Evaluation of an Online Prostate Cancer Screening Decision Aid

Goal of the project: To conduct a randomized controlled trial (RCT) to determine if CDC's online, simulated, human decision aid module, Talk to Nathan About Prostate Cancer Screening (treatment arm), is effective in improving knowledge, overcoming health literacy barriers, and resolving decisional conflict compared to a standard decision aid (control arm 1) and standard education materials (control arm 2). Also, to identify barriers and best practices for incorporating Talk to Nathan About Prostate Cancer Screening into the flow of primary care practice. Intended use of the resulting data: To measure evaluation outcomes, understand how to help men make decisions about the harms and benefits of prostate cancer screening that are in line with the patient's individual values and preferences, and make recommendations for improving the Talk to Nathan About Prostate Cancer Screening decision aid and incorporating it into primary care practice. Methods to be used to collect: The RCT is a three-group parallel design with one treatment arm and two control arms. Data will be collected from all arms using a pre-exposure survey, a post-exposure survey, and a post-clinic visit survey. The treatment arm will also complete a usability survey and a subset of the treatment arm will be invited to participate in user experience interviews. Health care providers at the four participating clinics will complete a short survey prior to executing the three-arm study and interviews will be conducted at the close of the study with study coordinators from the four participating clinics. The subpopulation to be studied: For the pre- and post-surveys, the usability survey, and the user experience interviews, the subpopulation is men aged 55-69 years. For the provider survey, the subpopulation is primary care providers who practice within the four clinics participating in the study. For the clinic coordinator interviews, the subpopulation is the study coordinators from the four participating clinics. How data will be analyzed: For quantitative survey data: intention-to-treat analysis; repeated measures analysis of variance across assessment time points; ordinary least squares regression; complier-average causal effect (CACE) approach to calculate treatment effect; maximum likelihood and Bayesian inferential methods for CACE. For qualitative data from the surveys and interviews: We will identify and analyze themes, patterns, and inter-relationships relevant to the evaluation questions for this study.