Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30 : a confirmatory versus exploratory approach

Background Multi attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure. Aim To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure. Methods QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis. Results CFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA. Conclusion CFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure.

Causes of rigidity in SMEs' export commencement decision

Today, small-medium sized enterprises (SMEs) collectively contribute to the largest percentage of job creation in OECD countries. SMEs have become increasingly international since the turn of the century despite being smaller in size in comparison to large multinational firms, and notably, exporting is the most favoured mode of international market entry utilised by SMEs in their internationalisation strategy. Governments around the world have acknowledged the importance of export promotion and have employed policies that are targeted at increasing the export activity of SMEs. However, in many countries, the involvement of SMEs in export operations remains rather low. Within Australia, for example, only about one-third of local SMEs are exporting and this raises an important question as to why there is such a huge percentage of non-exporters. Much scholarly research that focuses on this problem has concentrated on the broad concept of 'export barriers' that act as obstacles to a firm's export development. This paper takes a different approach to previous studies and proposes that a firm's resistance to commence exporting can be better understood through an analysis of the behavioural decision process during its pre-export state. Using a sample of Australian SMEs, the factors that are important in preventing a firm’s initial export commencement decision are categorised and discussed through the use of factor analysis.

Clinical data warehousing : a business analytics approach for managing health data

Heterogeneous health data is a critical issue when managing health information for quality decision making processes. In this paper we examine the efficient aggregation of lifestyle information through a data warehousing architecture lens. We present a proof of concept for a clinical data warehouse architecture that enables evidence based decision making processes by integrating and organising disparate data silos in support of healthcare services improvement paradigms.