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Estimation of heterogeneous average treatment effect: Panel data correlated random coefficients model with polychotomous endogenous treatments.

机译：异质平均治疗效果的估计：面板数据将随机系数模型与多发性内源性治疗相关联。

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摘要

We estimated the treatment effects of biologic disease modifying anti-rheumatoid drugs (DMARDs) on the quarterly total health-care expenditure, while controlling non-random assignment to treatment (endogeneity) and allowing heterogeneity in treatment effects. The structural parameters, heterogeneous (ATE), and homogeneous (ATE1) average treatment effects were defined as the impact of treatment on quarterly total health-care expenditure, if patients are randomly assigned to biologic DMARDs.;A retrospective cohort was selected from California Medicaid paid claims between 01/01/1999 and 12/31/2005. Non-overlapping quarters were created from pharmacy claims for biologic (adalimumab and etanercept) and standard (leflunomide, hydroxychloroquine and sulfasalazine) DMARDs. Final sample included 23,297 observations on 5,239 individual patients.;A fixed-effects panel data correlated random coefficients (CRC) model allowed for heterogeneity in treatment effects. Endogeneity was controlled by adding a generalized residual function constructed based on Lee's (1983) approach. Selection choice model was varied from the multinomial, nested, and mixed logit.;Controlling endogeneity significantly increased ATE1 for both biologic DMARDs, as compared to naive fixed-effects (baseline standard DMARD). Nested-logit based ATE1 was higher as compared to the multinomial-logit ATE1. Allowing for unobserved heterogeneity resulted in the ATE of adalimumab to decrease under the multinomial-logit corrected model, while an increase was observed in the nested-logit corrected model. In case of ATE for etanercept, an increase was observed under both the above mentioned models as compared to ATE1.;The results point out the need to control for time-varying endogeneity in panel data models. When treatment effects are heterogeneous and especially when treatment selection is a discrete choice set, the specification of latent index model matters. Sorting on gains is an important source of bias in medical outcomes and in this study, it manifested in terms of large differences in the magnitude of ATE1 and ATE parameters, which questions homogeneity assumption.;The methodological issues addressed in this study impact our understanding of the cost effects of drug treatment. Models need to be realistic to mimic real life clinical decisions to inform important drug coverage decisions. Panel data CRC model with endogeneity correction is one such tool to assess comparative effectiveness using an observational study design for expenditure outcomes.
机译：我们估计了生物疾病修饰抗类风湿药（DMARDs）对季度医疗总支出的治疗效果，同时控制了治疗的非随机分配（内源性）并允许治疗效果的异质性。如果将患者随机分配给生物DMARD，则将结构参数，异质（ATE）和均质（ATE1）平均治疗效果定义为治疗对季度总医疗保健支出的影响。；回顾性队列选自加利福尼亚医疗补助在1999年1月1日至2005年12月31日之间支付了索赔。不重叠的四分之一是根据生物药品（阿达木单抗和依那西普）和标准（来氟米特，羟氯喹和柳氮磺胺吡啶）DMARD的药理要求创建的。最终样本包括对5,239名患者的23,297项观察结果。固定效果面板数据相关的随机系数（CRC）模型允许治疗效果存在异质性。通过添加基于Lee（1983）方法构建的广义残差函数来控制内生性。选择选择模型从多项式，嵌套式和混合对数变化而来。与内在的固定效应（基线标准DMARD）相比，控制内生性使两种生物DMARD的ATE1均显着增加。与多项式登录ATE1相比，基于嵌套登录的ATE1更高。在多项式对数校正模型下，允许未观察到的异质性导致阿达木单抗的ATE降低，而在嵌套对数校正模型下观察到了增加。在ATE用于依那西普的情况下，与ATE1相比，在上述两种模型下均观察到了增加。结果表明需要控制面板数据模型中随时间变化的内生性。当治疗效果不均一时，尤其是当治疗选择是离散选择集时，潜在指数模型的规范就很重要。对收益进行排序是医学结果偏倚的重要来源，在这项研究中，它表现为ATE1和ATE参数的大小存在巨大差异，这对同质性假设提出了质疑。药物治疗的成本效应。模型必须真实可行，以模仿现实生活中的临床决策，以告知重要的药物覆盖范围决策。使用内生性校正的面板数据CRC模型就是这样一种工具，它使用观察性研究设计来评估支出结果，从而评估比较效果。

著录项

  • 作者

    Kawatkar, Aniket Arun.;

  • 作者单位

    University of Southern California.;

  • 授予单位 University of Southern California.;
  • 学科 Economics General.;Economics Theory.;Health Sciences Pharmacy.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 136 p.
  • 总页数 136
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 经济学;经济学;药剂学;
  • 关键词

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