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Comparison Of Methods To Estimate Health State Utilities For Ovarian Cancer Using Quality of Life Data: A Gynecologic Oncology Group Study
Abstract
Highlights
►
Cost analyses of ovarian cancer treatment could be biased if estimation
methods are used to measure quality-adjusted life years.
► Comparisons of several utility-estimation methods found significant differences (p<0.001) between the utility values from these methods.
► There's a need to validate utility estimation methods before they can be recommended for cost analyses in ovarian cancer.
► Comparisons of several utility-estimation methods found significant differences (p<0.001) between the utility values from these methods.
► There's a need to validate utility estimation methods before they can be recommended for cost analyses in ovarian cancer.
Background
Cost-effectiveness/cost-utility
analyses are increasingly needed to inform decisions about care.
Algorithms have been developed using the Functional Assessment of Cancer
Therapy (FACT) quality of life instrument to estimate utility weights
for cost analyses. This study was designed to compare these algorithms
in the setting of ovarian cancer.
Methods
GOG-0152
was a 550-patient randomized phase III trial of interval cytoreduction,
and GOG-0172 was a 415-patient randomized phase III trial comparing
intravenous versus intraperitoneal therapy among women with advanced
ovarian cancer. QOL data were collected via the FACT at four time points
in each study. Two published mapping algorithms (Cheung and Dobrez) and
a linear transformation method were applied to these data. The
agreement between measures was assessed by the concordance correlation
coefficient (rCCC), and paired t-tests were used to compare means.
Results
While agreement between the estimation algorithms was good (ranged from 0.72 to 0.81), there were statistically significant (p< 0.001)
and clinically meaningful differences between the scores: mean scores
were higher with Dobrez than with Cheung or the linear transformation
method.. Scores were also statistically significantly different
(p<0.001) between studies.
Conclusions
In
the absence of prospectively collected utility data, the use of mapping
algorithms is feasible, however, the optimal algorithm is not clear.
There were significant differences between studies, which highlights the
need for validation of these algorithms in specific settings. If cost
analyses incorporate mapping algorithms to obtain utility estimates,
investigators should take the variability into account.
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