Specialty bias in treatment recommendations and quality of life among radiation oncologists and urologists for localized prostate cancer.

BACKGROUND: Given the importance of physician attitudes about different treatments and the quality of life (QOL) in prostate cancer, we performed a national survey of specialists to assess treatment recommendations and perceptions of treatment-related survival and QOL. METHODS: We mailed a self-administered survey instrument to a random sample of 1366 specialists in the U.S. Respondents were asked for treatment recommendations and survival that varied by PSA levels and Gleason scores and estimate QOL outcomes. Pearson's chi-square and multivariable regression models were used to test for differences in each outcome. RESULTS: Response rates were similar for radiation oncologists (52.6%) and urologists (52.3%; P=0.92). Across all risk strata, urologists were more likely to recommend surgery than were radiation oncologists, for conditions ranging from PSA>20 and Gleason score 8-10 (35.2 vs. 0.2%; P<0.001) to PSA 4-10 and Gleason score 7 (87.5 vs. 20.9%; P<0.001). Radiation oncologists were also more likely to recommend radiation therapy relative to urologists (all P<0.001). From low- to high-risk prostate cancer, radiation oncologists and urologists perceived their treatment as being better for improving survival (all P<0.001). Each specialty also viewed their treatment as having less urinary incontinence (all P<0.001). CONCLUSIONS: Radiation oncologists and urologists both prefer the treatment modalities they offer, perceive them to be more effective and to lead to a better QOL. Patients may be receiving biased information, and a truly informed consent process with shared decision-making may be possible only if they are evaluated by both specialties before deciding upon a treatment course.

Potential bias in the bank: what distinguishes refusers, nonresponders and participants in a clinic-based biobank?

BACKGROUND: Biobanks are an important resource for genetic and epidemiologic research, but bias may be introduced if those who accept the recruitment invitation differ systematically from those who do not in terms of attributes important to health-related investigations. To understand potential bias in a clinic-based biobank of biological samples, including genetic data linked to electronic health record information, we compared patient characteristics and self-reported information among participants, nonresponders and refusers. We also compared reasons for nonparticipation between refusers and nonresponders to elucidate potential pathways to reduce nonparticipation and any uncovered bias. METHODS: We mailed recruitment packets to 1,600 adult patients with upcoming appointments at Mayo Clinic (Rochester, Minn., USA) and recorded their participation status. Administrative data were used to compare characteristics across groups. We used phone interviews with 26 nonresponders and 26 refusers to collect self-reported information, including reasons for nonparticipation. Participants were asked to complete a mailed questionnaire. RESULTS: We achieved 26.2% participation (n=419) with 12.1% refusing (n=193) and 61.8% nonresponse (n=988). In multivariate analyses, sex, age, region of residence, and race/ethnicity were significantly associated with participation. The groups differed in information-seeking behaviors and research experience. Refusers more often cited privacy concerns, while nonresponders more often identified time constraints as the reason for nonparticipation. CONCLUSION: For genomic medicine to advance, large, representative biobanks are required. Significant associations between patient characteristics and nonresponse, as well as systematic differences between refusers and nonresponders, could introduce bias. Oversampling or recruitment changes, including heightened attention to privacy protection and participation burden, may be necessary to increase participation among less-represented groups.