Clinical Trials最新文献

{"title":"Estimands in clinical trials of complex disease processes.","authors":"Richard J Cook, Jerald F Lawless","doi":"10.1177/17407745241268054","DOIUrl":"10.1177/17407745241268054","url":null,"abstract":"<p><p>Clinical trials with random assignment of treatment provide evidence about causal effects of an experimental treatment compared to standard care. However, when disease processes involve multiple types of possibly semi-competing events, specification of target estimands and causal inferences can be challenging. Intercurrent events such as study withdrawal, the introduction of rescue medication, and death further complicate matters. There has been much discussion about these issues in recent years, but guidance remains ambiguous. Some recommended approaches are formulated in terms of hypothetical settings that have little bearing in the real world. We discuss issues in formulating estimands, beginning with intercurrent events in the context of a linear model and then move on to more complex disease history processes amenable to multistate modeling. We elucidate the meaning of estimands implicit in some recommended approaches for dealing with intercurrent events and highlight the disconnect between estimands formulated in terms of potential outcomes and the real world.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"604-611"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528884/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142046433","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}

{"title":"Inferences for the distribution of the duration of response in a comparative clinical study.","authors":"Ying Cui, Bo Huang, Lu Mao, Hajime Uno, Lee-Jen Wei, Lu Tian","doi":"10.1177/17407745241264188","DOIUrl":"10.1177/17407745241264188","url":null,"abstract":"<p><p>Duration of response is an important endpoint used in drug development. Prolonged duration for response is often viewed as an early indication of treatment efficacy. However, there are numerous difficulties in studying the distribution of duration of response based on observed data subject to right censoring in practice. The most important obstacle is that the distribution of the duration of response is in general not identifiable in the presence of censoring due to the simple fact that there is no information on the joint distribution of time to response and time to progression beyond the largest follow-up time. In this article, we introduce the restricted duration of response as a replacement of the conventional duration of response. The distribution of restricted duration of response is estimable and we have proposed several nonparametric estimators in this article. The corresponding inference procedure and additional downstream analysis have been developed. Extensive numerical simulations have been conducted to examine the finite sample performance of the proposed estimators. It appears that a new regression-based two-step estimator for the survival function of the restricted duration of response tends to have a robust and superior performance, and we recommend its use in practice. A real data example from oncology has been used to illustrate the analysis for restricted duration of response.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"541-552"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141901174","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}

{"title":"Multiply robust estimation of principal causal effects with noncompliance and survival outcomes.","authors":"Chao Cheng, Yueqi Guo, Bo Liu, Lisa Wruck, Fan Li, Fan Li","doi":"10.1177/17407745241251773","DOIUrl":"10.1177/17407745241251773","url":null,"abstract":"<p><p>Treatment noncompliance and censoring are two common complications in clinical trials. Motivated by the ADAPTABLE pragmatic clinical trial, we develop methods for assessing treatment effects in the presence of treatment noncompliance with a right-censored survival outcome. We classify the participants into principal strata, defined by their joint potential compliance status under treatment and control. We propose a multiply robust estimator for the causal effects on the survival probability scale within each principal stratum. This estimator is consistent even if one, sometimes two, of the four working models-on the treatment assignment, the principal strata, censoring, and the outcome-is misspecified. A sensitivity analysis strategy is developed to address violations of key identification assumptions, the principal ignorability and monotonicity. We apply the proposed approach to the ADAPTABLE trial to study the causal effect of taking low- versus high-dosage aspirin on all-cause mortality and hospitalization from cardiovascular diseases.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"553-561"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141174971","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}

{"title":"Defining estimand for the win ratio: Separate the true effect from censoring.","authors":"Lu Mao","doi":"10.1177/17407745241259356","DOIUrl":"10.1177/17407745241259356","url":null,"abstract":"<p><p>The win ratio has been increasingly used in trials with hierarchical composite endpoints. While the outcomes involved and the rule for their comparisons vary with the application, there is invariably little attention to the estimand of the resulting statistic, causing difficulties in interpretation and cross-trial comparison. We make the case for articulating the estimand as a first step to win ratio analysis and establish that the root cause for its elusiveness is its intrinsic dependency on the time frame of comparison, which, if left unspecified, is set haphazardly by trial-specific censoring. From the statistical literature, we summarize two general approaches to overcome this uncertainty-a nonparametric one that pre-specifies the time frame for all comparisons, and a semiparametric one that posits a constant win ratio across all times-each with publicly available software and real examples. Finally, we discuss unsolved challenges, such as estimand construction and inference in the presence of intercurrent events.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"584-594"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11502278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141792130","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}

