Statistical Methods in Medical Research最新文献_第7页

Applying survey weights to ordinal regression models for improved inference in outcome-dependent samples with ordinal outcomes. 将调查权重应用于序数回归模型，以改进具有序数结果的结果依赖性样本的推断。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-23 DOI: 10.1177/09622802241282091

Aya A Mitani, Osvaldo Espin-Garcia, Daniel Fernández, Victoria Landsman

{"title":"Applying survey weights to ordinal regression models for improved inference in outcome-dependent samples with ordinal outcomes.","authors":"Aya A Mitani, Osvaldo Espin-Garcia, Daniel Fernández, Victoria Landsman","doi":"10.1177/09622802241282091","DOIUrl":"10.1177/09622802241282091","url":null,"abstract":"Researchers often use outcome-dependent sampling to study the exposure-outcome association. The case-control study is a widely used example of outcome-dependent sampling when the outcome is binary. When the outcome is ordinal, standard ordinal regression models generally produce biased coefficients when the sampling fractions depend on the values of the outcome variable. To address this problem, we studied the performance of survey-weighted ordinal regression models with weights inversely proportional to the sampling fractions. Through an extensive simulation study, we compared the performance of four ordinal regression models (SM: stereotype model; AC: adjacent-category logit model; CR: continuation-ratio logit model; and CM: cumulative logit model), with and without sampling weights under outcome-dependent sampling. We observed that when using weights, all four models produced estimates with negligible bias of all regression coefficients. Without weights, only stereotype model and adjacent-category logit model produced estimates with negligible to low bias for all coefficients except for the intercepts in all scenarios. In one scenario, the unweighted continuation-ratio logit model also produced estimates with low bias. The weighted stereotype model and adjacent-category logit model also produced estimates with lower relative root mean square errors compared to the unweighted models in most scenarios. In some of the scenarios with unevenly distributed categories, the weighted continuation-ratio logit model and cumulative logit model produced estimates with lower relative root mean square errors compared to the respective unweighted models. We used a study of knee osteoarthritis as an example.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2007-2026"},"PeriodicalIF":1.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142508312","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

Comparison of random forest methods for conditional average treatment effect estimation with a continuous treatment. 连续治疗条件下平均治疗效果估计的随机森林方法比较。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-09 DOI: 10.1177/09622802241275401

Sami Tabib, Denis Larocque

引用次数: 0

A seamless Phase I/II platform design with a time-to-event efficacy endpoint for potential COVID-19 therapies. 为潜在的 COVID-19 疗法设计了一个无缝的 I/II 期平台，其疗效终点为事件发生时间。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-10-14 DOI: 10.1177/09622802241288348

Thomas Jaki, Helen Barnett, Andrew Titman, Pavel Mozgunov

引用次数: 0

Testing for a treatment effect in a selected subgroup. 测试选定分组的治疗效果。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-11-01 Epub Date: 2024-09-25 DOI: 10.1177/09622802241277764

Nigel Stallard

引用次数: 0

Delayed kernels for longitudinal survival analysis and dynamic prediction. 用于纵向生存分析和动态预测的延迟核。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-10-01 Epub Date: 2024-08-30 DOI: 10.1177/09622802241275382

Annabel Louisa Davies, Anthony Cc Coolen, Tobias Galla

{"title":"Delayed kernels for longitudinal survival analysis and dynamic prediction.","authors":"Annabel Louisa Davies, Anthony Cc Coolen, Tobias Galla","doi":"10.1177/09622802241275382","DOIUrl":"10.1177/09622802241275382","url":null,"abstract":"Predicting patient survival probabilities based on observed covariates is an important assessment in clinical practice. These patient-specific covariates are often measured over multiple follow-up appointments. It is then of interest to predict survival based on the history of these longitudinal measurements, and to update predictions as more observations become available. The standard approaches to these so-called 'dynamic prediction' assessments are joint models and landmark analysis. Joint models involve high-dimensional parameterizations, and their computational complexity often prohibits including multiple longitudinal covariates. Landmark analysis is simpler, but discards a proportion of the available data at each 'landmark time'. In this work, we propose a 'delayed kernel' approach to dynamic prediction that sits somewhere in between the two standard methods in terms of complexity. By conditioning hazard rates directly on the covariate measurements over the observation time frame, we define a model that takes into account the full history of covariate measurements but is more practical and parsimonious than joint modelling. Time-dependent association kernels describe the impact of covariate changes at earlier times on the patient's hazard rate at later times. Under the constraints that our model (a) reduces to the standard Cox model for time-independent covariates, and (b) contains the instantaneous Cox model as a special case, we derive two natural kernel parameterizations. Upon application to three clinical data sets, we find that the predictive accuracy of the delayed kernel approach is comparable to that of the two existing standard methods.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1836-1858"},"PeriodicalIF":1.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142112180","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

