Michelle M Nuño, Stephanie L Pugh, Lingyun Ji, Jin Piao, James J Dignam, Jon A Steingrimsson
{"title":"On the use of external controls in clinical trials.","authors":"Michelle M Nuño, Stephanie L Pugh, Lingyun Ji, Jin Piao, James J Dignam, Jon A Steingrimsson","doi":"10.1093/jncimonographs/lgae046","DOIUrl":"10.1093/jncimonographs/lgae046","url":null,"abstract":"<p><p>Externally controlled trials have commonly been used when conducting a randomized controlled trial (RCT) is not feasible or ethical. By allowing the study of new treatments, use of external controls can lead to accelerated advances in the management of rare diseases. The use of external controls, however, introduces new challenges due to potential differences between the population the external controls are enrolled from and the population the patients on the new trial are enrolled from. Some differences include, but are not limited to, differences in how patients are diagnosed and treated, differences in the case mix of the underlying populations, differences in the ability to measure outcomes, and differences in data collection. We discuss the potential benefits and challenges of externally controlled trials, as well as strategies to mitigate bias, including the estimand and target-trial emulation framework. We also provide a brief overview of statistical methodology commonly used in these settings. We note that although the strategies presented may help mitigate some of these challenges, they cannot replace an RCT framework, and investigators should be aware of the potential limitations of externally controlled trials.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":"2025 68","pages":"30-34"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484947","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}
Ping Hu, Jon A Steingrimsson, Elodia Cole, Jean Cormack, Barbara K Dunn, Constantine Gatsonis, Cecilia Lee, Ni Li, Etta D Pisano, Jie He, Barnett S Kramer
{"title":"Design considerations and challenges in the CHinA National CancEr Screening (CHANCES) trial and Tomosynthesis Mammographic Imaging Screening Trial (TMIST).","authors":"Ping Hu, Jon A Steingrimsson, Elodia Cole, Jean Cormack, Barbara K Dunn, Constantine Gatsonis, Cecilia Lee, Ni Li, Etta D Pisano, Jie He, Barnett S Kramer","doi":"10.1093/jncimonographs/lgae049","DOIUrl":"10.1093/jncimonographs/lgae049","url":null,"abstract":"<p><p>This paper explores the design considerations and hurdles encountered by the CHinA National CancEr Screening (CHANCES) Trial and the Tomosynthesis Mammographic Imaging Screening Trial (TMIST), both aimed at advancing cancer screening research. Before population-based cancer screening programs are launched, it is important to have confidence that the potential benefits of the screening process and resulting interventions outweigh harms, an ethical imperative because the people actively invited into the programs are relatively healthy. Large randomized screening trials provide the strongest, direct evidence regarding the balance of benefits and harms. The implementation of cancer screening programs involves a series of steps, with outcomes influenced by factors such as the prevalence of the disease, availability of effective treatment within the health-care system, and acceptance by the target population-all of which may vary considerably from country to country. This paper examines how these factors shaped the design and statistical approach of the CHANCES Trial for lung and colorectal cancers and the TMIST trial for breast cancer. We discuss the rationale, objectives, endpoint definitions, trial designs, and sample size considerations, highlighting both the challenges and opportunities presented in different settings. Ultimately, the goal is to foster collaboration and develop screening strategies that are scientifically robust and practically effective for diverse populations worldwide.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":"2025 68","pages":"42-48"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484895","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}
Danielle M Enserro, Heather J Gunn, Mohamed I Elsaid, Fenghai Duan, Stephanie L Pugh
{"title":"Challenges to and considerations of designing cancer prevention trials.","authors":"Danielle M Enserro, Heather J Gunn, Mohamed I Elsaid, Fenghai Duan, Stephanie L Pugh","doi":"10.1093/jncimonographs/lgae044","DOIUrl":"10.1093/jncimonographs/lgae044","url":null,"abstract":"<p><p>Prevention trials in oncology are some of the most important cancer clinical trials that can be designed, implemented, analyzed, and interpreted. They are pivotal in the goal of stopping the development of cancer before it starts. Prevention trials are unique in that they not only have some of the same requirements and challenges as treatment trials but also have challenges that may make their design more complex. This paper aims to discuss some types of prevention trials and highlight their most common design challenges, including large sample size requirements, slow accrual rates with long accrual duration, extended follow-up periods with trial adherence issues and missing data, endpoints that require extended follow-up or have a high level of confounding, and problems with optimizing study design. This article provides real study examples and suggestions for designing prevention clinical trials while mitigating the known issues they face.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":"2025 68","pages":"49-55"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484885","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}
Gina L Mazza, Eva Culakova, Danielle M Enserro, James J Dignam, Joseph M Unger
