Wellman Gregory S, Vidican Carla
Ferris State University, College of Pharmacy, 220 Ferris Drive, Big Rapids, MI 49307, USA.
Res Social Adm Pharm. 2008 Sep;4(3):218-30. doi: 10.1016/j.sapharm.2007.08.002.
Consumers face an array of multiattribute prescription benefit insurance programs that include different access points (retail, supermarket, Internet, etc) and levels of pharmacist interaction (including medication therapy management services [MTMSs]). Because of this, there is a need for more sophisticated information to drive prescription benefit plan design.
A pilot study to determine if choice-based conjoint (CBC) analysis with hierarchical Bayes (HB) estimation for individual level part-worths could provide a stable model for attribute preferences for prescription benefit insurance; to pilot test the addition of MTMSs to a prescription benefit management model; and to pilot and compare logit-based utility estimates to HB estimations in a conjoint market simulator.
A mail-based survey was conducted using a random sample of 1500 residents of the United States. A CBC analysis instrument was developed to provide a single-stated choice from a selection of different prescription benefit plans. Choice tasks were varied based on the attributes: co-payment, pharmacy access, formulary, level of pharmacist interaction including MTMSs and monthly premium. Analysis included logit-based and HB estimation for utilities, and preference share market simulation testing.
The utility estimations from HB analysis were consistent with those seen in the logit-based analysis. A goodness of fit of 83% (root likelihood) was achieved in the HB utility estimations with only 4 choice tasks per respondents and the inclusion of MTM-like services. There was convergence on preference shares from the market simulation between the 2 estimation methods.
The use of CBC analysis with HB estimation provided utilities similar to those estimated using aggregated logit-based methods, with the added benefit of respondent specific part-worth scores for each attribute level. A larger sample, changes in the instrument design, more panels (tasks) per respondent, and selection of conjoint methods may allow for more predictive information from market simulators.
消费者面临一系列多属性的处方药福利保险计划，这些计划包括不同的获取途径（零售、超市、互联网等）以及不同水平的药剂师互动（包括药物治疗管理服务[MTMS]）。因此，需要更复杂的信息来推动处方药福利计划的设计。
进行一项试点研究，以确定使用具有层次贝叶斯（HB）估计个体水平部分价值的基于选择的联合分析（CBC）是否能为处方药福利保险的属性偏好提供一个稳定的模型；对在处方药福利管理模型中增加MTMS进行试点测试；并在联合市场模拟器中对基于logit的效用估计与HB估计进行试点和比较。
对1500名美国居民的随机样本进行了基于邮件的调查。开发了一种CBC分析工具，以便从不同的处方药福利计划中进行单一陈述选择。选择任务根据以下属性而变化：共付额、药房获取途径、药品目录、包括MTMS在内的药剂师互动水平以及月保费。分析包括基于logit和HB的效用估计，以及偏好份额市场模拟测试。
HB分析的效用估计与基于logit的分析结果一致。在HB效用估计中，每个受访者仅进行4个选择任务并纳入类似MTM的服务时，拟合优度达到了83%（根似然）。两种估计方法在市场模拟的偏好份额上趋于一致。
使用带有HB估计的CBC分析所提供的效用与使用基于logit的汇总方法估计的效用相似，其额外好处是为每个属性水平提供受访者特定的部分价值分数。更大的样本、工具设计的改变、每个受访者更多的面板（任务）以及联合方法的选择可能会从市场模拟器中获得更多预测信息。
