Estimating Joint Health State Utility Algorithms Under Partial Information.
Estimating Joint Health State Utility Algorithms Under Partial Information.
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DOI：
10.1016/j.jval.2022.09.009
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发表时间：
2023-05
期刊：
影响因子：
4.5
通讯作者：
Wittenberg, Eve
中科院分区：
文献类型：
--
作者：
Bray, Jeremy W.;Thornburg, Benjamin D.;Gebreselassie, Abraham W.;LaButte, Collin A.;Barbosa, Carolina;Wittenberg, Eve
我们探索了现有联合健康状态效用估计器的性能，当没有关于隔离单一条件健康状态的效用的数据时，排除任何共生条件。使用来自全国酒精和相关条件流行病学调查-III的数据，我们定义了2个信息集：(1)全信息集，包括在测试联合健康状态效用估计器的性能的大多数研究中使用的狭义健康状态效用，以及(2)有限信息集，仅包括研究人员更常见的更广义的健康状态效用。我们用一个酒精使用障碍与肝硬变、抑郁障碍或尼古丁使用障碍并存的例子来说明我们的分析。我们发现，有限信息下的联合健康状态效用估计器的性能明显不同于完全信息下的性能。全信息估计器通常高估联合状态效用，而有限信息估计器则低估联合状态效用，但最小估计器除外，后者在所有情况下都被高估。使用联合健康状态效用估计器的研究人员应该了解他们可以获得的信息集，并使用适合该信息集的方法学指导。基于它的易用性、一致性(以及可预测的偏差方向)和较低的均方根误差，我们建议在有限信息下使用最小估计器。
We explored the performance of existing joint health state utility estimators when data are not available on utilities that isolate single-condition health states excluding any co-occurring condition. Using data from the National Epidemiologic Survey on Alcohol and Related Conditions-III, we defined 2 information sets: (1) a full-information set that includes the narrowly defined health state utilities used in most studies that test the performance of joint health state utility estimators, and (2) a limited information set that includes only the more broadly defined health state utilities more commonly available to researchers. We used an example of alcohol use disorder co-occurring with cirrhosis of the liver, depressive disorder, or nicotine use disorder to illustrate our analysis. We found that the performance of joint health state utility estimators is appreciably different under limited information than under full information. Full-information estimators typically overestimate the joint state utility, whereas limited-information estimators underestimate the joint state utility, except for the minimum estimator, which is overestimated in all cases. Researchers using joint health state utility estimators should understand the information set available to them and use methodological guidance appropriate for that information set. We recommend the minimum estimator under limited information based on its ease of use, consistency (and therefore a predictable direction of bias), and lower root mean squared error.
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DOI：
10.1186/s13012-016-0408-4
发表时间：
2016-04-02
期刊：
Implementation science : IS
影响因子：
--
作者：
Bartlem KM;Bowman J;Freund M;Wye PM;Barker D;McElwaine KM;Wolfenden L;Campbell EM;McElduff P;Gillham K;Wiggers J
通讯作者：
Wiggers J
影响因子：
5.5
作者：
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通讯作者：
Brewer, Robert D.
影响因子：
3.6
作者：
Dale, William;Basu, Anirban;Meltzer, David
通讯作者：
Meltzer, David
影响因子：
3
作者：
Dale, William;Bilir, S. Pinar;Meltzer, David
通讯作者：
Meltzer, David
影响因子：
3.9
作者：
Korcha, Rachael A.;Polcin, Douglas L.;Galloway, Gantt P.
通讯作者：
Galloway, Gantt P.