{"title":"15th Annual University of Pennsylvania conference on statistical issues in clinical trial/advances in time to event analyses in clinical trials (morning panel discussion).","authors":"Pralay Mukhopadhyay, Douglas Schaubel, Mei-Cheng Wang","doi":"10.1177/17407745241272012","DOIUrl":"10.1177/17407745241272012","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"562-570"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142105126","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}

{"title":"Proceedings of the University of Pennsylvania 15th annual conference on statistical issues in clinical trials: Advances in time-to-event analyses in clinical trials-challenges and opportunities.","authors":"Mary E Putt","doi":"10.1177/17407745241276119","DOIUrl":"https://doi.org/10.1177/17407745241276119","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"18 1","pages":"17407745241276119"},"PeriodicalIF":2.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248397","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}

{"title":"Participant’s treatment guesses and adverse events in back pain trials: Nocebo in action?","authors":"Javier Muñoz Laguna, Hyangsook Lee, Eduard Poltavskiy, Jeehyoung Kim, Heejung Bang","doi":"10.1177/17407745241276124","DOIUrl":"https://doi.org/10.1177/17407745241276124","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"65 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248398","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}

{"title":"Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials.","authors":"Guangyu Tong, Jiaqi Tong, Yi Jiang, Denise Esserman, Michael O Harhay, Joshua L Warren","doi":"10.1177/17407745231222018","DOIUrl":"10.1177/17407745231222018","url":null,"abstract":"<p><strong>Background: </strong>Heterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster randomized trials with continuous outcomes.</p><p><strong>Methods: </strong>This article proposes models fitted in the Bayesian setting with hierarchical variance structure to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings.</p><p><strong>Results: </strong>Simulations showed that overall the newly introduced models have good performance, reporting low bias and approximately 95% coverage for the intraclass correlation coefficients and regression parameters in the variance model. When variances are heterogeneous, our proposed models had improved model fit over models with homogeneous variances. When used to analyze data from the Kerala Diabetes Prevention Program study, our models identified heterogeneous variances and intraclass correlation coefficients across clusters and examined cluster-level characteristics associated with such heterogeneity.</p><p><strong>Conclusion: </strong>We proposed new hierarchical Bayesian variance models to accommodate cluster-specific variances in cluster randomized trials. The newly developed methods inform the understanding of how an intervention strategy is implemented and disseminated differently across clusters and can help improve future trial design.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"451-460"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11233424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139402217","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}

{"title":"Public involvement in Australian clinical trials: A systematic review.","authors":"Tessa-May Zirnsak, Ashley H Ng, Catherine Brasier, Richard Gray","doi":"10.1177/17407745231224533","DOIUrl":"10.1177/17407745231224533","url":null,"abstract":"<p><strong>Background: </strong>Public involvement enhances the relevance, quality, and impact of research. There is some evidence that public involvement in Australian research lags other countries, such as the United Kingdom. The purpose of the systematic review was to establish the rates and describe the characteristics of public involvement in Australian clinical trials.</p><p><strong>Methods: </strong>We reviewed evidence of public involvement in all Australian randomised controlled trials published in the first 6 months of 2021. To determine the quality of public involvement, we used the five-item short-form version of the Guidance of Reporting Involvement Patients and the Public, version 2.</p><p><strong>Results: </strong>In total, 325 randomised controlled trials were included, of which 17 (5%) reported any public involvement. Six trials reported public involvement in setting the research aim and seven in developing study methods. The authors of one study reflected on the overall role and influence of public involvement in the research.</p><p><strong>Conclusion: </strong>Rate of public involvement in Australian clinical trials is seemingly substantially lower than those reported in countries with similar advanced public health care systems, notably the United Kingdom. Our observations may be explained by a lack of researcher skills in how to involve the public and the failure by major funding agencies in Australia to mandate public involvement when deciding on how to award grant funding.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"507-515"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139971180","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}

{"title":"Assessing the current utilization status of wearable devices in clinical research.","authors":"Takashi Miyakoshi, Yoichi M Ito","doi":"10.1177/17407745241230287","DOIUrl":"10.1177/17407745241230287","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background/aims: &lt;/strong&gt;Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov-a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords \"ActiGraph,\"\"Apple Watch,\"\"Empatica,\"\"Fitbit,\"\"Garmin,\" and \"wearable devices\" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the fu","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"470-482"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140130893","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}