Adjuvant treatment of premenopausal women with endocrine-responsive early breast cancer: design of the TEXT and SOFT trials

OBJECTIVES In 2003 the International Breast Cancer Study Group (IBCSG) initiated the TEXT and SOFT randomized phase III trials to answer two questions concerning adjuvant treatment for premenopausal women with endocrine-responsive early breast cancer: 1-What is the role of aromatase inhibitors (AI) for women treated with ovarian function suppression (OFS)? 2-What is the role of OFS for women who remain premenopausal and are treated with tamoxifen? METHODS TEXT randomized patients to receive exemestane or tamoxifen with OFS. SOFT randomized patients to receive exemestane with OFS, tamoxifen with OFS, or tamoxifen alone. Treatment was for 5 years from randomization. RESULTS TEXT and SOFT successfully met their enrollment goals in 2011. The 5738 enrolled women had lower-risk disease and lower observed disease-free survival (DFS) event rates than anticipated. Consequently, 7 and 13 additional years of follow-up for TEXT and SOFT, respectively, were required to reach the targeted DFS events (median follow-up about 10.5 and 15 years). To provide timely answers, protocol amendments in 2011 specified analyses based on chronological time and median follow-up. To assess the AI question, exemestane + OFS versus tamoxifen + OFS, a combined analysis of TEXT and SOFT became the primary analysis (n = 4717). The OFS question became the primary analysis from SOFT, assessing the unique comparison of tamoxifen + OFS versus tamoxifen alone (n = 2045). The first reports are anticipated in mid- and late-2014. CONCLUSIONS We present the original designs of TEXT and SOFT and adaptations to ensure timely answers to two questions concerning optimal adjuvant endocrine treatment for premenopausal women with endocrine-responsive breast cancer. Trial Registration TEXT: Clinicaltrials.govNCT00066703 SOFT: Clinicaltrials.govNCT00066690.

Preliminary Evaluation of a Guideline Classification System

In a previous paper, we presented a proposed expansion of the National Guideline Clearing-house (NGC) classification1. We performed a preliminary evaluation of the classification based on 100 guidelines randomly selected from the NGC collection. We found that 89 of the 100 guidelines could be assigned to a single guideline category. To test inter-observer agreement, twenty guidelines were also categorized by a second investigator. Agreement was found to be 40-90% depending on the axis, which compares favorably with agreement among MeSH indexers (30-60%)2. We conclude that categorization is feasible. Further research is needed to clarify axes with poor inter-observer agreement.

A PROCESS FOR ACHIEVING COMPARABLE DATA FROM HETEROGENEOUS DATABASES

The current state of health and biomedicine includes an enormity of heterogeneous data ‘silos’, collected for different purposes and represented differently, that are presently impossible to share or analyze in toto. The greatest challenge for large-scale and meaningful analyses of health-related data is to achieve a uniform data representation for data extracted from heterogeneous source representations. Based upon an analysis and categorization of heterogeneities, a process for achieving comparable data content by using a uniform terminological representation is developed. This process addresses the types of representational heterogeneities that commonly arise in healthcare data integration problems. Specifically, this process uses a reference terminology, and associated "maps" to transform heterogeneous data to a standard representation for comparability and secondary use. The capture of quality and precision of the “maps” between local terms and reference terminology concepts enhances the meaning of the aggregated data, empowering end users with better-informed queries for subsequent analyses. A data integration case study in the domain of pediatric asthma illustrates the development and use of a reference terminology for creating comparable data from heterogeneous source representations. The contribution of this research is a generalized process for the integration of data from heterogeneous source representations, and this process can be applied and extended to other problems where heterogeneous data needs to be merged.

Towards a Hybrid Method to Categorize Interruptions and Activities in Healthcare

Objective Interruptions are known to have a negative impact on activity performance. Understanding how an interruption contributes to human error is limited because there is not a standard method for analyzing and classifying interruptions. Qualitative data are typically analyzed by either a deductive or an inductive method. Both methods have limitations. In this paper a hybrid method was developed that integrates deductive and inductive methods for the categorization of activities and interruptions recorded during an ethnographic study of physicians and registered nurses in a Level One Trauma Center. Understanding the effects of interruptions is important for designing and evaluating informatics tools in particular and for improving healthcare quality and patient safety in general. Method The hybrid method was developed using a deductive a priori classification framework with the provision of adding new categories discovered inductively in the data. The inductive process utilized line-by-line coding and constant comparison as stated in Grounded Theory. Results The categories of activities and interruptions were organized into a three-tiered hierarchy of activity. Validity and reliability of the categories were tested by categorizing a medical error case external to the study. No new categories of interruptions were identified during analysis of the medical error case. Conclusions Findings from this study provide evidence that the hybrid model of categorization is more complete than either a deductive or an inductive method alone. The hybrid method developed in this study provides the methodical support for understanding, analyzing, and managing interruptions and workflow.

Towards a hybrid method to categorize interruptions and activities in healthcare.

OBJECTIVE: Interruptions are known to have a negative impact on activity performance. Understanding how an interruption contributes to human error is limited because there is not a standard method for analyzing and classifying interruptions. Qualitative data are typically analyzed by either a deductive or an inductive method. Both methods have limitations. In this paper, a hybrid method was developed that integrates deductive and inductive methods for the categorization of activities and interruptions recorded during an ethnographic study of physicians and registered nurses in a Level One Trauma Center. Understanding the effects of interruptions is important for designing and evaluating informatics tools in particular as well as improving healthcare quality and patient safety in general. METHOD: The hybrid method was developed using a deductive a priori classification framework with the provision of adding new categories discovered inductively in the data. The inductive process utilized line-by-line coding and constant comparison as stated in Grounded Theory. RESULTS: The categories of activities and interruptions were organized into a three-tiered hierarchy of activity. Validity and reliability of the categories were tested by categorizing a medical error case external to the study. No new categories of interruptions were identified during analysis of the medical error case. CONCLUSIONS: Findings from this study provide evidence that the hybrid model of categorization is more complete than either a deductive or an inductive method alone. The hybrid method developed in this study provides the methodical support for understanding, analyzing, and managing interruptions and workflow.