1. A Review and Meta- Analysis of Utility Values for Lung Cancer Julie Migrin ASPH Environmental Health Fellow at the U.S. EPA

2. Outline Background: QALYs and lung cancer Illustrate the problem Suggest potential explanations Methods Results and conclusions Next steps

3. QALYs and utilities Quality adjusted life year, QALY, is a measure of health- related quality of life  Incorporates measures of both quantity (years) and quality of life (utility) Utility value (preference score, preference weight)  Ranges from 0 to 1  Indicator of global, health related quality of life  No one way to elicit Uses: Regulatory cost effectiveness analysis, medical decision making ·Assume a policy extends the baseline health profile (white) · The gain in QALYs is shown in gray

4. Lung Cancer 200,000 new U.S. cases each year (CDC data) Cost: $9.6 billion per year in U.S. (CDC data) Environmental/Occupational Causes  Radon  Pesticides  Asbestos  Polycyclic aromatic hydrocarbons (PAHs)  Vinyl Chloride (monomer of PVC)  Arsenic  Chromium What’s Lurking in Your Countertop? July 24, 2008

5. Small Cell Lung Cancer Lung Cancer Types Non Small Cell Lung Cancer

6. Which utility should you use??? There are numerous published utility estimates for lung cancer, spanning a very broad range of values In this study:  Metastatic/Advanced lung cancer Range: 0-0.85  Nonmetastatic/Early stage lung cancer Range: 0.40-1.0  Mixed or unspecified severity lung cancer Range: 0.43-0.76 Results of this variation?

7. Possible explanations for utility variation Upper and lower bound labels Possibilities:  Death----- perfect health  Absence of disease------ normal health  Worst imaginable------ well  Or…. No given bound labels Death Worst imaginable Absence of disease???Normal health??? Well Perfect health

8. Possible explanations for utility variation Elicitation techniques Examples:  Standard gamble  Time trade-off  Rating Scale  Generic index/ Multi- attribute utility index(Health Utilities Index, EQ-5D)  Judgment AttributeLevelDescription SENSATION1Able to see, hear, and speak normally for age. 2Requires equipment to see or hear or speak. 3Sees, hears, or speaks with limitations even with equipment. 4Blind, deaf, or mute. Multi-Attribute Health Status Classification System: Health Utilities Index Mark 2 (HUI2)

9. Possible explanations for utility variation Respondent Examples:  Patients  Physicians or researchers  Family members  Members of the public

10. Findings from the Literature Bound Labels Perfect health < Absence of disease (Fryback and Lawrence, 1997) (King et al., 2003) Normal health < Perfect health Excellent health < Perfect health (Tengs and Lin, 2003) Elicitation Technique Rating Scale < Time tradeoff < Standard Gamble (Read et al., 1984) (Stiggelbout et al., 1996) (Stiggelbout et al., 1994) Standard Gamble < Rating Scale < Time tradeoff (Hornberger et al., 1992) Respondent Nonpatients < Patients (Gabriel et al., 1999) (Boyd et al., 1990) Polsky et al., 2001) (Sackett and Torrance, 1978) (Tengs and Lin, 2002)

11. Study Objectives Objective 1: Provide pooled estimates which reflect the available lung cancer utility literature Objective 2: Determine which methodological factors significantly influence the value of utility for lung cancer

12. Methods Data Collection  Searched PubMed, National Health Service Economic Evaluation Database, and Cost Effectiveness Analysis Registry from the Center for the Evaluation of Value and Risk in Health  Search terms: “lung cancer” + one of the following: “quality of life”, “utility”, “preference score”, “cost- effectiveness”, “cost-utility”, “QALY”  Inclusion criteria: Written in English ≥1 unique utility Noted elicitation technique Noted respondent

13. Methods 23 articles (2 to 45 utilities each) 223 unique utility values Reviewed for 1. Mean or median utility value 2. Measure of variance 3. Number of respondents 4. Type of respondent 5. Elicitation technique 6. Bounds on utility scale 7. Lung cancer type 8. Lung cancer severity

14. Methods Statistical modeling  Meta-regression Using study characteristics (at the group or individual level) as explanatory variables Summary measure of effect (utility) as dependent variable  Hierarchical Linear Model (aka Mixed, Multilevel, Random Effects Models) Nested data structure Violation of assumption of independence Allows addition of group level variables

15. Methods HLM (i=1,...n j individual in j=1,…J groups)  First Level Equation Y ij =b 0j + b 1j X ij + … + ε ij ε ij ~ N (0,σ 2 )  Second level equation b 0j = γ 00 + γ 01 C j + U 0j U 0j ~ N (0,τ 00 ) b 1j = γ 10 (constant slope across groups)  Full model Y ij = (γ 00 + γ 01 C j + U 0j ) + (γ 10 )X ij + ε ij Y ij = γ 00 + γ 01 C j + γ 10 X ij + U 0j + ε ij Group effect Individual Effect Random intercept Individual Level error Individual level model in each group Group specific intercept Group specific slope

16. Methods Level one/observation level variables  Type of respondent  Elicitation technique  Lung cancer severity Level two/study level variables  Lower bound of utility scale  Upper bound of utility scale  Lung cancer type Dummy variables  Categorical predictor variables  Example: two dummies used to represent the three categories of respondent (patient, expert, public)  Full reference case: metastatic, non-small-cell lung cancer (NSCLC), patient, standard gamble method, with scale ranging from death to perfect health Observation weights  Number of respondents / variance of utility value

