Post on 20-Jan-2015
description
Dintsios CM / Scheibler FF / Janssen I / Gerber A / Finger R
HTAi, Bilbao, June 25th 2012
How Glaucoma Patients Assess Different Aspects of Their Treatment?
An Elicitation of Patients’ Preferences by Analytic Hierarchy Process (AHP)
Rationale: Why calculate weights?
Patients weight different treatment aspects according to their preferences Patients’ preferences
These aspects may serve as patient-relevant measures (endpoints) in HTA Patients’ involvement
By weighting treatment aspects prioritization is based on patients’ views Legitimating
In benefit assessments or cost-effectiveness analyses the derived weights can be used for the aggregation of multiple (composite) endpoints Endpoint aggregation
…
Weighting treatment aspects
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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Rationale: Why AHP?
Patient preferences as a basis for weighting treatment aspects
elicit preferences via AHP AHP is one method – others are available, e.g.
Conjoint Ananlysis
AHP
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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Objective of the AHP
To weigh the different aspects of glaucoma treatment by eliciting the preferences of patients with the AHP-procedure.
I.e. to estimate the „relative importance“ of treatment aspects, especially importance to patients
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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The mathematician Thomas L. SAATY developed the AHP procedure in the early 1970th as a technique to solve multicriteria decision problems
How the procedure works: Decision issues are structured hierarchically into
different levels of criteria / alternatives.
Background
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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Hierarchical Structure of the AHP
Decision Problem (which car do I want?)
Criterium 1(e.g. colour)
Criterium 2(e.g. gasoline consumption)
... Criterium n
Criterium 1.1 ...
Alternative 1(Peugeot 206)
Alternative n...Alternative 2
(Golf)
...
Criterion 1Criterion 2
Criterion n
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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How the AHP works
Pairwise comparisons of criteria are used to elicit the relative importance of one criterion in comparison to the others
Mathematical procedure: based on matrices of pairwise comparisons weights are calculated for each criterion with the help of the „Eigenvector“-method
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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AHP scale used in pairwise comparisons
How much more important is criterion A in comparison to criterion B?
13579 3 5 7 9
A BEqual importance
1 - equally important3 - slightly more important5 - more important7 - much more important9 - extremely more important
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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AHP matrix of pairwise comparisons
A B C
B
A
C
1 1/5
1
2
6
1
5
1/2 1/6
AHP Matrix
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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Mathematical conditions on the AHP structure
1) Completeness of criteria a complete set of criteria should be assessed
2) Independence of preference information at the different levels of hierarchy
3) Independence of criteria should be disjunct, exclude each other
4) Scale should be a relative scale preferences measured on a common relative scale
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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The preferences of individuals should correspond to the following prerequisites
1) Reciprocity if A is 3 times more important than B, then B is 1/3 as important
as A
2) Transitivity if A >B and B>C then A>C
3) Consistency resulting from reciprocity and transitivity
Prerequisites
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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Pairwise comparison matrix A; relative weights w1, w2 und w3 of the compared elements are known
Multiply matrix A with the vector W of the weights:
This equates to the multiplication of the respective weight with the number of the compared elements n
3 x w1
3 x w2
3 x w3
Calculation of the right “Eigenvector” (I)
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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This relation is described by the following equation:
A x W = n x W
with A = pairwise comparison matrix,
W = vector of the weights, n = compared elements
In Matrix Algebra: W = right „Eigenvector“ n = Eigen-value of matrix A
In reality W is unknown and has to be approximated by a regression analysis approach
Calculation of the right “Eigenvector” (II)
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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This transforms the equation to:
A* x W* = λmax x W*
with A* = pairwise comparison matrix, W* = right Eigenvector of matrix A, λmax = maximal Eigen-value of matrix A*
Basic assumption of AHP: the calculated right Eigenvector of matrix A equates approximately the vector of the relative weights
Calculation of the right “Eigenvector” (III)
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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Is measured by the so called Consistency Ratio (CR)
Is checking for the „logic“ of the particular pairwise comparison, (i. e. how consistent is the respective pairwise comparison with regard to all the other pairwise comparisons)
According to SAATY a CR ≤ 0,1 is accepted and allows for the conclusion that the weights are derived on a consistent basis
Inconsistency
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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Inconsistent judgments
A > B > C > A
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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N = 7 Pretest => Language abilities, Cognition, Proxies
N = 25 Patients
Setting: Ophthalmology ambulance at University of Bonn
Glaucoma patients: different manifestations and severities
AHP-Questionnaire
Elicitation of utilities with EQ5D – VAS
Stratification according to these utilities
Patient sample
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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N = 25 Study; 68,3±13,3 years; €8 17; first diagnosis 1 – � �21 years
Glaucoma
Primary chronic wide-angle glaucoma 55%
Narrow-angle (congestive) glaucoma 9%
Wide-angle glaucoma with narrow-angle component 9%
Normal-tension glaucoma 9%
Suspected glaucoma 18%
visual acuity bad eye 0,59 ± 0,33
Tensio RE 17,2 ± 6,2; Tensio LE 18,1 ± 4,7
Glaucoma management +/- 82%/18%
Patients’ characteristics
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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Reading and seeing detail
Peripheral vision
Darkness and glare
Autonomy subdivided in:
household chores
outdoor mobility
Treatment-related patient’s burden
Side effects
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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Assessed aspects
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Example: Questionnaire different side effects
21
EQ-5D VAS
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weight, mean, sd, CI95%
Reading and seeing detail 0.229, 0.212 ± 0.123, 0.161 -
0.263
Peripheral vision 0.089, 0.085 ± 0.058, 0.061 -
0.109
Darkness and glare 0.153, 0.165 ± 0.111, 0.119 -
0.211
Autonomy subdivided in: 0.394, 0.371 ± 0.145, 0.311 -
0.431
household chores 0.239, 0.275 ± 0.258,
0.168 - 0.381
outdoor mobility 0.761, 0.725 ± 0.258, 0.619 -
0.832
Treatment-related burden 0.047, 0.052 ± 0.050, 0.027 -
0.076
Side effects 0.088, 0.115 ± 0.131, 0.060 - 0.168
Utilities EQ-5D vs VAS (82.76 ± 22.21 vs. 65.64 ± 19.95, p =
0.003)
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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Results
U > 80 (N = 7)
U < 65 (N = 11)
© Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’ preferences
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Stratified population comparison
AutonomyReading detailsDarkness & GlarePeripheral visionSide effectsTreatment burden
AutonomyReading detailsDarkness & GlarePeripheral visionSide effectsTreatment burden
AutonomyReading detailsDarkness & GlarePeripheral visionSide effectsTreatment burden
Thank you!