부산대학교 도서관

The analysis of time to treatment data and the evaluation of subsequent effects on health outcomes can be complex due to the nature of the data and the relationships amongst the variables. This study proposes an alternative method of analyzing such data using latent class analysis ( LCA). The association between time to begin adjuvant chemotherapy after breast cancer surgery and survival was investigated using both 'traditional' regression analysis and LCA. Women with breast cancer undergoing surgery and subsequent adjuvant chemotherapy in two English regions between January 01, 1998 and December 31, 2004 were identified from a linked cancer registry-Hospital Episode Statistics dataset ( n = 10,366). Patient, tumor, and treatment information were extracted. A Cox proportional hazards model was used to analyze 5-year survival using regression analysis and LCA. Using 'traditional' regression analysis, women beginning chemotherapy >10 weeks after surgery had worse survival in region 1 ( HR = 1.49, 95% CI 1.13-1.95 compared to <3 weeks) but not region 2. LCA split the women into three groups representing short, medium, and long waits. The median time to begin chemotherapy in the 'long' wait group was 70 (region 1) and 57 (region 2) days. In this group, increased time to begin chemotherapy was associated with worse survival (region 1 HR = 1.15, 95% CI 1.11-1.18; region 2 HR = 1.08, 95% CI 1.03-1.13 per week increase). LCA identified a group of 13-15% of women for whom a longer time to begin chemotherapy had an adverse effect on survival. This methodology provides an excellent framework in which to examine complex associations between the delivery of patient care and patient outcomes. [ABSTRACT FROM AUTHOR]