Statistical Methods in Medical Research最新文献_第3页

A contaminated regression model for count health data. 计数健康数据的污染回归模型。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2025-01-19 DOI: 10.1177/09622802241307613

Arnoldus F Otto, Johannes T Ferreira, Salvatore Daniele Tomarchio, Andriëtte Bekker, Antonio Punzo

{"title":"A contaminated regression model for count health data.","authors":"Arnoldus F Otto, Johannes T Ferreira, Salvatore Daniele Tomarchio, Andriëtte Bekker, Antonio Punzo","doi":"10.1177/09622802241307613","DOIUrl":"10.1177/09622802241307613","url":null,"abstract":"In medical and health research, investigators are often interested in countable quantities such as hospital length of stay (e.g., in days) or the number of doctor visits. Poisson regression is commonly used to model such count data, but this approach can't accommodate overdispersion-when the variance exceeds the mean. To address this issue, the negative binomial (NB) distribution (NB-D) and, by extension, NB regression provide a well-documented alternative. However, real-data applications present additional challenges that must be considered. Two such challenges are (i) the presence of (mild) outliers that can influence the performance of the NB-D and (ii) the availability of covariates that can enhance inference about the mean of the count variable of interest. To jointly address these issues, we propose the contaminated NB (cNB) distribution that exhibits the necessary flexibility to accommodate mild outliers. This model is shown to be simple and intuitive in interpretation. In addition to the parameters of the NB-D, our proposed model has a parameter describing the proportion of mild outliers and one specifying the degree of contamination. To allow available covariates to improve the estimation of the mean of the cNB distribution, we propose the cNB regression model. An expectation-maximization algorithm is outlined for parameter estimation, and its performance is evaluated through a parameter recovery study. The effectiveness of our model is demonstrated via a sensitivity analysis and on two health datasets, where it outperforms well-known count models. The methodology proposed is implemented in an R package which is available at https://github.com/arnootto/cNB.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"369-389"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143011863","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

LASSO-type instrumental variable selection methods with an application to Mendelian randomization. 应用于孟德尔随机化的 LASSO 型工具变量选择方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2024-11-15 DOI: 10.1177/09622802241281035

Muhammad Qasim, Kristofer Månsson, Narayanaswamy Balakrishnan

{"title":"LASSO-type instrumental variable selection methods with an application to Mendelian randomization.","authors":"Muhammad Qasim, Kristofer Månsson, Narayanaswamy Balakrishnan","doi":"10.1177/09622802241281035","DOIUrl":"10.1177/09622802241281035","url":null,"abstract":"Valid instrumental variables (IVs) must not directly impact the outcome variable and must also be uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid. The existing methods can lead to large bias relative to standard errors in situations with many weak and invalid instruments. In this paper, we derive a LASSO procedure for the k-class IV estimation methods in the linear IV model. In addition, we propose the jackknife IV method by using LASSO to address the problem of many weak invalid instruments in the case of heteroscedastic data. The proposed methods are robust for estimating causal effects in the presence of many invalid and valid instruments, with theoretical assurances of their execution. In addition, two-step numerical algorithms are developed for the estimation of causal effects. The performance of the proposed estimators is demonstrated via Monte Carlo simulations as well as an empirical application. We use Mendelian randomization as an application, wherein we estimate the causal effect of body mass index on the health-related quality of life index using single nucleotide polymorphisms as instruments for body mass index.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"201-223"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142628529","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

Efficient estimation of the marginal mean of recurrent events in randomized controlled trials. 随机对照试验中复发事件边际均值的有效估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2025-01-19 DOI: 10.1177/09622802241289557

