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

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

Weighting treatment aspects

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
…
Page 2 © Dintsios | HTAi | Bilbao, June 25 th2012 | Glaucoma patients’

AHP

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


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


Background

 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.


Hierarchical Structure of the AHP

Decision Problem
(which car do I want?)

Criterium22
Criterion
Criterium 1
Criterion 1
(e.g. gasoline ... Criteriumnn
Criterion
(e.g. colour)
consumption)

Criterium 1.1 ...

...

Alternative 1 Alternative 2
... Alternative n
(Peugeot 206) (Golf)


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


AHP scale used in pairwise comparisons

How much more important is criterion A in
comparison to criterion B?

9 7 5 3 1 3 5 7 9

A Equal importance B
1 - equally important
3 - slightly more important
5 - more important
7 - much more important
9 - extremely more important


AHP Matrix

AHP matrix of pairwise comparisons

A B C

A 1 1/5 2

B 5 1 6

C 1/2 1/6 1


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


Prerequisites

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


Calculation of the right “Eigenvector” (I)

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:
3 x w1
3 x w2
3 x w3

 This equates to the multiplication of the respective weight
with the number of the compared elements n


Calculation of the right “Eigenvector” (II)

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” (III)

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


Inconsistency

 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


Inconsistent judgments

A>B>C>A


Patient sample

n N = 7 Pretest => Language abilities, Cognition, Proxies
n N = 25 Patients
n Setting: Ophthalmology ambulance at University of Bonn
n Glaucoma patients: different manifestations and severities
n AHP-Questionnaire
n Elicitation of utilities with EQ5D – VAS
n Stratification according to these utilities


Patients’ characteristics

 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%


Assessed aspects

 Reading and seeing detail
 Peripheral vision
 Darkness and glare
 Autonomy subdivided in:
 household chores
 outdoor mobility
 Treatment-related patient’s burden
 Side effects


Example: Questionnaire different side effects


EQ-5D VAS

21

Inconsistency = 0.04

Inconsistency
= 0.00

Inconsistency
= 0.01

Results
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)

Stratified population comparison
Autonomy
Reading details
Darkness & Glare
Peripheral vision
Side effects
Treatment burden

U > 80 (N = 7)
Autonomy
Reading details
Darkness & Glare
Side effects
Treatment burden

U < 65 (N = 11)
Autonomy
Reading details
Darkness & Glare
Side effects
Treatment burden


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

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