How Glaucoma Patients Assess Different Aspects of Their Treatment? An Elicitation of Patients’ Preferences by Analytic Hierarchy Process (AHP)

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


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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


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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


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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


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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


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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


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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


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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


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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


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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)


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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)


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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)


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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


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Inconsistent judgments

A > B > C > A


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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


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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


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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


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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)


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Results

U > 80 (N = 7)

U < 65 (N = 11)


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Stratified population comparison

AutonomyReading detailsDarkness & GlarePeripheral visionSide effectsTreatment burden



Thank you!

Health & Medicine

How Glaucoma Patients Assess Different Aspects of Their Treatment? An Elicitation of Patients’ Preferences by Analytic Hierarchy Process (AHP)