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46
applicable
applicable
Table 4.10 Evaluation of reliability
Box 6. Reliability
Design requirements
O. Argyriou et al.
very goodadequate doubtful inadequate NA
1 Were patients stable in the interim
period on the construct to be measured?
6 For ordinal scores: Was a weighted kappa calculated? Weighted Kappa
7 For ordinal scores: Was the weighting scheme described?
e.g. linear, quadratic
Other
8 Were there any other important flaws in the design or
statistical methods of the study?
Evidence
provided
that
patients
were stabl
calculated
Weighting
scheme
described
No other
important
methodological
flaws
Assumable
that patients
were stable
e
Weighting scheme
NOT described
Unclear if
patients
were stable
Unweighted Kappa
calculated or not
described
Other minor
methodological
flaws
Patients
were NOT
atable
Other
important
methodological
flaws
Not
Not
Box 7. Measurement error
Design requirements
1 Were patients stable in the interim period on the construct to
be measured?
2 Was the time interval appropriate?
3 Were the test conditions similar for the measurements? (e.g.
typeof administration, environment, instructions)
Patients were stable
(evidence provided)
Time interval
appropriate
Test conditions were
similar (evidence
provided)
Assumable that patients
were stable were stable
Assumable that test
conditions were
similar
doubtful Inadequate NAvery good adequate
Unclear if patients
were stable
Doubtful whether
time interval was
appropriate or time
interval was not
stated
Unclear if test
conditions were
similar
Patients were
NOT stable
Time interval
NOT
appropriate
Test conditions
were NOT
similar
Statistical methods
4 For continuous scores: Was the Standard Error of
Measurement(SEM), Smallest Detectable Change (SDC) or
Limits of Agreement (LoA) calculated?
5 For dichotomous/nominal/ordinal scores: Was the percentage
(positive and negative) agreement calculated?
SEM, SDC, or LoA
calculated
% positive and
negative agreement
calculated
Possible to calculate
LoA from the data
presented
% agreement
calculated
SEM calculated
based on
Cronbach’s
alpha, or on SD
from another
population
% agreement
not calculated
Not
applicable
Not
applicable
Other
6 Were there any other important flaws in the design or
statistical methods of the study?
No other important
methodological
flaws
Other minor
methodological flaws
Other
important
methodological
flaws
Caroline B Terwee etal. COSMIN methodology for assessing the content validity of PROMs. Available at: https://www.
cosmin.nl/wp- content/uploads/COSMIN- methodology- for- content- validity- user- manual- v1.pdf
4 Methodology for Systematic Reviews on Measurement Properties of Patient Reported Outcome…
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Table. 4.10 (continued)
47
2 Was the time interval appropriate?
3 Were the test conditions similar for the
measurements? e.g. type of administration,
environment, instructions
Statistical methods
4 For continuous scores: Was
an intraclass correlation
coefficient (ICC) calculated?
5 For dichotomous/nominal/
ordinal scores: Was kappa
calculated?
ICC calculated
and model or
formula of the
ICC is described
Kappa
calculated
Time
interval
appropriate
Test
conditions
were
similar
(evidence
provided)
ICC calculated but
model or formula
of the ICC not
described or not
optimal.
Pearson or
Spearman
correlation
coefficient
calculated with
evidence
provided that no
systematic change
has occurred
Assumable
that test
conditions
were
similar
Doubtful
whether
time interval
was
appropriate
or time
interval was
not stated
Unclear if
test
conditions
were similar
Pearson or
Spearman
correlation
coefficient
calculated
WITHOUT
evidence
provided that
no systematic
change has
occurred or
WITH evidence
that systematic
change has
occurred
Time
interval
NOT
appropriate
Test
conditions
were NOT
similar
No ICC or
Pearson or
Spearman
correlations
calculated
No kappa
calculated
Na
Na
6 For ordinal scores: Was a
weighted kappa calculated?
7 For ordinal scores: Was the
weighting scheme described?
e.g. linear, quadratic
Weighted
Kappa
calculated
Weighting
scheme
described
Weighting
scheme NOT
described
Unweighted
Kappa
calculated or
not described
Na
Na
48
eN
Table 4.11 Assessing risk of bias in a study on measurement error
Box 7. Measurement error
O. Argyriou et al.
Design requirements
1 Were patients stable in the
interim period on the
construct to be measured?