{"title":"Design and analysis considerations for investigating patient subgroups of interest within cancer clinical trials.","authors":"Gina L Mazza, Eva Culakova, Danielle M Enserro, James J Dignam, Joseph M Unger","doi":"10.1093/jncimonographs/lgae045","DOIUrl":"10.1093/jncimonographs/lgae045","url":null,"abstract":"<p><p>Examining treatment effects in subgroups of patients defined by demographic, genetic, or clinical characteristics is increasingly of interest given the pursuit of personalized medicine and the importance of representation and equity in treatment decisions. The magnitude or even the direction of the treatment effect may vary across subgroups, and these differential treatment effects could have clinical implications. Subgroup analyses require caution in their interpretation, however, because of the high probability of a false-positive or false-negative conclusion. We outline study design and analysis considerations for responsibly investigating and reporting differential treatment effects across subgroups in oncology trials, with examples from the National Cancer Institute's National Clinical Trials Network and Community Oncology Research Program. Recommendations include ensuring appropriate representation of patients from subgroups of interest, recognizing power and multiplicity limitations, and treating exploratory subgroup analyses as hypothesis generating rather than practice changing.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":"2025 68","pages":"22-29"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484887","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}
Grant Izmirlian, Lev A Sirota, Vance W Berger, Victor Kipnis
{"title":"The fundamentals of multiplicity adjustment in biostatistics.","authors":"Grant Izmirlian, Lev A Sirota, Vance W Berger, Victor Kipnis","doi":"10.1093/jncimonographs/lgae050","DOIUrl":"10.1093/jncimonographs/lgae050","url":null,"abstract":"<p><p>The statistical problem of multiplicity is concerned with making protected multiple inferences and their valid interpretation in a particular study. Most discussions of multiplicity focus on the increase of type I error rate if testing is done without any adjustment, with only a few papers discussing its ramifications for type II errors/power. We provide a survey of main approaches to protected inference in biomedical studies, touching on procedures to control the family-wise error rate, false discovery rate, as well as false discovery exceedance probability. We discuss several notions of power including total power, average power, and power defined as exceedance probability for the true positive proportion. We provide commentary on best practices for adjusting for multiplicity in both type I and type II errors within families defined by primary, secondary, and exploratory endpoints in clinical trials and in experimental studies.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":"2025 68","pages":"10-13"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484955","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}
Joseph M Unger, Gina L Mazza, Mohamed I Elsaid, Fenhai Duan, Emily V Dressler, Anna C Snavely, Danielle M Enserro, Stephanie L Pugh
{"title":"When to adjust for multiplicity in cancer clinical trials.","authors":"Joseph M Unger, Gina L Mazza, Mohamed I Elsaid, Fenhai Duan, Emily V Dressler, Anna C Snavely, Danielle M Enserro, Stephanie L Pugh","doi":"10.1093/jncimonographs/lgae051","DOIUrl":"10.1093/jncimonographs/lgae051","url":null,"abstract":"<p><p>Interpreting cancer clinical trial results often depends on addressing issues of multiplicity. When testing multiple hypotheses, unreliable findings can occur by chance due to the inflation of the type I error rate, the probability of mistakenly rejecting the null hypothesis when the null hypothesis is true. In this setting, researchers may often set the type I error rate (or the alpha level) low to limit false positive findings and the interpretation of a causal relationship where none exists. Conversely, overly conservative type I error control may result in declaring findings, that do not meet multiplicity-adjusted alpha levels, as false when they are actually true, reducing opportunities for new discovery. This presentation focuses on multiplicity adjustment in the context of clinical trials conducted within the NCI's Community Oncology Research Program (NCORP). Because federally sponsored trials often require long-term participation from patients and represent a substantial investment by taxpayers, striking the right balance between optimizing what is learned from these trials, while avoiding false positive results, should be a priority.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":"2025 68","pages":"3-9"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484964","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}
Anna C Snavely, Heather J Gunn, Ju-Whei Lee, Stephanie L Pugh, William E Barlow, Eva Culakova, Kathryn B Arnold, Carol A Kittel, Sydney Smith, Bret M Hanlon, Angelina D Tan, Travis Dockter, David Zahrieh, Emily V Dressler
{"title":"Intracluster correlation coefficients from cluster randomized trials conducted within the NCI Community Oncology Research Program (NCORP).","authors":"Anna C Snavely, Heather J Gunn, Ju-Whei Lee, Stephanie L Pugh, William E Barlow, Eva Culakova, Kathryn B Arnold, Carol A Kittel, Sydney Smith, Bret M Hanlon, Angelina D Tan, Travis Dockter, David Zahrieh, Emily V Dressler","doi":"10.1093/jncimonographs/lgae048","DOIUrl":"10.1093/jncimonographs/lgae048","url":null,"abstract":"<p><p>The intracluster correlation coefficient (ICC) measures the correlation of observations within clusters and is a key parameter for power and sample size calculations for cluster randomized trials (CRTs). To facilitate the design of future CRTs within the National Cancer Institute Community Oncology Research Program (NCORP), all studies from the NCORP website were reviewed to identify completed CRTs. ICCs for primary and secondary outcomes (when available) were ascertained from these trials and summarized in this article as a resource for future trial development. Although ICCs are relatively small for many outcome cluster combinations, that is not always the case, so consideration should always be given to the specific outcome of interest, trial design, and type of cluster when estimating an ICC to facilitate trial development.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":"2025 68","pages":"65-72"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484943","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}
