Comparison of methods and programs for calculating health life...
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Comparison of methods and programs forcalculating health life expectancies
Fiona Matthews, Vikki O’Neill, Carol Jagger
REVES - 28th May 2013
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Outline
I Methods used to calculate health expectancies
I DataI CFAS Data
I Some preliminary resultsI ELECTI IMaChI SPACE
I Future directions...?
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inHALE Workstream on methodology
I Evaluates the different methods for calculating healthy lifeexpectancy from both cross-sectional and longitudinal datasources.
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inHALE Workstream on methodology
I Which methods for cross sectional and longitudinal data aremost robust in the presence of (i) missing data (ii) unequaltime intervals?
I Which hypotheses can be reliably tested using cross sectionalmethodology?
I How large, how often and for how long? Study design issuesfor measuring HLE.
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Implementation
I Collation of methods for longitudinal and cross-sectionalHALE
I Collecting some exemplar datasets to test methods
I Investigate similarities and differences
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Methods used to calculate health expectancies
I Cross-sectionalI The Sullivan method
I Repeated cross-sectionalI The intercensal method
I LongitudinalI Discrete multi-state models
→ IMaCh, SPACE
I Continuous multi-state models
→ ELECT
I Increment decrement life tables
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Methods used to calculate health expectancies
I Cross-sectionalI The Sullivan method
I Repeated cross-sectionalI The intercensal method
I LongitudinalI Discrete multi-state models
→ IMaCh, SPACE
I Continuous multi-state models
→ ELECT
I Increment decrement life tables
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Methods used to calculate health expectancies
I Cross-sectionalI The Sullivan method
I Repeated cross-sectionalI The intercensal method
I LongitudinalI Discrete multi-state models → IMaCh, SPACEI Continuous multi-state models → ELECTI Increment decrement life tables
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Scenarios
Software:
Scenario: IMaCh SPACE ELECT DD Life-table
Rare disorder
X X X
Common disorder
X X X
No recovery
N/A X X
Rare recovery
Acceleration with age
Uneven covariate structure
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Scenarios
Software:
Scenario: IMaCh SPACE ELECT DD Life-table
Rare disorder X X X
Common disorder X X X
No recovery N/A X X
Rare recovery
Acceleration with age
Uneven covariate structure
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Methods to date
I MRC Cognitive Function and Ageing Study
I ScenariosI Cognitive impairment free life expectancyI Disability free life expectancyI Stroke free life expectancy
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MRC Cognitive Function and Ageing study
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CFAS
Data summary:
I Sample size: 13,004I Classifications of Disability:
I State 1: No DisabilityI State 2: Mild to Severe DisabilityI State 3: Death
I Classifications of Cognitive Impairment:I State 1: MMSE 18− 30I State 2: MMSE 0− 17I State 3: Death
For comparing software:
I No missing states at baseline
I No two events in same month
I Data right-censored at 12/2005
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Disability
ELECT:Estimation of Life Expectancies
using Continuous-Time multi-state
models
65 70 75 80 85 90 95 100
0
2
4
6
8
10
12
14
16
Female marginal LEs for age range specified.
Age
Life
Exp
ecta
ncy
(Yea
rs)
Legend
Total LELE in state No DisabilityLE in state Mild−Severe Disability
IMaCh:A maximum likelihood computer
program using Interpolation of
Markov Chains
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Disability
SPACE:Stochastic Population Analysis for Complex Events
65 70 75 80 85 90 95 100
0
2
4
6
8
10
12
14
16
SPACE:Female marginal LEs for age range specified.
Age
Life
Exp
ecta
ncy
(Yea
rs)
Legend
Total LELE in state No DisabilityLE in state Mild−Severe Disability
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Disability
SPACE:Stochastic Population Analysis for Complex Events
65 70 75 80 85 90 95 100
0
2
4
6
8
10
12
14
16
SPACE:Female marginal LEs for age range specified.
Age
Life
Exp
ecta
ncy
(Yea
rs)
Legend
Total LELE in state No DisabilityLE in state Mild−Severe Disability
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Disability: Comparison
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Cognitive Impairment
ELECT:Estimation of Life Expectancies
using Continuous-Time multi-state
models
65 70 75 80 85 90 95 100
0
2
4
6
8
10
12
14
16
Female marginal LEs for age range specified.
Age
Life
Exp
ecta
ncy
(Yea
rs)
Legend
Total LELE in state MMSE 18−30LE in state MMSE 0−17
IMaCh:A maximum likelihood computer
program using Interpolation of
Markov Chains
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Cognitive Impairment
SPACE:Stochastic Population Analysis for Complex Events
65 70 75 80 85 90 95 100
0
2
4
6
8
10
12
14
16
SPACE:Female marginal LEs for age range specified.
Age
Life
Exp
ecta
ncy
(Yea
rs)
Legend
Total LELE in state MMSE 18−30LE in state MMSE 0−17
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Cognitive Impairment
SPACE:Stochastic Population Analysis for Complex Events
65 70 75 80 85 90 95 100
0
2
4
6
8
10
12
14
16
SPACE:Female marginal LEs for age range specified.
Age
Life
Exp
ecta
ncy
(Yea
rs)
Legend
Total LELE in state MMSE 18−30LE in state MMSE 0−17
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Cognitive Impairment: Comparison
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Future software
SoftwareIMaCh
ELECTR SPACE sas
GMSLTBrown& Lynch
LXPCT 2 Weden StataOthers?
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Future
I New datasets to replicate results
I Investigate differences
I New scenarios to test the programs
I Search for other packages
I ? Differences with cross-sectional methods
I Study design issues
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Future scenarios (and data?)
Scenario
Rare disorder CFAS
Commondisorder
CFAS
No recovery CFAS
Accelerationwith age
Rare recovery
Unevencovariatestructure
Others?
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Dissemination
I Ultimately run a workshop at Reves in Edinburgh
I Write a guide on the different methods