Duke Clinical Research Institute, Duke University, Durham, NC, USA.
Department of Population Health Sciences, Duke University, Durham, NC, USA.
Med Decis Making. 2021 Feb;41(2):222-232. doi: 10.1177/0272989X20979833. Epub 2021 Jan 19.
To test the convergent validity of simple and more complex study designs in a discrete-choice experiment (DCE) of multiple sclerosis (MS) treatment preferences.
Five hundred US adults with MS completed an online DCE survey. Respondents answered 8 choice questions with pairs of constructed MS treatment profiles defined by delays in problems with walking, delays in problems with cognition, thyroid disorders, and 10-y risks of kidney failure and serious brain infection (i.e., progressive multifocal leukoencephalopathy [PML]). Four hundred respondents completed choice questions using 4 levels for all attributes, except thyroid disorders with 3 levels. One hundred respondents completed choice questions using only the 2 extreme attribute levels of the 4-level version. Random-parameters logit models were used to estimate choice-model parameters.
Respondents viewing the 4-level and 2-level versions agreed on the relative importance of the 3 most important attributes: cognition, walking, and PML. Respondents viewing the 4-level version indicated much stronger disutility for a 0% to 0.5% increase in kidney-failure risk than those viewing the 2-level version where the risk for kidney failure increased from 0% to 3%. Otherwise, utilities for other 4-level attributes were approximately linear but with significantly steeper slopes (except for cognition) than the 2-level estimates, indicating that attributes were perceived as more important as the number of levels increased.
Although the relative importance of some attributes was similar, the 2-level and 4-level versions generally failed to demonstrate convergent validity. If the study goal is attribute rankings, a 2-level version could be adequate. If goals include quantifying tradeoffs among attribute levels, more complex designs can help respondents discriminate among attribute levels. Reductions in measurement error using fewer attribute levels appear to have come at the expense of less discriminating evaluations.
在多发性硬化症（MS）治疗偏好的离散选择实验（DCE）中，测试简单和更复杂设计的收敛有效性。
500 名美国 MS 成年人完成了在线 DCE 调查。受访者回答了 8 个选择题，其中包含由行走问题延迟、认知问题延迟、甲状腺疾病以及 10 年肾衰竭和严重脑部感染（即进行性多灶性白质脑病 [PML]）风险组成的 MS 治疗方案配对。400 名受访者使用除甲状腺疾病外的所有属性的 4 个水平完成了选择问题，而甲状腺疾病的属性水平为 3 个。100 名受访者使用 4 级版本的仅 2 个极端属性水平完成了选择问题。随机参数对数模型用于估计选择模型参数。
观看 4 级和 2 级版本的受访者对 3 个最重要属性（认知、行走和 PML）的相对重要性达成了一致。观看 4 级版本的受访者表示，与观看 2 级版本相比，他们对肾衰竭风险从 0%增加到 0.5%增加的不舒适感更强，而在 2 级版本中，肾衰竭风险从 0%增加到 3%。否则，其他 4 级属性的效用接近线性，但斜率明显更陡（认知除外），表明随着水平数量的增加，属性被认为更重要。
尽管一些属性的相对重要性相似，但 2 级和 4 级版本通常未能表现出收敛有效性。如果研究目标是属性排名，2 级版本可能就足够了。如果目标包括量化属性水平之间的权衡，更复杂的设计可以帮助受访者区分属性水平。使用较少的属性水平减少测量误差似乎是以牺牲更具辨别力的评估为代价的。
