Dressler, Emily V

Abstract 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.

Abstract 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.

PURPOSE Routine collection of sexual orientation (SO) and gender identity (GI; collectively SOGI) in cancer clinics advances cancer care equity. METHODS In 2022, NCI Community Oncology Research Program (NCORP) practice groups were asked about routine collection of SOGI data in the electronic health record. The proportions of practice groups reporting collection of SO and/or GI data were calculated, and practice group characteristics were assessed for associations. RESULTS Of 271 practice groups nationwide, 42% (n = 112) collect SO data, 58% (n = 157) collect GI data, and 35% (n = 96) collect both. In multivariate analyses, SO data collection was associated with practice groups having minority outreach staff (odds ratio [OR], 2.07 [95% CI, 1.12 to 3.81]; P = .02); GI data collection was associated with practice groups located in the Northeastern United States (OR, 2.08 [95% CI, 0.73 to 5.91]; P = .045), and those with a higher proportion of new patients who were White (OR, 1.02 [95% CI, 1.01 to 1.04]; P < .001). Practice groups in the South were least likely to collect SOGI data (OR, 0.49 [95% CI, 0.26 to 0.94]; P = .004). There were no statistically significant differences in SO and/or GI collection on the basis of the practice group's proportion of Medicaid/Medicare patients, number of new patients with cancer per year, or practice ownership. CONCLUSION Slightly over one third of NCORP practice groups report routinely collecting SOGI data. There are regional differences in data collection, underscoring the need to craft targeted, region-specific interventions focused on boosting the capture and recording of SOGI data in an affirming manner.

PURPOSE Oncology advanced practice providers (APPs), including nurse practitioners, clinical nurse specialists, physician assistants, and clinical pharmacists, contribute significantly to quality cancer care. Understanding the research-related roles of APPs in the National Cancer Institute's (NCI) Community Oncology Research Program (NCORP) could lead to enhanced protocol development, trial conduct, and accrual. METHODS The 2022 NCORP Landscape Assessment Survey asked two questions about the utilization and roles of APPs in the NCORP. RESULTS A total of 271 practice groups completed the 2022 survey, with a response rate of 90%. Of the 259 nonpediatric exclusive practice groups analyzed in this study, 92% used APPs for clinical care activities and 73% used APPs for research activities. APPs most often provided clinical care for patients enrolled in trials (97%), followed by assistance with coordination (65%), presenting/explaining clinical trials (59%), screening patients (49%), ordering investigational drugs (37%), and consenting participants (24%). Some groups reported APPs as an enrolling investigator (18%) and/or participating in institutional oversight/selection of trials (15%). Only 5% of NCORP sites reported APPs as a site primary investigator for trials, and very few (3%) reported APPs participating in protocol development. CONCLUSION Practice groups report involving APPs in clinical research within the NCORP network; however, opportunities for growth exists. As team-based care has enhanced clinical practice in oncology, this same approach can be used to enhance successful research. Suggested strategies include supporting APP research-related time, recognition, and education. The findings of this survey and subsequent recommendations may be applied to all adult oncology practices that participate in clinical research.