Luca Genetti, Giuliana Cortese, Henrik Ravn, Thomas Scheike

{"title":"Efficient estimation of the marginal mean of recurrent events in randomized controlled trials.","authors":"Luca Genetti, Giuliana Cortese, Henrik Ravn, Thomas Scheike","doi":"10.1177/09622802241289557","DOIUrl":"10.1177/09622802241289557","url":null,"abstract":"Recurrent events data are often encountered in biomedical settings, where individuals may also experience a terminal event such as death. A useful estimand to summarize such data is the marginal mean of the cumulative number of recurrent events up to a specific time horizon, allowing also for the possible presence of a terminal event. Recently, it was found that augmented estimators can estimate this quantity efficiently, providing improved inference. Improvement in efficiency by the use of covariate adjustment is increasing in popularity as the methods get further developed, and is supported by regulatory agencies EMA (2015) and FDA (2023). Motivated by these arguments, this article presents novel efficient estimators for clinical data from randomized controlled trials, accounting for additional information from auxiliary covariates. Moreover, in randomized studies when both right censoring and competing risks are present, we propose a novel doubly augmented estimator of the marginal mean , which has two optimal augmentation components due to censoring and randomization. We provide theoretical and asymptotic details for the novel estimators, also confirmed by simulation studies. Then, we discuss how to improve efficiency, both theoretically by computing the expected amount of variance reduction, and practically by showing the performance of different working regression models that are needed in the augmentation, when they are correctly specified or misspecified. The methods are applied to the LEADER study, a randomized controlled trial that studied cardiovascular safety of treatments in type 2 diabetes patients.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"258-276"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143011949","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 sequential design using restricted mean survival time as the primary endpoint in clinical trials. 采用限制平均生存时间作为临床试验主要终点的组序贯设计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2025-01-19 DOI: 10.1177/09622802241304111

Zhaojin Li, Xiang Geng, Yawen Hou, Zheng Chen

{"title":"Group sequential design using restricted mean survival time as the primary endpoint in clinical trials.","authors":"Zhaojin Li, Xiang Geng, Yawen Hou, Zheng Chen","doi":"10.1177/09622802241304111","DOIUrl":"10.1177/09622802241304111","url":null,"abstract":"The proportional hazards (PH) assumption is often violated in clinical trials. If the most commonly used Log-rank test is used for trial design in non-proportional hazard (NPH) cases, it will result in power loss or inflation, and the effect measures hazard ratio will become difficult to interpret. To circumvent the issue caused by the NPH for trial design and to make the effect measures easy to interpret and communicate, two simulation-free methods about restricted mean survival time group sequential (GS-RMST) design are introduced in this study: the independent increment GS-RMST (GS-RMSTi) design and the non-independent increment GS-RMST (GS-RMSTn) design. For the above two designs, the corresponding analytic expression of the variance-covariance matrix, the calculations of the stopping boundaries and sample size are given in the study. Simulation studies show that both designs can achieve the corresponding nominal type I error and nominal power. The GS-RMSTn simulation studies show that the Max-Combo test group sequential design is robust in different NPH scenarios and is suitable for discovering whether there is a treatment effect difference. However, it does not have a corresponding easy-to-interpret effect measure indicating effect difference magnitude. GS-RMST performs well in both PH and NPH scenarios, and it can obtain time-scale effect measures that are easy to understand by both physicians and patients. Examples of both GS-RMST designs are also illustrated.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"336-354"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143011973","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

Adjusting for switches to multiple treatments: Should switches be handled separately or combined? 调整开关到多种处理：开关应该单独处理还是组合处理？

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2025-01-17 DOI: 10.1177/09622802241300049

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

{"title":"Adjusting for switches to multiple treatments: Should switches be handled separately or combined?","authors":"Helen Bell Gorrod, Shahrul Mt-Isa, Jingyi Xuan, Kristel Vandormael, William Malbecq, Victoria Yorke-Edwards, Ian R White, Nicholas Latimer","doi":"10.1177/09622802241300049","DOIUrl":"10.1177/09622802241300049","url":null,"abstract":"Treatment switching is common in randomised controlled trials (RCTs). Participants may switch onto a variety of different treatments, all of which may have different treatment effects. Adjustment analyses that target hypothetical estimands - estimating outcomes that would have been observed in the absence of treatment switching - have focused primarily on a single type of switch. In this study, we assess the performance of applications of inverse probability of censoring weights (IPCW) and two-stage estimation (TSE) which adjust for multiple switches by either (i) adjusting for each type of switching separately ('treatments separate') or (ii) adjusting for switches combined without differentiating between switched-to treatments ('treatments combined'). We simulate 48 scenarios in which RCT participants may switch to multiple treatments. Switch proportions, treatment effects, number of switched-to treatments and censoring proportions were varied. Method performance measures included mean percentage bias in restricted mean survival time and the frequency of model convergence. Similar levels of bias were produced by treatments combined and treatments separate in both TSE and IPCW applications. In the scenarios examined, there was no demonstrable advantage associated with adjusting for each type of switch separately, compared with adjusting for all switches together.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"322-335"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143011900","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 regression analysis of clustered current status data with latent variables. 对带有潜变量的聚类现状数据进行联合回归分析。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2024-10-23 DOI: 10.1177/09622802241280792