2 Was the time interval
appropriate?
3 Were the test conditions
similar for the
measurements? (e.g. type of
administration, environment,
instructions)
Statistical methods
4 For continuous scores: Was
the Standard Error of
Measurement(SEM), Smallest
Detectable Change (SDC) or
Limits of Agreement (LoA)
calculated?
5 For dichotomous/nominal/
ordinal scores: Was the
percentage (positive and
negative) agreement
calculated?
Patients were
stable
(evidence
provided)
Time interval
appropriate
Test conditions
were similar
(evidence
provided)
SEM, SDC, or
LoA
calculated
% positive
and negative
agreement
calculated
Assumabl
that
patients
were
stable
Assumable
that test
conditions
were
similar
Possible
to
calculate
LoA from
the data
presented
%
agreement
calculated
doubtful Inadequat
e
Unclear if
patients
were stable
Doubtful
whether time
interval was
appropriate
or time
interval was
not stated
Unclear if test
conditions were
similar
Patients were
NOT stable
Time interval
NOT
appropriate
Test
conditions
were NOT
similar
SEM
calculated
based on
Cronbach’s
alpha, or on
SD from
another
population
% agreement
not calculated
Avery good adequate
Not
applicable
Not
applicable
Caroline B Terwee etal. COSMIN methodology for assessing the content validity of PROMs. Available at: https://www.
cosmin.nl/wp- content/uploads/COSMIN- methodology- for- content- validity- user- manual- v1.pdf
eN
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Table 4.12 Assessing risk of bias in a study on criterion validity
Box 8. Criterion validity
49
Statistical methods
1 For continuous scores: Were
correlations, or the area under the
receiver operating curve calculated?
2 For dichotomous scores: Were
sensitivity and specificity
determined?
Other
3 Were there any other important flaws
in the design or statistical methods of
the study?
Correlations
or AUC
calculated
Sensitivity and
specificity
calculated
No other
important
methodological
flaws
doubtful inadequat
Correlation
or AUC NOT
calculated
specificity
NOT
calculated
Other mino
methodological
flaws
r
Other
important
methodological
flaws
Avery good adequate
s
Na
NaSensitivity and
Caroline B Terwee etal. COSMIN methodology for assessing the content validity of PROMs. Available at: https://www.
cosmin.nl/wp- content/uploads/COSMIN- methodology- for- content- validity- user- manual- v1.pdf
50
Table 4.13 Assessing risk of bias in a study on hypotheses testing for construct validity
Box 9. Hypotheses testing for construct validity
9a. Comparison with other outcome measurement instruments (convergent validity)
O. Argyriou et al.
Design
requirements
1 Is it clear what
the comparator
instrument(s)
measure(s)?
2 Were the
measurement
properties of
the comparator
instrument(s)
sufficient?
Statistical methods
3 Were design and
statistical methods
adequate for the
hypotheses to be
tested?
very good
Constructs
measured by the
comparator
instrument(s) is
clear
Sufficient
measurement
properties of the
comparator
instrument(s) in
population
similar to the
study population
Statistical Assumable that Statistical Statistical
methods applied statistical methods methods methods applied
appropriate were appropriate applied NOT
a
adequate doubtful
Sufficient
measurement
properties of the
comparator
instrument(s) but
not sure it these
apply to the study
population
Some
information on
measurement
properties of the
comparator
instrument(s) in
any study
population
optimal
inadequate NA
Constructs
measured by the
comparator
instrument(s) is not
clear
No information on
the measurement
properties of the
comparator
instrument(s), OR
evidence of
insufficient
measurement
properties of the
comparator
instrument(s)
NOT appropriat
e
9b. Comparison between subgroups (discriminative or known-groups validity)
Design requirements
5 Was an adequate
description provided of
important characteristics
of the subgroups?
