Health Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany.
Gesellschaft für empirische Beratung GmbH, Freiburg, Germany.
PLoS One. 2021 Aug 23;16(8):e0256521. doi: 10.1371/journal.pone.0256521. eCollection 2021.
To examine subgroup-specific treatment preferences and characteristics of patients with hemophilia A.
Best-Worst Scaling (BWS) Case 3 (four attributes: application type; bleeding frequencies/year; inhibitor development risk; thromboembolic events of hemophilia A treatment risk) conducted via online survey. Respondents chose the best and the worst option of three treatment alternatives. Data were analyzed via latent class model (LCM), allowing capture of heterogeneity in the sample. Respondents were grouped into a predefined number of classes with distinct preferences.
The final dataset contained 57 respondents. LCM analysis segmented the sample into two classes with heterogeneous preferences. Preferences within each were homogeneous. For class 1, the most decisive factor was bleeding frequency/year. Respondents seemed to focus mainly on this in their choice decisions. With some distance, inhibitor development was the second most important. The remaining attributes were of far less importance for respondents in this class. Respondents in class 2 based their choice decisions primarily on inhibitor development, also followed, by some distance, the second most important attribute bleeding frequency/year. There was statistical significance (P < 0.05) between the number of annual bleedings and the probability of class membership.
The LCM analysis addresses heterogeneity in respondents' choice decisions, which helps to tailor treatment alternatives to individual needs. Study results support clinical and allocative decision-making and improve the quality of interpretation of clinical data.
研究血友病 A 患者的亚组特异性治疗偏好和特征。
通过在线调查进行最佳最差分级法案例 3(四个属性:应用类型;出血频率/年;抑制剂发展风险;血友病 A 治疗风险的血栓栓塞事件)。受访者选择三种治疗方案中最好和最差的选项。通过潜在类别模型(LCM)分析数据,允许捕获样本中的异质性。受访者被分为具有不同偏好的预先定义数量的类别。
最终数据集包含 57 名受访者。LCM 分析将样本分为具有不同偏好的两个类别。每个类别的偏好都是同质的。对于类别 1,最决定性的因素是出血频率/年。受访者似乎主要在他们的选择决策中关注这一点。抑制剂的发展是第二重要的。其余属性对该类别的受访者来说重要性要小得多。类别 2 的受访者主要根据抑制剂的发展来做出选择决定,其次是第二重要的属性出血频率/年。每年出血次数和类别成员概率之间存在统计学意义(P < 0.05)。
LCM 分析解决了受访者选择决策中的异质性问题,有助于根据个体需求定制治疗方案。研究结果支持临床和分配决策,并提高了对临床数据的解释质量。