Journal of the National Cancer Institute. Monographs最新文献

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.

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.

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.

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.

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.

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.

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.

One-dose prophylactic HPV vaccination of pre-adolescents may reduce cervical cancer deaths dramatically in lower-resource settings, but the benefits of achieving immediate high coverage among pre-adolescents would not be realized for 20 to 40 years. Prophylactic vaccine efficacy is reduced after sexual debut, and current therapeutic intervention candidates designed to treat existing HPV infections or precancerous lesions have yielded insufficient evidence to warrant widespread use. However, we are developing a feasible, scalable, high-quality cervical screening approach that could prevent hundreds of thousands of deaths, while we work to achieve high coverage of one-dose vaccination for adolescent cohorts. A time-limited "one screen" campaign approach for lower-resource settings could complement parallel efforts to achieve high coverage with one-dose vaccination. This screen-triage-treat strategy would target the highest risk groups of screening age (ie, 25 to 49 years) for once-in-a-lifetime HPV testing of self-collected samples using a low-cost accurate HPV test; subsequent triage relying on extended genotyping and a validated deep-learning algorithm for automated visual evaluation (AVE) would stratify management based on risk to provide treatment for those most likely to develop cancer without overburdening health care systems. Early efficacy of this approach has been demonstrated in 9 countries within the HPV-AVE (PAVE) Study Consortium. We estimate that the cost per death averted of a screen-triage-treat campaign is of similar magnitude to prophylactic vaccination. We do not envision perpetual investment in ubiquitous brick-and-mortar screening programs if "one dose, one screen" is implemented with high coverage and targets the highest-risk populations. In collaboration with in-country stakeholders, efforts to ensure acceptability, risk communication, and cost-effectiveness are underway.

Background: The World Health Organization recommends a single-dose human papillomavirus (HPV) vaccination schedule for girls and boys to accelerate progress toward cervical cancer elimination. We applied the Theoretical Framework of Acceptability (TFA) within the context of HPV vaccination to assess the acceptability of a single-dose schedule among health-care professionals in Kenya.

Methods: A REDCap survey was developed using relevant Theoretical Framework of Acceptability domains and validated with health-care professionals. Descriptive analyses and multivariate Poisson regression were conducted to assess factors associated with increased acceptability. Free-text responses were analyzed using a rapid qualitative approach, and findings were presented using a joint display.

Results: Among 385 responses, 74.2% of health-care professionals were female and 48.6% were nurses. On average, respondents had been in their position for 60 months, and one-third (33.2%) were based at level-4 facilities. The majority (75.84%) thought that giving a single-dose of the HPV vaccine to adolescent girls and young women was either acceptable or very acceptable. Qualitative findings highlighted that lack of information was the underlying reason for health-care professionals who were resistant, and most clinicians thought that a singled-dose schedule was less burdensome to clinicians and patients. Hospital directors had a non-statistically significantly lower acceptability likelihood than nurses (incident rate ratio = 0.93, 95% confidence interval = 0.45 to 1.71) and health-care professionals at urban facilities had a non-statistically significantly lower acceptability likelihood than clinicians in rural facilities (incident rate ratio = 0.97, 95% confidence interval = 0.83 to 1.13).

Conclusion: Although not statistically significant, predictors of increased acceptability provide information to tailor strategies to increase HPV vaccination coverage and accelerate progress toward cervical cancer elimination.