Bridges, J.F., Kinter, E.T., Kidane, L. et al. Patient-Patient-Centered-Outcome-Res (2008) 1: 273. doi:10.2165/1312067-200801040-00009
Clinical and healthcare decision makers have repeatedly endorsed patient-centered care as a goal of the health system. However, traditional methods of evaluation reinforce societal views, and research focusing on views of patients is often referred to as ‘soft science.’ Conjoint analysis presents a scientifically rigorous research tool that can be used to understand patient preferences and inform decision making. This paper documents applications of conjoint analysis in medicine and systematically reviews this literature in order to identify publication trends and the range of topics to which conjoint analysis has been applied. In addition, we document important methodological aspects such as sample size, experimental design, and method of analysis.
Publications were identified through a MEDLINE search using multiple search terms for identification. We classified each article into one of three categories: clinical applications (n = 122); methodological contributions (n = 56); and health system applications (n = 47). Articles that did not use or adequately discuss conjoint analysis methods (n = 164) were discarded. We identified a near exponential increase in the application of conjoint analyses over the last 10 years of the study period (1997–2007). Over this period, the proportion of applications on clinical topics increased from 40% of articles published in MEDLINE from 1998 to 2002, to 64% of articles published from 2003 to 2007 (p = 0.002).
The average sample size among articles focusing on health system applications (n = 556) was significantly higher than clinical applications (n = 277) [p = 0.001], although this 2-fold difference was primarily due to a number of outliers reporting sample sizes in the thousands. The vast majority of papers claimed to use orthogonal factorial designs, although over a quarter of papers did not report their design properties. In terms of types of analysis, logistic regression was favored among clinical applications (28%), while probit was most commonly used among health systems applications (38%). However, 25% of clinical applications and 33% of health systems articles failed to report what regression methods were used. We used the International Classification of Diseases — version 9 (ICD-9) coding system to categorize clinical applications, with approximately 26% of publications focusing on neoplasm. Program planning and evaluation applications accounted for 22% of the health system articles.
While interest in conjoint analysis in health is likely to continue, better guidelines for conducting and reporting conjoint analyses are needed.