Advancing Education, Research, and Quality of Care for the Head and Neck oncology patient.
Introduction: Clinical trials are pivotal to advance Head and Neck Cancer (HNC) treatment, offering a platform to evaluate novel therapies and enhance patient outcomes. Despite females comprising 26.5% of HNC cases, they have been historically underrepresented in clinical trials. This study investigates whether exclusion language regardnig contraception requirements is associated with disproportionately low female enrollment.
Methods: A comprehensive review of NIH-sponsored HNC clinical trials published on ClinicalTrials.gov from 2002 to 2019 was conducted. 155 trials were identified and cross-referenced with their published results. All trials were assessed for exclusion/inclusion criteria related to contraception requirments, male/female enrollment, and available co-variates. 98 trials with complete data were included in this analysis. A 4-point ordinal ranking system was developed to categorize the equality of contraception requirement language in each trial. A multi-variable logistic regression was performed to assess the impact of contraception specific exclusion language on female enrollment. The dependent variable was defined as over or under representation of female participants, and the independent variables included contraception language rank, study enrollment size, study design (single vs. multi-site), and study initiation date.
Results: Studies with less biased contraception exclusion language and/or requirements were significantly more likely to enroll a representative proportion of female patients (aOR=3.281; p=0.039). This was the only variable significantly associated with female enrollment.
Conclusion: As the Head and Neck Cancer community strives to ensure equitable enrollment in trials so treatment efficacy is tested adequately across patient demographics, this study has identified a simple, but crucial way to enhance female representation. Our findings indicate that developing language that makes contraception requirements seems relatively similar for both males and females results in optimal gender enrollmet.