A tight fit of the SIR dynamic epidemic model to daily cases of COVID-19 reported during the 2021-2022 Omicron surge in New York City: A novel approach. 将 SIR 动态流行病模型与纽约市 2021-2022 年 Omicron 疫情激增期间报告的 COVID-19 每日病例紧密拟合：一种新方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-10-01 DOI: 10.1177/09622802241277956

Jeffrey E Harris

{"title":"A tight fit of the SIR dynamic epidemic model to daily cases of COVID-19 reported during the 2021-2022 Omicron surge in New York City: A novel approach.","authors":"Jeffrey E Harris","doi":"10.1177/09622802241277956","DOIUrl":"10.1177/09622802241277956","url":null,"abstract":"We describe a novel approach for recovering the underlying parameters of the SIR dynamic epidemic model from observed data on case incidence. We formulate a discrete-time approximation of the original continuous-time model and search for the parameter vector that minimizes the standard least squares criterion function. We show that the gradient vector and matrix of second-order derivatives of the criterion function with respect to the parameters adhere to their own systems of difference equations and thus can be exactly calculated iteratively. Applying our new approach, we estimated a four-parameter SIR model from daily reported cases of COVID-19 during the SARS-CoV-2 Omicron/BA.1 surge of December 2021-March 2022 in New York City. The estimated SIR model showed a tight fit to the observed data, but less so when we excluded residual cases attributable to the Delta variant during the initial upswing of the wave in December. Our analyses of both the real-world COVID-19 data and simulated case incidence data revealed an important problem of weak parameter identification. While our methods permitted for the separate estimation of the infection transmission parameter and the infection persistence parameter, only a linear combination of these two key parameters could be estimated with precision. The SIR model appears to be an adequate reduced-form description of the Omicron surge, but it is not necessarily the correct structural model. Prior information above and beyond case incidence data may be required to sharply identify the parameters and thus distinguish between alternative epidemic models.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"33 10","pages":"1877-1898"},"PeriodicalIF":1.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669223","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

Joint modeling of zero-inflated longitudinal measurements and time-to-event outcomes with applications to dynamic prediction. 零膨胀纵向测量和时间到事件结果的联合建模，并应用于动态预测。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-10-01 Epub Date: 2024-10-07 DOI: 10.1177/09622802241268466

Mojtaba Ganjali, Taban Baghfalaki, Narayanaswamy Balakrishnan

{"title":"Joint modeling of zero-inflated longitudinal measurements and time-to-event outcomes with applications to dynamic prediction.","authors":"Mojtaba Ganjali, Taban Baghfalaki, Narayanaswamy Balakrishnan","doi":"10.1177/09622802241268466","DOIUrl":"10.1177/09622802241268466","url":null,"abstract":"In this article, we present a joint modeling approach for zero-inflated longitudinal count measurements and time-to-event outcomes. For the longitudinal sub-model, a mixed effects Hurdle model is utilized, incorporating various distributional assumptions such as zero-inflated Poisson, zero-inflated negative binomial, or zero-inflated generalized Poisson. For the time-to-event sub-model, a Cox proportional hazard model is applied. For the functional form linking the longitudinal outcome history to the hazard of the event, a linear combination is used. This combination is derived from the current values of the linear predictors of Hurdle mixed effects. Some other forms are also considered, including a linear combination of the current slopes of the linear predictors of Hurdle mixed effects as well as the shared random effects. A Markov chain Monte Carlo method is implemented for Bayesian parameter estimation. Dynamic prediction using joint modeling is highly valuable in personalized medicine, as discussed here for joint modeling of zero-inflated longitudinal count measurements and time-to-event outcomes. We assess and demonstrate the effectiveness of the proposed joint models through extensive simulation studies, with a specific emphasis on parameter estimation and dynamic predictions for both over-dispersed and under-dispersed data. We finally apply the joint model to longitudinal microbiome pregnancy and HIV data sets.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1731-1767"},"PeriodicalIF":1.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142381665","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

