Observational studies最新文献_第3页

Causal Mediation Analysis for Effect Heterogeneity. 效应异质性的因果中介分析。

Observational studies Pub Date : 2026-03-14 eCollection Date: 2026-01-01 DOI: 10.1353/obs.2026.a985143

Jiaqing Zhang, Linda Valeri

引用次数: 0

Analytic Considerations for the Evaluation of Tuberculosis Preventative Therapy Effectiveness Among People Living with HIV. 艾滋病病毒感染者结核病预防治疗效果评价的分析思考

Observational studies Pub Date : 2025-11-04 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a973070

Lindsey M Schader, Seth Zissette, Fadimatu Mishara, Macarthur Charles, Sarita Shah, David Benkeser

引用次数: 0

Safe inference outside of randomized trials: Application of the stability-controlled quasi-experiment to the effects of three COVID-19 therapies. 随机试验外的安全推断：稳定性控制准实验对三种COVID-19治疗效果的应用

Observational studies Pub Date : 2025-11-04 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a973069

David Ami Wulf, Chad Hazlett, Brian L Hill, Jeffrey N Chiang, David Goodman-Meza, Bogdan Pasanuic, Onyebuchi A Arah, Kristine M Erlandson, Brian T Montague

{"title":"Safe inference outside of randomized trials: Application of the stability-controlled quasi-experiment to the effects of three COVID-19 therapies.","authors":"David Ami Wulf, Chad Hazlett, Brian L Hill, Jeffrey N Chiang, David Goodman-Meza, Bogdan Pasanuic, Onyebuchi A Arah, Kristine M Erlandson, Brian T Montague","doi":"10.1353/obs.2025.a973069","DOIUrl":"10.1353/obs.2025.a973069","url":null,"abstract":"When estimating the effects of medical therapies from their use outside of randomized trials, researchers often rely on assumptions that are difficult to justify and typically impossible to verify. The resulting estimates may thus be far from their intended causal targets, potentially making a harmful treatment appear beneficial or vice versa. We review the stability-controlled quasi-experiment (SCQE), a method suited to settings where a treatment's prevalence changes sharply over a short period, and apply it to assess the effects of remdesivir, hydroxychloroquine, and dexamethasone on COVID-19 mortality. Rather than requiring debate about the absence (or limited strength) of unobserved confounding, about \"parallel trends'', or other well-known strategies, the SCQE asks users to reason about a \"baseline trend'' assumption. In this setting, this asks\"How much could COVID-19 mortality have changed over a short period, absent the treatment change in question?'' Any plausible range for this assumption yields a corresponding range of plausible causal effect estimates. Conversely, SCQE clarifies what baseline trends must be defended or refuted in order to defend or refute a given conclusion about a treatment's efficacy or harm. Using data from two hospital systems early in the COVID-19 pandemic, we show that SCQE could have enabled safe yet informative inferences about treatment effects before clinical trial completion, producing conclusions consistent with eventual trial results.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"11 3","pages":"301-330"},"PeriodicalIF":0.0,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13155347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Errata to: "lmtp: An R Package for Estimating the Causal Effects of Modified Treatment Policies". 对“lmtp：用于估计修改治疗政策的因果效应的R包”的勘误表。

Observational studies Pub Date : 2025-11-04 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a973072

Nicholas Williams, Iván Díaz

引用次数: 0

Review of Handbook of Statistical Methods for Precision Medicine (edited by Eric Laber, Bibhas Chakraborty, Erica E.M. Moodie, Tianxi Cai, and Mark van der Laan). 《精准医学统计方法手册》（由Eric Laber、Bibhas Chakraborty、Erica E.M. Moodie、Tianxi Cai和Mark van der Laan编辑）。

Observational studies Pub Date : 2025-11-04 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a973071

Kristin A Linn

引用次数: 0

Constrained Design of a Binary Instrument in a Partially Linear Model. 部分线性模型下二元仪器的约束设计。

Observational studies Pub Date : 2025-11-04 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a973067

Tim Morrison, Minh Nguyen, Jonathan Chen, Michael Baiocchi, Art B Owen

{"title":"Constrained Design of a Binary Instrument in a Partially Linear Model.","authors":"Tim Morrison, Minh Nguyen, Jonathan Chen, Michael Baiocchi, Art B Owen","doi":"10.1353/obs.2025.a973067","DOIUrl":"10.1353/obs.2025.a973067","url":null,"abstract":"We study the question of how best to assign an encouragement in a randomized encouragement study. In our setting, units arrive with covariates, receive a nudge toward treatment or control, acquire one of those statuses in a way that need not align with the nudge, and finally have a response observed. The nudge can be modeled as a binary instrument if one assumes that it affects the response only via the treatment status. Our goal is to assign the nudge as a function of covariates in a way that best estimates the local average treatment effect (LATE). We assume a partially linear model, wherein the baseline model is non-parametric and the treatment term is linear in the covariates. Under this model, we outline a two-stage procedure to consistently and optimally estimate the LATE. Though the variance of the LATE is intractable, we derive a finite sample approximation and thus a design criterion to minimize. This criterion is convex, allowing for constraints that might arise for budgetary or ethical reasons. We prove conditions under which our solution asymptotically recovers the lowest true variance among all possible nudge propensities. A one-stage version of the algorithm is consistent but not necessarily optimal. We apply our method to a semi-synthetic example involving triage in an emergency department and find significant gains relative to a regression discontinuity design.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"11 3","pages":"213-248"},"PeriodicalIF":0.0,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13155346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 interventionist approach can address questions related to causes of effects if causes are considered as states instead of interventions. 如果将原因视为状态而不是干预，干预主义方法可以解决与结果的原因有关的问题。

Observational studies Pub Date : 2025-06-25 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a963648

