Paula's poster for ISPOR on comparing cancer-specific preference-based outcome measures.

1. COMPARING CANCER-SPECIFIC
PREFERENCE-BASED OUTCOME
MEASURES: THE SAME BUT DIFFERENT
Lorgelly P1 & Norman R2 and Cancer 2015 investigators
1Office of Health Economics; 2Curtin University
Corresponding author: plorgelly@ohe.org
BACKGROUND
• Disease-specific outcome measures for use in economic
evaluations are growing in popularity.
• Within cancer there are now two preference-based
measures: the EORTC-8D and the QLU-C10D. [1,2]
• Both map responses from the EORTC QLQ-C30, a
questionnaire which measures the quality-of-life of cancer
patients.
• They share some commonalities in the C30 items that
they draw from and the analytical approach applied in
selecting their item dimensions, but they differ in other
areas: the clinical characteristics of the patient group
within which they conducted their analysis and the
valuation approach.
• No research to date has compared the two measures and
evaluated what impact their use will have on HTA and
decision making.
Acknowledgements
Cancer 2015 is funded by the Victorian Cancer Agency Translational
Research Program. We are grateful to all the cancer patients who
agreed to participate in the cohort.
References
1. King MT et al. "QLU-C10D: a health state classification system for a
multi-attribute utility measure based on the EORTC QLQ-C30."
Quality of Life Research 25 (2016): 625-636.
2. Rowen D et al. "Deriving a preference-based measure for cancer
using the EORTC QLQ-C30." Value in Health 14 (2011): 721-731.
3. Thomas DM et al. "Cancer 2015: a longitudinal whole-of-system
study of genomic cancer medicine." Drug Discovery Today 20
(2015): 1429-1432.
AIMS
• Compare and contrast the EORTC-8D and the QLU-C10D.
• Consider the validity, sensitivity, ceiling effects and
agreement of the instruments and cancer-specific QALYs.
METHODS
• Cancer 2015, a longitudinal prospective population-based
cancer genomic cohort, was utilised in the analysis. [3]
• The EORTC QLQ-C30 was asked at baseline (diagnosis) and
at various follow-up points (3, 6, 12 months).
• The respective algorithms were applied to generate health
state values for the EORTC-8D and the QLU-C10D.
• Cancer-specific baseline values were evaluated and
compared.
• Quality adjusted life-years (QALYs) were estimated and
assessed.
RESULTS
• Complete case analysis of 1,663 patients found that the EORTC-8D and QLU-C10D are highly correlated (0.947), although
the EORTC-8D values at baseline were significantly higher than the QLU-C10D values (0.830 vs 0.736, p<0.001).
• The range in values is different. The minimum value for EORTC-8D is 0.292 while for QLU-C10D it is -0.022. [Figure 1]
• There is strong agreement between the instruments at baseline (ICC=0.770).
• QALYs could be estimated for 1,142 patients. EORTC-8D QALY estimates were significantly higher than QLU-C10D QALYs
(0.911 vs 0.821, p<0.001). [Figure 2]
• Differences in QALY estimates (EORTC-8D QALYs minus QLU-C10D QALYs) appear to be sensitive to the site of the cancer,
but also other disease parameters suggesting different sensitivities. [Table 1]
DISCUSSION
• There is some overlap in the 8 dimensions of the EORTC-
8D (physical functioning, role functioning, pain, emotional
functioning, social functioning, nausea, fatigue and sleep
disturbance, and constipation and diarrhoea) and the 10
dimensions of the QLU-C10D (physical functioning, role
functioning, social functioning, emotional functioning, pain,
fatigue, sleep, appetite, nausea and bowel problems).
• Despite the overlap they produce different baseline values
and importantly QALY estimates.
• There would appear to be differences in the sensitivity of
each instrument (significantly different QALYs are
estimated for Head and Neck and Bone Cancer) and the
responsiveness to identify change (as evidenced by
significant coefficient on ECOG decline, i.e. decline in
functioning).
CONCLUSIONS
• It is well known that generic preference-based measures
often produce different conclusions.
• This analysis confirms that disease-specific preference-
based measures in cancer also suffer from the same
variability, even when drawn from the same quality-of-life
instrument.
• Further research is required to understand the reasons for
the variability, particularly if recommendations for
reimbursement/adoption change in light of using one
instrument over another.
Explanatory Variable Coeff. p-value
Baseline ECOG Limited (reference is Normal) 0.025 0.007
Baseline ECOG Self-care 0.015 0.329
Baseline ECOG Limited Self-care -0.038 0.125
ECOG Declined over time (reference is No change) 0.021 0.034
ECOG Improved over time -0.008 0.552
Curative treatment intention (reference is no treatment) 0.017 0.231
Palliative treatment intention 0.012 0.470
Six month follow-up (reference is 3mth FU) 0.033 0.002
Died (reference is alive at final analysis point) -0.070 0.000
Prostate (reference is Breast) 0.003 0.823
Head & neck 0.029 0.051
Colorectal -0.006 0.657
Lung 0.021 0.148
Bone 0.037 0.042
Cervical 0.024 0.221
Renal -0.011 0.599
Cancer Unknown Primary 0.008 0.756
Oesophagogastric 0.028 0.136
Other site 0.009 0.500
Stage 0 (reference is Stage 1) 0.042 0.248
Stage 2 -0.003 0.806
Stage 3 0.020 0.066
Stage 4 0.010 0.450
Stage 5 0.018 0.326
Unable to stage -0.014 0.294
Note: variables included in the regression by not reported in the table include the difference in baseline utility values,
age, gender, hospital type, insurance status, smoking status
Figure 1: Histogram of baseline utility values Table 1: Regression results for difference in QALY estimates Figure 2: Correlation in QALY estimates
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
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QLU-C10D EORTC-8D
y = 1.0057x + 0.0974
R² = 0.9611
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-0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2
EORTC-8DQALYs
QLU-C10D QALYs
45° line
PCN165