Yanqin Feng, Sijie Wu, Jieli Ding

{"title":"Joint regression analysis of clustered current status data with latent variables.","authors":"Yanqin Feng, Sijie Wu, Jieli Ding","doi":"10.1177/09622802241280792","DOIUrl":"10.1177/09622802241280792","url":null,"abstract":"Clustered current status data frequently occur in many fields of survival studies. Some potential factors related to the hazards of interest cannot be directly observed but are characterized through multiple correlated observable surrogates. In this article, we propose a joint modeling method for regression analysis of clustered current status data with latent variables and potentially informative cluster sizes. The proposed models consist of a factor analysis model to characterize latent variables through their multiple surrogates and an additive hazards frailty model to investigate covariate effects on the failure time and incorporate intra-cluster correlations. We develop an estimation procedure that combines the expectation-maximization algorithm and the weighted estimating equations. The consistency and asymptotic normality of the proposed estimators are established. The finite-sample performance of the proposed method is assessed via a series of simulation studies. This procedure is applied to analyze clustered current status data from the National Toxicology Program on a tumorigenicity study given by the United States Department of Health and Human Services.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"224-242"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142508325","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. 一种贝叶斯分层模型，用于疾病制图，该模型考虑了尺度和重尾潜在效应。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub 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":"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":"307-321"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874469/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The effect of estimating prevalences on the population-wise error rate. 估计患病率对总体误差率的影响。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub Date: 2025-01-19 DOI: 10.1177/09622802241307237

Remi Luschei, Werner Brannath

{"title":"The effect of estimating prevalences on the population-wise error rate.","authors":"Remi Luschei, Werner Brannath","doi":"10.1177/09622802241307237","DOIUrl":"10.1177/09622802241307237","url":null,"abstract":"The population-wise error rate is a type I error rate for clinical trials with multiple target populations. In such trials, a treatment is tested for its efficacy in each population. The population-wise error rate is defined as the probability that a randomly selected, future patient will be exposed to an inefficient treatment based on the study results. It can be understood and computed as an average of strata-specific family wise error rates and involves the prevalences of these strata. A major issue of this concept is that the prevalences are usually unknown in practice, so that the population-wise error rate cannot be directly controlled. Instead, one could use an estimator based on the given sample, like their maximum-likelihood estimator under a multinomial distribution. In this article, we demonstrate through simulations that this does not substantially inflate the true population-wise error rate. We differentiate between the expected population-wise error rate, which is almost perfectly controlled, and study-specific values of the population-wise error rate which are conditioned on all subgroup sample sizes and vary within a narrow range. Thereby, we consider up to eight different overlapping populations and moderate to large sample sizes. In these settings, we also consider the maximum strata-wise family wise error rate, which is found to be, on average, at least bounded by twice the significance level used for population-wise error rate control.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"390-404"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143011999","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

Is inverse probability of censoring weighting a safer choice than per-protocol analysis in clinical trials? 在临床试验中，逆概率审查加权比按方案分析更安全吗？

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-02-01 Epub 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":"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":"286-306"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874582/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weighting methods for truncation by death in cluster-randomized trials.

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-31 DOI: 10.1177/09622802241309348

Dane Isenberg, Michael O Harhay, Nandita Mitra, Fan Li

{"title":"Weighting methods for truncation by death in cluster-randomized trials.","authors":"Dane Isenberg, Michael O Harhay, Nandita Mitra, Fan Li","doi":"10.1177/09622802241309348","DOIUrl":"https://doi.org/10.1177/09622802241309348","url":null,"abstract":"Patient-centered outcomes, such as quality of life and length of hospital stay, are the focus in a wide array of clinical studies. However, participants in randomized trials for elderly or critically and severely ill patient populations may have truncated or undefined non-mortality outcomes if they do not survive through the measurement time point. To address truncation by death, the survivor average causal effect has been proposed as a causally interpretable subgroup treatment effect defined under the principal stratification framework. However, the majority of methods for estimating the survivor average causal effect have been developed in the context of individually randomized trials. Only limited discussions have been centered around cluster-randomized trials, where methods typically involve strong distributional assumptions for outcome modeling. In this article, we propose two weighting methods to estimate the survivor average causal effect in cluster-randomized trials that obviate the need for potentially complicated outcome distribution modeling. We establish the requisite assumptions that address latent clustering effects to enable point identification of the survivor average causal effect, and we provide computationally efficient asymptotic variance estimators for each weighting estimator. In simulations, we evaluate our weighting estimators, demonstrating their finite-sample operating characteristics and robustness to certain departures from the identification assumptions. We illustrate our methods using data from a cluster-randomized trial to assess the impact of a sedation protocol on mechanical ventilation among children with acute respiratory failure.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802241309348"},"PeriodicalIF":1.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068032","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