Ian Shrier

{"title":"The interventionist approach can address questions related to causes of effects if causes are considered as states instead of interventions.","authors":"Ian Shrier","doi":"10.1353/obs.2025.a963648","DOIUrl":"10.1353/obs.2025.a963648","url":null,"abstract":"The interventionist approach to causal inference recommends that observational studies be framed as randomized trials with well-defined interventions to more precisely define the population of interest, exposure comparisons, assignment procedures, follow-up period, and outcomes. However, others suggest causes are not restricted to interventions, and the approach is too restrictive and will limit science. The described examples usually refer to questions about causes of effects/outcomes (rather than effects of interventions), where the 'cause' of interest often represents a mediator variable between an existing or hypothesized intervention. These questions are important because they represent the foundation for improving existing interventions and developing new interventions. In this article, I show how the interventionist approach can be used to try and answer these broader 'causes of effects/outcomes' questions. I use the sufficient casual set framework popularized by Rothman, which considers causes as states rather than interventions. The method is fully consistent with the potential outcomes approach and the need for well-defined counterfactuals. Whereas the effect of an intervention can theoretically be evaluated with an idealized single randomized trial, evaluating the causes of effects/outcomes requires evidence synthesis across multiple studies that each emulate a different target randomized trial. In other words, questions related to the effects of interventions are a special case where all the evidence that needs to be synthesized is available within a single idealized trial. Finally, considering states as causes also provides a transparent way to think about well-defined counterfactuals, and how factors that are commonly referred to as \"non-manipulable\" such as sex can also be studied as causes.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"11 2","pages":"189-208"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12959901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147367526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An overview of methods for receiver operating characteristic analysis, with an application to SARS-CoV-2 vaccine-induced humoral responses in solid organ transplant recipients. 接受者工作特征分析方法综述，并在实体器官移植受者SARS-CoV-2疫苗诱导的体液反应中的应用

Observational studies Pub Date : 2025-06-25 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a963645

Nathaniel P Dowd, Bryan Blette, James D Chappell, Natasha B Halasa, Andrew J Spieker

{"title":"An overview of methods for receiver operating characteristic analysis, with an application to SARS-CoV-2 vaccine-induced humoral responses in solid organ transplant recipients.","authors":"Nathaniel P Dowd, Bryan Blette, James D Chappell, Natasha B Halasa, Andrew J Spieker","doi":"10.1353/obs.2025.a963645","DOIUrl":"10.1353/obs.2025.a963645","url":null,"abstract":"Receiver operating characteristic (ROC) analysis is a tool to evaluate the capacity of an ordinal or numeric measure to distinguish between states, often employed in the evaluation of diagnostic tests. Assumptions regarding the nature of the data or the structure of the ROC curve can improve efficiency of estimation for the ROC curve itself or its associated area under the curve (a measure of stochastic ordering closely related to the target parameter of a Mann-Whitney <math><mi>U</mi></math> test). Despite recent interest in methods to conduct comparisons by way of stochastic ordering, ROC modeling strategies are not widely known but may be appreciated by practitioners of these methods. The major goals of this manuscript are to (1) provide an overview of non-parametric, parametric, and semiparametric frameworks for ROC curve estimation, (2) guide the practice of ROC analysis by offering considerations regarding methodological trade-offs, and (3) supply sample R code to further guide implementation. We utilize simulations to demonstrate various features of the methods we discuss. As an illustrative example, we analyze data from a recent cohort study in order to compare responses to SARS-CoV-2 vaccination between solid organ transplant recipients and healthy individuals.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"11 2","pages":"91-212"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12959903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147367555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Review of "A First Course in Causal Inference" by Peng Ding. 评丁鹏《因果推理第一课》。

Observational studies Pub Date : 2025-06-25 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a963649

Nicole E Pashley

引用次数: 0

A new four-arm within-study comparison: Design, implementation, and data. 一种新的四臂研究内比较：设计、实施和数据。

Observational studies Pub Date : 2025-06-25 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a963647

Bryan Keller, Vivian C Wong, Sangbaek Park, Jingru Zhang, Patrick Sheehan, Peter M Steiner

{"title":"A new four-arm within-study comparison: Design, implementation, and data.","authors":"Bryan Keller, Vivian C Wong, Sangbaek Park, Jingru Zhang, Patrick Sheehan, Peter M Steiner","doi":"10.1353/obs.2025.a963647","DOIUrl":"10.1353/obs.2025.a963647","url":null,"abstract":"Within-study comparisons (WSCs) use real, rather than simulated, data to compare estimates from observational studies against benchmarks from randomized controlled trials (RCTs). A primary goal of WSCs is to assess whether well-designed quasi-experimental designs (QEDs) can produce internally valid causal effect estimates comparable to those from RCTs. In this paper, we describe the design and implementation of a new type of WSC. Motivated by Shadish et al. (2008), we examine the impact of a mathematics training intervention and a vocabulary study session on posttest scores for mathematics and vocabulary, respectively. We extend the original design in three ways. First, before random assignment, we ask participants to express a preference for either the mathematics or vocabulary training session, after which they are randomly assigned regardless of preferences. This allows us to experimentally identify and estimate the overall average treatment effect (ATE) and two conditional ATEs: the average treatment effect on the treated (ATT) and the average treatment effect on the untreated (ATU). Second, participant recruitment and sample size (N = 2200) were determined through power analyses for comparing RCT and QED estimates, ensuring sufficient power for methodological comparisons. Finally, the study's eligibility criteria, recruitment, treatment allocation, and analysis plan were preregistered on the Open Science Foundation platform, and the data are publicly accessible. We believe that this WSC design and the resulting data set will be valuable for researchers seeking to evaluate causal inference methods and test identification assumptions using real-world data.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"11 2","pages":"153-188"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12959902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147367499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0