Emily V Dressler, Stephanie L Pugh, Heather J Gunn, Joseph M Unger, David M Zahrieh, Anna C Snavely
{"title":"Practical design considerations for cluster randomized controlled trials: lessons learned in community oncology research.","authors":"Emily V Dressler, Stephanie L Pugh, Heather J Gunn, Joseph M Unger, David M Zahrieh, Anna C Snavely","doi":"10.1093/jncimonographs/lgae053","DOIUrl":"10.1093/jncimonographs/lgae053","url":null,"abstract":"<p><p>Cancer care delivery research trials conducted within the National Cancer Institute (NCI) Community Oncology Research Program (NCORP) routinely implement interventions at the practice or provider level, necessitating the use of cluster randomized controlled trials (cRCTs). The intervention delivery requires cluster-level randomization instead of participant-level, affecting sample size calculation and statistical analyses to incorporate correlation between participants within a practice. Practical challenges exist in the conduct of these cRCTs due to unique trial network infrastructures, including the possibility of unequal participant accrual totals and rates and staggered study initiation by clusters, potentially with differences between randomized arms. Execution of cRCT designs can be complex, ie, if some clusters do not accrue participants, unintended cluster-level crossover occurs, how best to identify appropriate cluster-level stratification, timing of randomization, and multilevel eligibility criteria considerations. This article shares lessons learned with potential mitigation strategies from 3 NCORP cRCTs.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":"2025 68","pages":"56-64"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484952","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}
Cecilia Monge, Linsey Eldridge, Paul C Pearlman, Viji Venkatesh, Michelle Tregear, Patrick J Loehrer, Annette Galassi, Satish Gopal, Ophira Ginsburg
{"title":"Global perspectives on patient-centered outcomes: advancing patient-centered cancer clinical trials globally.","authors":"Cecilia Monge, Linsey Eldridge, Paul C Pearlman, Viji Venkatesh, Michelle Tregear, Patrick J Loehrer, Annette Galassi, Satish Gopal, Ophira Ginsburg","doi":"10.1093/jncimonographs/lgae043","DOIUrl":"10.1093/jncimonographs/lgae043","url":null,"abstract":"<p><p>Patient-centered clinical trials prioritize the patient experience and outcomes that matter most to those affected by cancer. By centering on patient values and experiences, patient-centered outcomes research generates evidence to inform policies and practices, facilitating more personalized and effective cancer care. This manuscript explores the importance of patient-centered approaches in the global context, emphasizing challenges and opportunities for substantive patient engagement and the integration of patient-reported measures in clinical therapeutic trials in low- and middle-income countries. Despite important barriers such as limited infrastructure and funding constraints, leveraging innovative strategies and investing in research infrastructure and regulatory harmonization initiatives can enhance the capacity of low- and middle-income countries to conduct high-quality research and address the global burden of cancer more effectively. Through these efforts, patient-centered care and research can be extended to underserved populations, ensuring equitable access to cancer care worldwide.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":"2025 68","pages":"35-41"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484941","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}
Hanna Bandos, Pedro A Torres-Saavedra, Eva Culakova, Heather J Gunn, Minji K Lee, Fenghai Duan, Reena S Cecchini, Joseph M Unger, Amylou C Dueck, Jon A Steingrimsson
{"title":"Best practices and pragmatic approaches for patient-reported outcomes and quality of life measures in cancer clinical trials.","authors":"Hanna Bandos, Pedro A Torres-Saavedra, Eva Culakova, Heather J Gunn, Minji K Lee, Fenghai Duan, Reena S Cecchini, Joseph M Unger, Amylou C Dueck, Jon A Steingrimsson","doi":"10.1093/jncimonographs/lgae047","DOIUrl":"10.1093/jncimonographs/lgae047","url":null,"abstract":"<p><p>Patient-reported outcomes (PROs) are often collected in cancer clinical trials. Data obtained from trials with PROs are essential in evaluating participant experiences relating to symptoms, financial toxicity, or health-related quality of life. Although most features of clinical trial design, implementation, and analyses apply to trials with PROs, several considerations are unique. In this paper, we focus on specific issues such as selection of the tool, timing and frequency of assessments, and data collection methods. We discuss how the estimand framework can be used in connection with PROs, properties of common estimation methods, and handling of missing outcomes. With a plethora of literature available, we aim to summarize best practices and pragmatic approaches to the design and analysis of the studies incorporating PROs.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":"2025 68","pages":"14-21"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484851","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}