Statistics in Medicine最新文献

{"title":"To Explain, to Predict, or to Describe: Figuring out the Study Goal [Commentary on \"On the Uses and Abuses of Regression Models\" by Carlin and Moreno-Betancur].","authors":"Galit Shmueli","doi":"10.1002/sim.10307","DOIUrl":"10.1002/sim.10307","url":null,"abstract":"<p><p>I strongly support Carlin and Moreno-Betancur's assertion that regression modeling (and in fact, any modeling) should be driven by the type of research question: descriptive, predictive, or causal. I share six points to highlight and clarify further confusions and suggest additional tricks to identify the right type. These include a focus on actions, individual vs. collective, additional causal typologies, the significance of variable names, software-related issues, and the role of data.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 13-14","pages":"e10307"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minding Averages: Comment on \"Experimental Design and Power Calculation in Omics Circadian Rhythmicity Detection Using the Cosinor Model\" by Zong et al.","authors":"Pål O Westermark","doi":"10.1002/sim.70044","DOIUrl":"10.1002/sim.70044","url":null,"abstract":"","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70044"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12094218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal Inference With Outcomes Truncated by Death and Missing Not at Random.","authors":"Wei Li, Yuan Liu, Shanshan Luo, Zhi Geng","doi":"10.1002/sim.70126","DOIUrl":"https://doi.org/10.1002/sim.70126","url":null,"abstract":"<p><p>In clinical trials, principal stratification analysis is commonly employed to address the issue of truncation by death, where a subject dies before the outcome can be measured. However, in practice, many survivor outcomes may remain uncollected or be missing not at random, posing a challenge to standard principal stratification analysis. In this article, we explore the identification, estimation, and bounds of the average treatment effect within a subpopulation of individuals who would potentially survive under both treatment and control conditions. We show that the causal parameter of interest can be identified by introducing a proxy variable that affects the outcome only through the principal strata, while requiring that the treatment variable does not directly affect the missingness mechanism. Subsequently, we propose an approach for estimating causal parameters and derive nonparametric bounds in cases where identification assumptions are violated. We illustrate the performance of the proposed method through simulation studies and a real dataset obtained from a human immunodeficiency virus study.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70126"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Closed MCP-Mod for Pairwise Comparisons of Several Doses With a Control.","authors":"Franz Koenig, Sergey Krasnozhon, Bjoern Bornkamp, Frank Bretz, Ekkehard Glimm, Alexandra Graf, Dong Xi","doi":"10.1002/sim.70124","DOIUrl":"10.1002/sim.70124","url":null,"abstract":"<p><p>The MCP-Mod approach by Bretz et al. is commonly applied for dose-response testing and estimation in clinical trials. The MCP part of MCP-Mod was originally developed to detect a dose-response signal using a multiple contrast test, but it is not appropriate to make a specific claim that the drug has a positive effect at an individual dose. In this paper, we extend the MCP-Mod approach to obtain confirmatory p-values for detecting a dose-response signal as well as for the pairwise comparisons of the individual doses against placebo. We apply the closed test principle from Marcus et al. to the optimal contrast tests based on a candidate set of plausible dose-response shapes available at the planning stage of a clinical trial. We show that the contrast coefficients have to be optimized under suitable constraints to guarantee strong Type 1 error rate control at a pre-specified significance level. Motivated by a recent clinical trial, we evaluate the operating characteristics of the proposed methods in a comprehensive simulation study.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70124"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weighted Repeated Measures Correlation Coefficient: A New Correlation Coefficient for Handling Missing Data With Repeated Measures.","authors":"Masahiro Kondo, Kengo Nagashima, Shiroh Isono, Yasunori Sato","doi":"10.1002/sim.70046","DOIUrl":"10.1002/sim.70046","url":null,"abstract":"<p><p>The relationship between two variables measured multiple times per individual has often been evaluated in clinical studies. These data are not independent; therefore, the Pearson correlation coefficient is inappropriate, and some correlation coefficients for these data have been proposed. However, in the presence of missing data, the existing methods can be biased. In this article, we proposed a weighted repeated measures correlation coefficient that provides an accurate estimate, even with missing data, in a study in which participants ideally have the same number of measurements. We also provided a bootstrap confidence interval for the weighted repeated measures correlation coefficients. We evaluated the performance of the proposed and existing methods (i.e., simple Pearson correlation coefficient, the Pearson correlation coefficient for average, average of the Pearson correlation coefficient, correlation coefficient based on analysis of covariance, and correlation coefficient based on the linear mixed-effects model) through simulations and application to actual data. In numerical evaluations using simulations, the proposed method consistently outperformed existing methods. We recommend using a weighted repeated measures correlation coefficient to handle missing values in multiple-measurement data.