to uncover cytosine phosphate guanine sites linked to health-related conditions such as asthma and smoking. The results identify crucial cytosine phosphate guanine sites and provide insights into their complex, nonlinear relationships with outcome variables.

Xichen Mou

Hongmei Zhang

S Hasan Arshad

Generalized Bayesian kernel machine regression

广义贝叶斯核机器回归

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.

Abbreviation	Stat Methods Med Res
Journal Impact	1.54
Quartiles(Global)	STATISTICS & PROBABILITY(Q1)

ISSN	0962-2802, 1477-0334
h-index	104

Publication Information	Publisher: SAGE Publications Ltd，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1992，Proportion of original research papers: 94.07%，Self Citation Rate:6.20%， Gold OA Rate: 23.88%
Average review cycle	网友分享经验：>12周，或约稿
Average recruitment ratio	网友分享经验：较易

Statistical Methods in Medical Research

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