Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer.

Abstract

Adherence to adjuvant endocrine therapy (AET) is low in women with breast cancer, which increases the risk of recurrence and mortality. A consistently reported barrier to adherence is low perceived necessity of AET and high concerns. Existing interventions to support medication beliefs have mixed effectiveness and rarely target medication beliefs specifically. We developed an information leaflet with five candidate components aiming to increase necessity beliefs about AET and reduce concerns; (1) diagrams explaining how AET works; (2) icon arrays displaying the benefits of AET; (3) information about the prevalence of side-effects; (4) answers to common concerns and (5) quotes and pictures from breast cancer survivors. Guided by the multiphase optimisation strategy (MOST), we aimed to optimise the content of the information leaflet. We planned for the dataset to be open access to provide an exemplar for other investigators to use. The content of the leaflet was optimised in a fully powered online 2 5 factorial experiment. Each candidate component of the leaflet was operationalised as a factor with two levels; on vs off or enhanced vs basic. Healthy women (n=1604) completed the beliefs about medicines questionnaire and were randomised to view one of 32 versions of the information leaflet. The 32 versions comprised unique combinations of the factor levels corresponding to the five candidate intervention components. Time spent on the information leaflet page of the survey was recorded. After viewing the information leaflet, participants completed the beliefs about medicines questionnaire again, a true/false questionnaire assessing their objective knowledge of AET, a subjective rating of their knowledge of AET, and a questionnaire evaluating their satisfaction with the information they received. The factorial dataset provides the opportunity for other investigators interested in using the MOST framework to learn about complex factorial designs, using a real dataset.