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70046"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12100710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144128711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Study Factor Regression Model: An Application in Nutritional Epidemiology.","authors":"Roberta De Vito, Alejandra Avalos-Pacheco","doi":"10.1002/sim.70108","DOIUrl":"10.1002/sim.70108","url":null,"abstract":"<p><p>Diet is a risk factor for many diseases. In nutritional epidemiology, studying reproducible dietary patterns is critical to reveal important associations with health. However, this task is challenging: diverse cultural and ethnic backgrounds may critically impact eating patterns by showing heterogeneity, leading to incorrect dietary patterns and obscuring the components shared across different groups or populations. Moreover, covariate effects generated from observed variables, such as demographics and other confounders, can further bias these dietary patterns. Identifying the shared and group-specific dietary components and covariate effects is essential to drive accurate conclusions. To address these issues, we introduce a new modeling factor regression, the Multistudy Factor Regression (MSFR) model. The MSFR model analyzes different populations simultaneously, achieving three goals: capturing shared component(s) across populations, identifying group-specific structures, and correcting for covariate effects. We use this novel method to derive common and ethnic-specific dietary patterns in a multicenter epidemiological study in Hispanic/Latinos community. Our model improves the accuracy of common and group dietary signals, provides a robust estimation of factor cardinality, and yields better prediction than other techniques, revealing important associations with health and cardiovascular disease. In summary, we provide a tool to integrate different groups, providing accurate dietary signals crucial to inform public health policy.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70108"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to the Commentary on \"Experimental Design and Power Calculation in Omics Circadian Rhythmicity Detection Using the Cosinor Model\".","authors":"RuoFei Yin, George C Tseng","doi":"10.1002/sim.70079","DOIUrl":"10.1002/sim.70079","url":null,"abstract":"&lt;p&gt;&lt;p&gt;We agree and highly appreciate the correction of the non-central parameter (ncp) in the power calculation by Dr. Westermark. We acknowledge the mathematical mistake in our derivation. As indicated in the commentary, under the \"evenly spaced sampling design\" (e.g., sampling every 2 h for 24 h with four biological replicates at each time point; &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mn&gt;12&lt;/mn&gt; &lt;mo&gt;×&lt;/mo&gt; &lt;mn&gt;4&lt;/mn&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;48&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ 12times 4=48 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; samples), discrepancy of the non-central parameter and the resulting statistical power between the corrected calculation and ours is 0. Below, we conduct further simulations to investigate the degree of statistical power miscalculation in \"unevenly-spaced sampling design\" in case potential users may have applied our formula in their experimental planning. We consider two types of sampling designs throughout the 24 h of a day: (i) Uniformly distributed (uniform): &lt;math&gt; &lt;semantics&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;t&lt;/mi&gt; &lt;mi&gt;i&lt;/mi&gt;&lt;/msub&gt; &lt;mover&gt;&lt;mo&gt;∼&lt;/mo&gt; &lt;mi&gt;iid&lt;/mi&gt;&lt;/mover&gt; &lt;mtext&gt;UNIF&lt;/mtext&gt; &lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt; &lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;24&lt;/mn&gt;&lt;/mrow&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {t}_ioverset{iid}{sim}mathrm{UNIF}left(0,24right) $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ; (ii) Unimodal Gaussian distributed (unimodal): &lt;math&gt; &lt;semantics&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;t&lt;/mi&gt; &lt;mi&gt;i&lt;/mi&gt;&lt;/msub&gt; &lt;mover&gt;&lt;mo&gt;∼&lt;/mo&gt; &lt;mi&gt;iid&lt;/mi&gt;&lt;/mover&gt; &lt;mi&gt;N&lt;/mi&gt; &lt;mfenced&gt;&lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;msup&gt;&lt;mi&gt;δ&lt;/mi&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;/mfenced&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {t}_ioverset{iid}{sim }Nleft(0,{delta}^2right) $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; with &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;δ&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;2&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;4&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;6&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;8&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ delta =2,4,6,8 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; . The following figure shows results for amplitude-to-noise ratio &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;r&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mi&gt;A&lt;/mi&gt; &lt;mo&gt;/&lt;/mo&gt; &lt;mi&gt;σ&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.4&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;0.6&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;0.8&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ r=A/sigma =0.4,0.6,0.8 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; and phase &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;ϕ&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;3&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;6&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;9&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ phi =0,3,6,9 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; . Note that results of &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;ϕ&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;12&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;15&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;18&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;21&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ phi =12,15,18,21 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; are identical to &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;ϕ&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;3&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;6&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;9&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ phi =0,3,6,9 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; due to symmetry. As expected, for uniformly or almost uniformly distributed designs (i.e., Uniform, Unimodal-sd6, and