Statistical methods
6 Were design and statistical
methods adequate for the
hypotheses to be tested?
very good adequate doubtful NA
Adequate
description of
the important
characteristics
of the
subgroups
Statistical
methods
applied
appropriate
Adequate
description of most
of the important
characteristics of
the subgroups
Assumable that Statistical
statistical methods
were appropriate
Poor of no
description of
the important
characteristics
of the
subgroups
s
method
applied NOT
optimal
inadequate
Statistical
methods
applied
NOT
appropriate
Caroline B Terwee etal. COSMIN methodology for assessing the content validity of PROMs. Available at: https://www.
cosmin.nl/wp- content/uploads/COSMIN- methodology- for- content- validity- user- manual- v1.pdf
4 Methodology for Systematic Reviews on Measurement Properties of Patient Reported Outcome…
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Table 4.14 Assessing risk of bias in a study on responsiveness
Box 10. Responsiveness
10a. Criterion approach (i.e. comparison to a gold standard)
51
Statistical methods
1 For continuous scores: Were
correlations between change
scores, or the area under the
Receiver Operator Curve
(ROC) curve calculated?
2 For dichotomous scales: Were
sensitivity and specificity
(changed versus not changed)
determined?
10b. Construct approach (i.e. hypotheses testing; comparison with other outcome measurement
instruments)
Design requirements
4 Is it clear what the
comparator instrument(s)
measure(s)?
5 Were the measurement
properties of the
comparator instrument(s)
sufficient?
very good adequate doubtful NAinadequate
Correlations
or Are under
the ROC Curve
(AUC)
calculated
specificity
calculated
very good adequate doubtful NAinadequate
Constructs
measured by the
comparator
instrument(s) is
clear
Sufficient
measurement
properties of the
comparator
instrument(s) in
a population
similar to the
study population
Sufficient
measurement
properties of the
comparator
instrument(s)
but not sure if
these apply to
the study
population
Some
information on
measurement
properties of
the
comparator
instrument(s)
in any study
population
Correlations
or Are NOT
calculated
Sensitivity and
specificity
NOT
calculated
Constructs
measured by
the
comparator
instrument(s)
is not clear
NO
information on
the
measurement
properties of
the
comparator
instruments(s)
OR evidence of
insufficient
quality of
comparator
instruments(s)
na
naSensitivity and
Statistical methods
6 Were design and statistical
methods adequate for the
hypotheses to be tested?
Other
7 Were there any other
important flaws in the
design or statistical
methods of the study?
Statistical
methods applied
appropriate
No other
important
methodological
flaws
Assumable that
statistical
methods were
appropriate
Statistical
methods
applied NOT
optimal
Other minor
methodological
flaws
Caroline B Terwee etal. COSMIN methodology for assessing the content validity of PROMs. Available at: https://www.
cosmin.nl/wp- content/uploads/COSMIN- methodology- for- content- validity- user- manual- v1.pdf
Statistical
methods
applied NOT
appropriate
Other
important
methodological
flaws
52
Table. 4.14 (continued)
10c. Construct approach: (i.e. hypotheses testing: comparison between subgroups)
O. Argyriou et al.
Design requirements
8 Was an adequate
description provided of
important characteristics
of the subgroups?
Statistical methods
9 Were design and
statistical methods
adequate for the
hypotheses to be tested?
10d. Construct approach: (i.e. hypotheses testing: before and after intervention)
Design requirements
11 Was an adequate
description provided of
the intervention given?
Statistical methods
12 Were design and
statistical methods
adequate for the
hypotheses to be tested?
very good adequate
Adequate
description of the
important
characteristics
of the subgroups
Statistical
methods applied
appropriate
very good adequate
Adequate
description of
the intervention
Statistical
methods applied
appropriate
Adequate
description of
most of the
important
characteristics
of th
subgroups
Assumable that
statistical
methods were
appropriate
Assumable that
statistical
methods were
appropriate
e
doubtful inadequate
Poor or no
description of
the important
characteristics
of the
subgroups
Statistical
methods
applied NOT
optimal
doubtful inadequat
Poor
description of
the
intervention
Statistical
methods
applied NOT
optimal
Statistical
methods
applied NOT
appropriate
NO
description of
the
intervention
Statistical
methods
applied NOT
appropriate
NA
NA
e
*Consider
**It automation tools were used, indicate how many records were excluded by a human and how many were excluded by automaton tools.
4 Methodology for Systematic Reviews on Measurement Properties of Patient Reported Outcome…
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53
Identification of studies via databases and registers
Records removed before
screening:
Records identified from*:
Databases (n = )
Registers (n = )
IdentificationScreeningIncluded
Records screened
(n = )
Reports sought for retrieval
(n = )
Reports assessed for eligibility
(n = )
Studies included in review
(n = )
Reports of included studies
(n = )
, it feasible to do so, reporting the number of records identified from each database or register searcned (rather than the total number across all databases/registers).
