Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada.
BMC Med Res Methodol. 2012 Feb 20;12:15. doi: 10.1186/1471-2288-12-15.
A discrete choice experiment (DCE) is a preference survey which asks participants to make a choice among product portfolios comparing the key product characteristics by performing several choice tasks. Analyzing DCE data needs to account for within-participant correlation because choices from the same participant are likely to be similar. In this study, we empirically compared some commonly-used statistical methods for analyzing DCE data while accounting for within-participant correlation based on a survey of patient preference for colorectal cancer (CRC) screening tests conducted in Hamilton, Ontario, Canada in 2002.
A two-stage DCE design was used to investigate the impact of six attributes on participants' preferences for CRC screening test and willingness to undertake the test. We compared six models for clustered binary outcomes (logistic and probit regressions using cluster-robust standard error (SE), random-effects and generalized estimating equation approaches) and three models for clustered nominal outcomes (multinomial logistic and probit regressions with cluster-robust SE and random-effects multinomial logistic model). We also fitted a bivariate probit model with cluster-robust SE treating the choices from two stages as two correlated binary outcomes. The rank of relative importance between attributes and the estimates of β coefficient within attributes were used to assess the model robustness.
In total 468 participants with each completing 10 choices were analyzed. Similar results were reported for the rank of relative importance and β coefficients across models for stage-one data on evaluating participants' preferences for the test. The six attributes ranked from high to low as follows: cost, specificity, process, sensitivity, preparation and pain. However, the results differed across models for stage-two data on evaluating participants' willingness to undertake the tests. Little within-patient correlation (ICC ≈ 0) was found in stage-one data, but substantial within-patient correlation existed (ICC = 0.659) in stage-two data.
When small clustering effect presented in DCE data, results remained robust across statistical models. However, results varied when larger clustering effect presented. Therefore, it is important to assess the robustness of the estimates via sensitivity analysis using different models for analyzing clustered data from DCE studies.
离散选择实验（DCE）是一种偏好调查，它通过执行多项选择任务，要求参与者在比较关键产品特征的产品组合中做出选择。分析 DCE 数据需要考虑参与者内相关性，因为来自同一参与者的选择很可能相似。在这项研究中，我们根据 2002 年在加拿大安大略省汉密尔顿市进行的一项关于结直肠癌（CRC）筛查测试患者偏好的调查，实证比较了一些常用的统计方法，以分析考虑参与者内相关性的 DCE 数据。
采用两阶段 DCE 设计，调查了六个属性对参与者对 CRC 筛查测试的偏好和进行测试的意愿的影响。我们比较了六种用于聚类二项结果的模型（使用聚类稳健标准误差（SE）的逻辑和概率回归、随机效应和广义估计方程方法）和三种用于聚类名义结果的模型（具有聚类稳健 SE 的多项逻辑和概率回归以及随机效应多项逻辑模型）。我们还拟合了一个具有聚类稳健 SE 的双变量概率模型，将两个阶段的选择视为两个相关的二项结果。属性之间的相对重要性的排名和属性内的β系数的估计值用于评估模型稳健性。
共分析了 468 名参与者，每位参与者完成了 10 次选择。在评估参与者对测试的偏好的第一阶段数据中，不同模型的相对重要性排名和β系数结果相似。六个属性的排名从高到低依次为：成本、特异性、过程、敏感性、准备和疼痛。然而，在评估参与者进行测试的意愿的第二阶段数据中，不同模型的结果不同。在第一阶段数据中发现患者内相关性很小（ICC≈0），但在第二阶段数据中存在很大的患者内相关性（ICC=0.659）。
当 DCE 数据中存在小的聚类效应时，结果在不同的统计模型中仍然稳健。然而，当存在较大的聚类效应时，结果会有所不同。因此，通过使用不同的模型对 DCE 研究中的聚类数据进行敏感性分析，评估估计值的稳健性非常重要。