Unimodal-sd8), the difference between the corrected and previous power calculations is 0 or close to 0. For moderate to e","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70079"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous-Time Causal Inference With Marked Point Process Weights: An Example on Sodium-Glucose Co-Transporters 2 Inhibitor Medications and Urinary Tract Infection.","authors":"Sumeet Kalia, Olli Saarela, Tao Chen, Braden O'Neill, Christopher Meaney, Rahim Moineddin, Babak Aliarzadeh, Frank Sullivan, Michelle Greiver","doi":"10.1002/sim.70102","DOIUrl":"10.1002/sim.70102","url":null,"abstract":"<p><p>Treatment-confounder feedback is present in time-to-recurrent or longitudinal event analysis when time-dependent confounders are themselves influenced by previous treatments. Conventional models produce misleading statistical inference of causal effects due to conditioning on these factors on the causal pathway. Marginal structural models are often applied to quantify the causal treatment effect, estimated using longitudinal weights that mimic the randomization procedure by balancing the covariate distributions across the treatment groups. The weights are usually constructed in discrete time intervals, which is appropriate if the follow-up visits are scheduled and regular. However, in primary care, visit times can be irregular and informative, and the treatment history consists of duration and doses. This can be modeled through a continuous-time marked point process. We constructed a continuous-time marginal structural model to estimate the effect of cumulative exposure to Sodium-Glucose co-Transporters 2 Inhibitor (SGLT-2i) medications on time-to-recurrent urinary tract infection (UTI). We used a cohort of type II diabetes patients with chronic kidney disease and constructed a marked point process that characterized the recurrent flare episodes of primary care visits (i.e., point process) with marks for the multinominal dose (none, low, high) of SGLT-2i medications and recurrent episodes of UTI. We applied the stabilized and optimal treatment weights to estimate the hypothesized causal effect. Our results are concordant with earlier findings in which the recurrent episodes of UTI did not increase when patients were prescribed low dose or high dose of SGLT-2i medications.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70102"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inference for Cumulative Incidences and Treatment Effects in Randomized Controlled Trials With Time-to-Event Outcomes Under ICH E9 (R1).","authors":"Yuhao Deng, Shasha Han, Xiao-Hua Zhou","doi":"10.1002/sim.70091","DOIUrl":"https://doi.org/10.1002/sim.70091","url":null,"abstract":"<p><p>In randomized controlled trials (RCTs) that focus on time-to-event outcomes, intercurrent events can arise in two ways: as semi-competing events, which modify the hazard of the primary outcome events, or as competing events, which make the definition of the primary outcome events unclear. Although five strategies have been proposed in the ICH E9 (R1) addendum to address intercurrent events in RCTs, these strategies are not easily applicable to time-to-event outcomes when aiming for causal interpretations. In this study, we show how to define, estimate, and make inferences concerning objectives that have causal interpretations within these contexts. Specifically, we derive the mathematical formulations of the causal estimands corresponding to the five strategies and clarify the data structure needed to identify these causal estimands. Furthermore, we introduce nonparametric methods for estimating and making inferences about these causal estimands, including the asymptotic variance of estimators and the construction of hypothesis tests. Finally, we illustrate our methods using data from the LEADER Trial, which aims to investigate the effect of liraglutide on cardiovascular outcomes.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70091"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events.","authors":"Thomas R Fleming, Kevin J Carroll, Janet T Wittes, Scott S Emerson, Mark D Rothmann, Sylva Collins, Gregory Levin","doi":"10.1002/sim.70104","DOIUrl":"10.1002/sim.70104","url":null,"abstract":"<p><p>In randomized clinical trials, stopping study medication, use of rescue treatment, and other intercurrent events can complicate interpretation of results. The ICH E9(R1) Addendum on estimands stimulated important cross-disciplinary discussions on trial objectives. Unfortunately, with influence of the Addendum, many trials have proposed analyzing primary endpoints using \"while on treatment\", \"hypothetical\", or \"principal stratum\" strategies that handle intercurrent events in ways that use post-randomization outcomes to exclude information from randomized participants and don't preserve integrity of randomization, or that don't reliably capture the intervention's meaningful net effects. These approaches have inherent limitations in ability to draw scientifically rigorous inference on clinically relevant causal effects important for decisions about adopting interventions. We describe advantages of trials with standard-of-care control arms targeting estimands using \"treatment policy\" approaches for intercurrent events, while potentially incorporating other meaningful intercurrent events, such as death, into the primary endpoint applying a composite strategy. Well-designed and -conducted trials targeting such estimands achieve scientifically rigorous causal inference through analyzes that protect the integrity of randomization. Such estimands also provide meaningful information relevant to real-world settings because they (1) are unconditional in nature i.e., they don't condition on post-treatment circumstances that might not be many participants' experiences; and (2) properly evaluate the experimental intervention within a regimen that includes possible ancillary care that would be clinically appropriate in real-world settings. We hope to add clarity about appropriate strategies for intercurrent events and how to improve design, conduct, and analysis of clinical trials to address questions of greatest clinical importance reliably.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70104"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}