1 Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA 2 Center for Medicine in the Public Interest (CMPI), New York, New York, USA 3 Department of Gynecology and Obstetrics, The Johns Hopkins Hospital, Kelly Gynecologic Oncology Service, Baltimore, Maryland, USA.
Patient. 2008 Dec 1;1(4):273-82. doi: 10.2165/01312067-200801040-00009.
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.
临床和医疗保健决策者多次认可以患者为中心的护理是医疗体系的目标。然而，传统的评估方法强化了社会观点，而专注于患者观点的研究通常被称为“软科学”。联合分析提供了一种科学严谨的研究工具，可以用来了解患者偏好并为决策提供信息。本文记录了联合分析在医学中的应用，并系统地回顾了这方面的文献，以确定出版物趋势和联合分析应用的范围。此外，我们还记录了重要的方法学方面，如样本量、实验设计和分析方法。通过使用多个搜索词进行多次搜索，从 MEDLINE 中确定了出版物。我们将每篇文章分为三类：临床应用（n=122）；方法学贡献（n=56）；和卫生系统应用（n=47）。未使用或充分讨论联合分析方法的文章（n=164）被丢弃。我们发现，在研究期间的最后 10 年中，联合分析的应用呈近指数增长（1997-2007 年）。在此期间，在 MEDLINE 中发表的 1998 年至 2002 年期间，关于临床主题的应用比例从 40%增加到 2003 年至 2007 年期间的 64%（p=0.002）。关注卫生系统应用的文章的平均样本量（n=556）明显高于临床应用（n=277）[p=0.001]，尽管这种 2 倍的差异主要是由于一些异常值报告了数千个样本量。绝大多数论文声称使用正交因子设计，尽管四分之一以上的论文没有报告其设计属性。在分析类型方面，逻辑回归在临床应用中更为常见（28%），而概率回归在卫生系统应用中更为常见（38%）。然而，25%的临床应用和 33%的卫生系统文章未能报告使用了何种回归方法。我们使用国际疾病分类第 9 版（ICD-9）编码系统对临床应用进行分类，约 26%的出版物集中于肿瘤。规划和评估应用占卫生系统文章的 22%。尽管人们对健康联合分析的兴趣可能会继续，但需要更好的进行和报告联合分析的指南。
