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Explore preference heterogeneity for treatment among people with Type 2 diabetes: A comparison of random-parameters and latent-class estimation techniques
Journal of Choice Modelling ( IF 4.164 ) Pub Date : 2019-03-01 , DOI: 10.1016/j.jocm.2018.11.002
Mo Zhou , John F.P. Bridges

Abstract There has been an increasing interest in studying patient preference heterogeneity to support regulatory decision-making. While the traditional mixed logit (MXL) and the latent class logit (LCL) models have been commonly used to analyze preference heterogeneity in discrete choice data, they have limitations. This study empirically compares a random effects latent class logit (RELCL) model to the traditional approaches using preference data from a discrete-choice experiment among patients with Type 2 diabetes. Each survey contained 18 pairs of hypothetical diabetes medications that differed in six attributes. Sensitivity analysis is also performed to explore under what circumstances RELCL outperforms LCL. Significant preference heterogeneity was found in all models. The 2-class RELCL has the lowest BIC (8350.64) and prediction error (11.6%) compared to MXL (BIC = 9345.40; pred. err. = 13.0%) and the 5-class LCL (BIC = 8440.30; pred. err. = 16.4%), indicating improved model fit. Allowing random effects also reduces the number of classes from five in LCL to two, both having significant policy and clinical implications. RELCL provides the flexibility of LCL and the parsimony of MXL. Both our empirical results and sensitivity analysis shows that when there is significant preference heterogeneity among patients that cannot be captured by a small number of clusters, RELCL may be used to generate more accurate predictions and more parsimonious results that are policy-relevant.

中文翻译:

探索2型糖尿病患者偏好的治疗异质性:随机参数和潜在类别估计技术的比较

摘要研究患者偏好异质性以支持监管决策的兴趣日益浓厚。传统的混合logit(MXL)模型和潜在类logit(LCL)模型通常用于分析离散选择数据中的偏好异质性,但它们有局限性。这项研究使用来自2型糖尿病患者的离散选择实验的偏好数据,将随机效应潜伏类logit(RELCL)模型与传统方法进行了经验比较。每个调查都包含18对假想的糖尿病药物,它们在六个属性上有所不同。还进行了敏感性分析,以探索在什么情况下RELCL优于LCL。在所有模型中均发现显着的偏好异质性。2级RELCL的BIC(8350.64)和预测误差(11。6%),而MXL(BIC = 9345.40;预测错误= 13.0%)和5级LCL(BIC = 8440.30;预测错误= 16.4%),表明模型拟合得到了改善。允许随机效应还可以将拼箱中的类别数量从五个减少到两个,这两者都具有重要的政策和临床意义。RELCL提供了LCL的灵活性和MXL的简约性。我们的经验结果和敏感性分析均显示,当少数人群无法捕获的患者中存在显着的偏好异质性时,RELCL可用于生成更准确的预测和与政策相关的更简约的结果。允许随机效应还可以将拼箱中的类别数量从五个减少到两个,这两者都具有重要的政策和临床意义。RELCL提供了LCL的灵活性和MXL的简约性。我们的经验结果和敏感性分析均显示,当少数人群无法捕获的患者中存在显着的偏好异质性时,RELCL可用于生成更准确的预测和与政策相关的更简约的结果。允许随机效应还可以将拼箱中的类别数量从五个减少到两个,这两者都具有重要的政策和临床意义。RELCL提供了LCL的灵活性和MXL的简约性。我们的经验结果和敏感性分析均显示,当少数人群无法捕获的患者中存在显着的偏好异质性时,RELCL可用于生成更准确的预测和与政策相关的更简约的结果。
更新日期:2019-03-01
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