17. Results. Study Characteristics VariableNumber of utilities (n=223) Percentage of utilities Cancer stage Metastatic12555.6 Nonmetastatic4117.9 Mixed/Not specified 5926.5 Lung cancer type NSCLC14765.0 SCLC114.9 Mixed/Not specified 6730.1 Lower bound Death17577.6 Worst imaginable 20.9 Not stated48 21.5 VariableNumber of utilities (n=223) Percentage of utilities Upper bound Perfect health6126.5 Well / Full health6529.1 Normal health4721.1 Not stated5223.3 Respondent Patient16473.6 Expert5122.9 Public103.6 Elicitation technique Standard Gamble5624.2 Judgment3515.7 Direct rating5424.2 HALex62.7 AQOL3716.6 EQ-5D2511.2 Time trade-off125.4

18. Results Variable Coefficient estimate Standard errorP value Intercept 0.5730.067<.0001 Cancer stage Metastatic 0 Nonmetastatic 0.2520.032<.0001 Mixed/Not specified 0.1990.034<.0001 Lung cancer type NSCLC 0 SCLC -0.2220.0750.0034 Mixed/Not specified -0.2870.0800.0004 Lower bound Death 0 Worst imaginable 0.1090.0620.0835 Not stated -0.0800.2060.6967 Variable Coefficient estimate Standard errorP value Upper bound Perfect health0 Well / Full health0.1440.0450.0016 Normal health-0.0790.0490.1078 Not stated0.0120.2090.9552 Respondent Patient0 Expert0.1490.0740.0459 Public-0.1530.0460.0011 Elicitation technique Standard gamble0 Judgment-0.1130.1810.5337 Direct rating-0.1260.0800.1192 HALex0.1870.0600.0020 AQOL-0.2630.0950.0061 EQ-5D-0.1070.0700.1295 Time trade-off-0.1530.0790.0543

19. Results Variable Coefficient estimate Standard errorP value Intercept 0.5730.067<.0001 Cancer stage Metastatic 0 Nonmetastatic 0.2520.032<.0001 Mixed/Not specified 0.1990.034<.0001 Lung cancer type NSCLC 0 SCLC -0.2220.0750.0034 Mixed/Not specified -0.2870.0800.0004 Lower bound Death 0 Worst imaginable 0.1090.0620.0835 Not stated -0.0800.2060.6967 Pooled estimates (all other variables set to reference values*) Metastatic: 0.573 Nonmetastatic: 0.825 Mixed/ Not specified: 0.772 *Reference: NSCLC, death- perfect health, patient as respondent, standard gamble method

20. Results Variable Coefficient estimate Standard errorP value Intercept 0.5730.067<.0001 Cancer stage Metastatic 0 Nonmetastatic 0.2520.032<.0001 Mixed/Not specified 0.1990.034<.0001 Lung cancer type NSCLC 0 SCLC -0.2220.0750.0034 Mixed/Not specified -0.2870.0800.0004 Lower bound Death 0 Worst imaginable 0.1090.0620.0835 Not stated -0.0800.2060.6967 Lung Cancer Type Small-cell lung cancer: Utility is estimated to be 0.222 lower than for non-small-cell lung cancer Mixed/Not specified: Utility is estimated to be 0.287 lower than for non-small-cell lung cancer

21. Results Variable Coefficient estimate Standard errorP value Upper bound Perfect health0 Well / Full health0.1440.0450.0016 Normal health-0.0790.0490.1078 Not stated0.0120.2090.9552 Respondent Patient0 Expert0.1490.0740.0459 Public-0.1530.0460.0011 Elicitation technique Standard gamble0 Judgment-0.1130.1810.5337 Direct rating-0.1260.0800.1192 HALex0.1870.0600.0020 AQOL-0.2630.0950.0061 EQ-5D-0.1070.0700.1295 Time trade-off-0.1530.0790.0543 Upper Bound Well/full health: Utility is estimated to be 0.144 higher than for those values with perfect health as the upper bound label Respondent Expert: Utility is estimated to be 0.149 higher than for those values with patients as respondents Public: Utility is estimated to be 0.153 lower than for those values with patients as respondents

22. Results Variable Coefficient estimate Standard errorP value Upper bound Perfect health0 Well / Full health0.1440.0450.0016 Normal health-0.0790.0490.1078 Not stated0.0120.2090.9552 Respondent Patient0 Expert0.1490.0740.0459 Public-0.1530.0460.0011 Elicitation technique Standard gamble0 Judgment-0.1130.1810.5337 Direct rating-0.1260.0800.1192 HALex0.1870.0600.0020 AQOL-0.2630.0950.0061 EQ-5D-0.1070.0700.1295 Time trade-off-0.1530.0790.0543 Elicitation technique HALex: Utility is estimated to be 0.187 higher than for those values elicited with the standard gamble method AQOL: Utility is estimated to be 0.263 lower than for those values elicited with the standard gamble method Time trade-off: Utility is estimated to be 0.153 lower than for those values elicited with the standard gamble method

23. Results

24. Limitations  Lack of gray literature  Model predicts values outside standard range of 0-1  No demographic information included  Using weights

25. Conclusions Objective 1: Provide pooled estimates which reflect the available lung cancer utility literature Metastatic lung cancer: 0.573 Nonmetastatic lung cancer: 0.825 Mixed/Not specified lung cancer: 0.772

26. Conclusions Objective 2: Determine which methodological factors significantly influence the value of utility for lung cancer Significant predictors: Lung cancer stage, lung cancer subtype, respondent Near significant: Elicitation method Not significant: Upper and lower bound labels

27. Next steps… Larger meta-analysis looking at 12 common cancers Do different kinds of cancers impact quality of life more of less severely? With a larger sample, which methodological factors significantly impact the value of utility? Which demographic factors significantly impact the value of utility?

28. Thank you!!! Chris Dockins Will Wheeler Colleen Reid

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