Validation of the predictive accuracy of health-state utility values based on the Lloyd model for metastatic or recurrent breast cancer in Japan
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https://datadryad.org/dataset/doi:10.5061/dryad.r2280gbbp
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Although there is a lack of data on health-state utility values (HSUVs)
for calculating quality-adjusted life years in Japan, Cost-utility
analysis has been introduced by the Japanese government to inform
decision-making in the medical field since 2016. This study aimed to
determine whether the Lloyd model which was a predictive model of HSUVs
for metastatic breast cancer (MBC) patients in the United Kingdom can
accurately predict actual HSUVs for Japanese patients with MBC. The
prospective observational study, followed by the validation study of the
clinical predictive model. Forty-four Japanese patients with MBC
were studied at 336 survey points. This study consisted of two phases. In
the first phase, we constructed a database of clinical data prospectively
and HSUVs for Japanese patients with MBC to evaluate the predictive
accuracy of HSUVs calculated using the Lloyd model. In the second phase,
Bland-Altman analysis was used to determine how accurately predicted HSUVs
(based on the Lloyd model) correlated with actual HSUVs obtained using the
EuroQol 5-Dimension 5-Level questionnaire, a preference-based measure of
HSUVs in patients with MBC. In the Bland-Altman analysis, the
mean difference between HSUVs estimated by the Lloyd model and actual
HSUVs, or systematic error, was -0.106. The precision was 0.165. The 95%
limits of agreement ranged from -0.436 to 0.225. The t value was 4.6972,
which was greater than the t value with 2 degrees of freedom at the 5%
significance level (p=0.425). There were acceptable degrees of fixed and
proportional errors associated with the prediction of HSUVs based on the
Lloyd model for Japanese patients with MBC. We recommend that sensitivity
analysis be performed when conducting cost-effectiveness analyses with
HSUVs calculated using the Lloyd model.
提供机构:
Dryad
创建时间:
2021-11-10



