Objective: To develop a ridge penalized principal-components approach based on heritability that can be applied to high-dimensional family data. Methods: The first principal component of heritability for a trait constellation is defined as a linear combination of traits that maximizes the heritability, which is equivalent to maximize the family-specific variation relative to the subject-specific variation. To analyze high-dimensional data and prevent overfitting, we propose a penalized principal-components approach based on heritability by adding a ridge penalty to the subject-specific variation. We choose the optimal regularization parameter by cross-validation. Results: The principal-components approach based on heritability with and without ridge penalty was compared to the usual principal-components analysis in four settings. The penalized principal-components of heritability analysis had substantially larger coefficients for the traits with genetic effect than for the traits with no genetic effect, while the non-regularized analysis failed to identify the genetic traits. In addition, linkage analysis on the combined traits showed that the power of the proposed methods was higher than the usual principal-components analysis and the non-regularized principal-components of heritability analysis. Conclusions: The penalized principal-components approach based on heritability can effectively handle large number of traits with family structure and provide power gain for linkage analysis. The cross-validation procedure performs well in choosing optimal magnitude of penalty.

The American Cancer Society (ACS) and Coalition of Cancer Cooperative Groups (CCCG) provide a clinical trial (CT) information/matching/eligibility service (Clinical Trials Matching Service [CTMS]). Patients' demographic and clinical data, enrollment status, and self-reported barriers to CT participation were analyzed to assess enrollment rates and determinants of enrollment. During 3 years beginning October 1, 2007, the CTMS served 6,903 patients via the ACS call center. Among the 1,987 patients with follow-up information on enrollment, 219 (11.0%) enrolled in a CT; 48 of these 219 enrollees chose a CT they found via the CTMS. Patients were less likely to enroll if they had poor ECOG performance status (P = 0.032); were African American (P = 0.0003), were uninsured or had Medicaid coverage (P = 0.024), or had lower stage disease (P = 0.018). Enrollment varied by trial type/cancer site/system (P = .026). Several barriers significantly predicted nonenrollment. Broader availability of a CTMS might help improve patient participation in cancer clinical trials.

In family studies, canonical discriminant analysis can be used to find linear combinations of phenotypes that exhibit high ratios of between-family to within-family variabilities. But with large numbers of phenotypes, canonical discriminant analysis may overfit. To estimate the predicted ratios associated with the coefficients obtained from canonical discriminant analysis, two methods are developed; one is based on bias correction and the other based on cross-validation. Because the cross-validation is computationally intensive, an approximation to the cross-validation is also developed. Furthermore, these methods can be applied to perform variable selection in canonical discriminant analysis. The proposed methods are illustrated with simulation studies and applications to two real examples.

e17504 Methods: Data from CTMS constituents and follow-up information describing enrollment status and barriers to trial participation are reviewed. Results: During 15 months of operation the CTMS provided information to 10,997 individuals; 7,521 (68.39%) used the website only, and 3,476 (31.61%) also contacted the ACS call center. Among 981 of the 3,476 (28.22% the basis of analyses below) who consented to and could be reached for follow-up and who answered the question on enrollment status, 119 (12.13%) enrolled in a CT. Trial phase was known for 74 enrollees (phase I: 17 [22.97%]; II: 36 [48.65%]; III: 21 [28.38%]; IV: 0 [0%]). Enrollment was negatively (p < 0.05) associated with poor ECOG functional status and black race, and was positively related to disease stage. Among the 757 individuals with available disease site and enrollment information, those with stomach cancer accounted for the most enrollments (25, 24.75% of all enrollments); followed by melanoma (12, 11.88%) and kidney, renal pelvis, bladder, ureter and urethra (also 12, 11.88%), and breast cancer (11, 10.89%). The highest enrollment rates (% enrollees among individuals with available follow-up) were for multiple myeloma/plasma cell disorders (4/14, 28.57%), melanoma (12/49, 24.49%), primary CNS malignancy (5/31, 16.13%), and soft tissue sarcoma (6/45, 13.33%). The following barriers were significantly associated with non-enrollment: ‘I cannot travel to clinical trial site,‘ ‘I cannot find a clinical trial using the modality or treatment I want,‘ ‘My physical activity level is too low,‘ and ‘I do not have measurable disease or am cancer-free.‘ Conclusions: 12% of CTMS participants with available follow-up data for enrollment status participated in a CT. Several determinants of CT participation were identified. Strategies for eliminating racial disparities, facilitating transportation, and increasing participation among patients with earlier stage disease and more common tumor types must be developed and implemented. No significant financial relationships to disclose.