Average treatment effect on the treated, under lack of positivity. 在缺乏积极性的情况下，对被治疗者的平均治疗效果。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-10-01 Epub Date: 2024-09-09 DOI: 10.1177/09622802241269646

Yi Liu, Huiyue Li, Yunji Zhou, Roland A Matsouaka

{"title":"Average treatment effect on the treated, under lack of positivity.","authors":"Yi Liu, Huiyue Li, Yunji Zhou, Roland A Matsouaka","doi":"10.1177/09622802241269646","DOIUrl":"10.1177/09622802241269646","url":null,"abstract":"The use of propensity score methods has become ubiquitous in causal inference. At the heart of these methods is the positivity assumption. Violation of the positivity assumption leads to the presence of extreme propensity score weights when estimating average causal effects, which affects statistical inference. To circumvent this issue, trimming or truncating methods have been widely used. Unfortunately, these methods require that we pre-specify a threshold. There are a number of alternative methods to deal with the lack of positivity when we estimate the average treatment effect (ATE). However, no other methods exist beyond trimming and truncation to deal with the same issue when the goal is to estimate the average treatment effect on the treated (ATT). In this article, we propose a propensity score weight-based alternative for the ATT, called overlap weighted average treatment effect on the treated. The appeal of our proposed method lies in its ability to obtain similar or even better results than trimming and truncation while relaxing the constraint to choose an a priori threshold (or related measures). The performance of the proposed method is illustrated via a series of Monte Carlo simulations and a data analysis on racial disparities in health care expenditures.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1689-1717"},"PeriodicalIF":1.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154972","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

Flexible joint model for time-to-event and non-Gaussian longitudinal outcomes. 时间到事件和非高斯纵向结果的灵活联合模型。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-10-01 Epub Date: 2024-09-09 DOI: 10.1177/09622802241269010

Hortense Doms, Philippe Lambert, Catherine Legrand

引用次数: 0

Dynamic survival analysis: Modelling the hazard function via ordinary differential equations. 动态生存分析：通过常微分方程建立危险函数模型

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-10-01 Epub Date: 2024-08-20 DOI: 10.1177/09622802241268504

J Andres Christen, F Javier Rubio

{"title":"Dynamic survival analysis: Modelling the hazard function via ordinary differential equations.","authors":"J Andres Christen, F Javier Rubio","doi":"10.1177/09622802241268504","DOIUrl":"10.1177/09622802241268504","url":null,"abstract":"The hazard function represents one of the main quantities of interest in the analysis of survival data. We propose a general approach for parametrically modelling the dynamics of the hazard function using systems of autonomous ordinary differential equations (ODEs). This modelling approach can be used to provide qualitative and quantitative analyses of the evolution of the hazard function over time. Our proposal capitalises on the extensive literature on ODEs which, in particular, allows for establishing basic rules or laws on the dynamics of the hazard function via the use of autonomous ODEs. We show how to implement the proposed modelling framework in cases where there is an analytic solution to the system of ODEs or where an ODE solver is required to obtain a numerical solution. We focus on the use of a Bayesian modelling approach, but the proposed methodology can also be coupled with maximum likelihood estimation. A simulation study is presented to illustrate the performance of these models and the interplay of sample size and censoring. Two case studies using real data are presented to illustrate the use of the proposed approach and to highlight the interpretability of the corresponding models. We conclude with a discussion on potential extensions of our work and strategies to include covariates into our framework. Although we focus on examples of Medical Statistics, the proposed framework is applicable in any context where the interest lies in estimating and interpreting the dynamics of the hazard function.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1768-1782"},"PeriodicalIF":1.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005270","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