Statistical Methods in Medical Research最新文献

A Bayesian latent class approach to causal inference with longitudinal data.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-12-12 DOI: 10.1177/09622802241298704

Kuan Liu, Olli Saarela, George Tomlinson, Brian M Feldman, Eleanor Pullenayegum

{"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":"https://doi.org/10.1177/09622802241298704","url":null,"abstract":"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.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241298704"},"PeriodicalIF":1.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-12-12 DOI: 10.1177/09622802241299410

Takehiro Shoji, Jun Tsuchida, Hiroshi Yadohisa

{"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":"https://doi.org/10.1177/09622802241299410","url":null,"abstract":"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.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241299410"},"PeriodicalIF":1.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Approximation to the optimal allocation for response adaptive designs.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-12-12 DOI: 10.1177/09622802241293750

Yanqing Yi, Xikui Wang

{"title":"Approximation to the optimal allocation for response adaptive designs.","authors":"Yanqing Yi, Xikui Wang","doi":"10.1177/09622802241293750","DOIUrl":"https://doi.org/10.1177/09622802241293750","url":null,"abstract":"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 < 1.5%, which decreases further to < 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 < 0.7% probability for the trial to go in the undesired direction.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241293750"},"PeriodicalIF":1.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819217","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

Generalized Bayesian kernel machine regression. 广义贝叶斯核机器回归

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-12-12 DOI: 10.1177/09622802241280784

Xichen Mou, Hongmei Zhang, S Hasan Arshad

{"title":"Generalized Bayesian kernel machine regression.","authors":"Xichen Mou, Hongmei Zhang, S Hasan Arshad","doi":"10.1177/09622802241280784","DOIUrl":"https://doi.org/10.1177/09622802241280784","url":null,"abstract":"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.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241280784"},"PeriodicalIF":1.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819218","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

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

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-12-12 DOI: 10.1177/09622802241300845

Divan Aristo Burger, Sean van der Merwe, Janet van Niekerk, Emmanuel Lesaffre, Antoine Pironet

{"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":"https://doi.org/10.1177/09622802241300845","url":null,"abstract":"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.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241300845"},"PeriodicalIF":1.6,"publicationDate":"2024-12-12","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}

引用次数: 0

Is inverse probability of censoring weighting a safer choice than per-protocol analysis in clinical trials?

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-12-12 DOI: 10.1177/09622802241289559

Jingyi Xuan, Shahrul Mt-Isa, Nicholas Latimer, Helen Bell Gorrod, William Malbecq, Kristel Vandormael, Victoria Yorke-Edwards, Ian R White

{"title":"Is inverse probability of censoring weighting a safer choice than per-protocol analysis in clinical trials?","authors":"Jingyi Xuan, Shahrul Mt-Isa, Nicholas Latimer, Helen Bell Gorrod, William Malbecq, Kristel Vandormael, Victoria Yorke-Edwards, Ian R White","doi":"10.1177/09622802241289559","DOIUrl":"https://doi.org/10.1177/09622802241289559","url":null,"abstract":"Deviation from the treatment strategy under investigation occurs in many clinical trials. We term this intervention deviation. Per-protocol analyses are widely adopted to estimate a hypothetical estimand without the occurrence of intervention deviation. Per-protocol by censoring is prone to selection bias when intervention deviation is associated with time-varying confounders that also influence counterfactual outcomes. This can be corrected by inverse probability of censoring weighting, which gives extra weight to uncensored individuals who had similar prognostic characteristics to censored individuals. Such weights are computed by modelling selected covariates. Inverse probability of censoring weighting relies on the no unmeasured confounding assumption whose plausibility is not statistically testable. Suboptimal implementation of inverse probability of censoring weighting which violates the assumption will lead to bias. In a simulation study, we evaluated the performance of per-protocol and inverse probability of censoring weighting with different implementations to explore whether inverse probability of censoring weighting is a safe alternative to per-protocol. Scenarios were designed to vary intervention deviation in one or both arms with different prevalences, correlation between two confounders, effect of each confounder, and sample size. Results show that inverse probability of censoring weighting with different combinations of covariates outperforms per-protocol in most scenarios, except for an unusual case where selection bias caused by two confounders is in two directions, and 'cancels' out.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241289559"},"PeriodicalIF":1.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819219","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

Marginal semiparametric accelerated failure time cure model for clustered survival data.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-12-10 DOI: 10.1177/09622802241295335

Yi Niu, Duze Fan, Jie Ding, Yingwei Peng

{"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":"https://doi.org/10.1177/09622802241295335","url":null,"abstract":"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.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241295335"},"PeriodicalIF":1.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical selection of genetic and gene by environment interaction effects in high-dimensional mixed models.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-12-10 DOI: 10.1177/09622802241293768

Julien St-Pierre, Karim Oualkacha, Sahir Rai Bhatnagar

{"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":"https://doi.org/10.1177/09622802241293768","url":null,"abstract":"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.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241293768"},"PeriodicalIF":1.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-12-10 DOI: 10.1177/09622802241293776

Victoire Michal, Alexandra M Schmidt, Laís Picinini Freitas, Oswaldo Gonçalves Cruz

{"title":"A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects.","authors":"Victoire Michal, Alexandra M Schmidt, Laís Picinini Freitas, Oswaldo Gonçalves Cruz","doi":"10.1177/09622802241293776","DOIUrl":"https://doi.org/10.1177/09622802241293776","url":null,"abstract":"In disease mapping, the relative risk of a disease is commonly estimated across different areas within a region of interest. The number of cases in an area is often assumed to follow a Poisson distribution whose mean is decomposed as the product between an offset and the logarithm of the disease's relative risk. The log risk may be written as the sum of fixed effects and latent random effects. A modified Besag-York-Mollié (BYM2) model decomposes each latent effect into a weighted sum of independent and spatial effects. We build on the BYM2 model to allow for heavy-tailed latent effects and accommodate potentially outlying risks, after accounting for the fixed effects. We assume a scale mixture structure wherein the variance of the latent process changes across areas and allows for outlier identification. We propose two prior specifications for this scale mixture parameter. These are compared through various simulation studies and in the analysis of Zika cases from the first (2015-2016) epidemic in Rio de Janeiro city, Brazil. The simulation studies show that the proposed model always performs at least as well as an alternative available in the literature, and often better, both in terms of widely applicable information criterion, mean squared error and of outlier identification. In particular, the proposed parametrisations are more efficient, in terms of outlier detection, when outliers are neighbours. Our analysis of Zika cases finds 23 out of 160 districts of Rio as potential outliers, after accounting for the socio-development index. Our proposed model may help prioritise interventions and identify potential issues in the recording of cases.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241293776"},"PeriodicalIF":1.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808097","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

Testing the equality of response rate functions for paired binary data with multiple groups.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-12-10 DOI: 10.1177/09622802241292672

Yufei Liu, Zhiming Li, Keyi Mou, Junhong Du

{"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":"https://doi.org/10.1177/09622802241292672","url":null,"abstract":"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.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241292672"},"PeriodicalIF":1.6,"publicationDate":"2024-12-10","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}

引用次数: 0