Duplicate records removed
(n = )
Records marked as ineligible
by automation tools (n = )
Records removed for other
reasons (n = )
Records excluded**
(n = )
Reports not retrieved
(n = )
Reports excluded:
Reason 1 (n = )
Reason 2 (n = )
Reason 3 (n = )
etc.
Fig. 4.9 PRISMA 2020 ow diagram for new systematic
reviews which included searches of databases, registers
Records identified from:
Websites (n = )
Organisations (n =)
Citation searching (n = )
etc.
Reports sought for retrieval
(n = )
Reports assessed for eligibility
(n = )
statement: an updated guideline for reporting systematic
reviews. BMJ 372, (2021)
Identification of studios via other methods
Reports s not retrieved
(n = )
Reports excluded:
and other sources. Page, M.J. et al. The PRISMA 2020
Reason 1 (n = )
Reason 2 (n = )
Reason 3 (n = )
etc.
Limitations andConsiderations
We have chosen to present the COSMIN methodology as a roadmap for performing systematic
reviews on measurement properties of PROMs,
mainly due to the structured approach and
detailed recommended process.
Researchers that are interested in performing
a systematic review on measurement properties
of PROMs, need to be aware of potential limitations, prior committing to following this
methodology.
On a recent article by McKenna and Heaney,
several points have been raised and we consider it
useful to briey mention them here [12].
According to this, the authors claim that there
is lack of evidence to support the COSMIN recommendations. It is discussed that the guidelines
have been produced based on empirical evidence,
and the experience of the COSMIN steering
committee.
In addition to that, while performing Delphi
studies to agree and produce recommendations in
a scientically robust manner, there may be concerns about the inclusivity of the participating
professionals.
A further point raised, concerns the omission
of several aspects in the assessment of the PROM,
that the authors consider signicant, such as the
construct theories, the fundamental measurements, unidimensionality, item generation and
reduction.
Moreover, it is identied that there has been
no actual evaluation of the COSMIN guidelines
themselves. As an overall concept, the critique
concludes that the COSMIN guidelines and recommendations are not evidence-based.
Lastly, the most signicant point relates to
who utilises and attempts to follow the COSMIN
methodology.
As the vast majority of the researchers performing these reviews are clinicians, and given
the complexity of the COSMIN guidance, it may
be extracted that they lack the necessary expertise and ability to interpret and evaluate the rele-
54
O. Argyriou et al.
vant information, hence producing inaccurate
reviews and recommendations.
Overall, we feel that through this chapter, a
researcher may be introduced to the basics of performing systematic reviews on measurement
properties of PROMs, and the COSMIN methodology and guidelines can be used as they introduce a step-wise approach and thorough
approach.
Nevertheless, the limitations discussed bear
some value—particularly with regards to the
researcher’s expertise and background in the eld.
These should be meticulously taken into account,
and the research team should certainly consider
the involvement of professionals with a strong
background in measurement, psychometrics, statistics and health-related quality of life research.
References
1. Chapter 18: Patient-reported outcomes. Cochrane
training. Available at https://training.cochrane.org/
handbook/archive/v6/chapter- 18. Accessed 4 Oct
2022.
2. COSMIN.Improving the selection of outcome measurement instruments. Available at https://www.cos-
min.nl/. Accessed 4 Oct 2022.
3. Mokkink LB, etal. Evaluation of the methodological
quality of systematic reviews of health status measurement instruments. Qual Life Res. 2009;18:313–33.
4. Mokkink LB, et al. The COSMIN study reached
international consensus on taxonomy, terminology,
and denitions of measurement properties for healthrelated patient-reported outcomes. J Clin Epidemiol.
2010;63:737–45.
5. Prinsen CAC, etal. COSMIN guideline for systematic
reviews of patient-reported outcome measures. Qual
Life Res. 2018;27:1147–57.
6. Mokkink LB, Prinsen CAC, Patrick DL, Alonso J,
Bouter LM, de Vet HCW, Terwee CB.COSMIN manual for systematic reviews of PROMs COSMIN methodology for systematic reviews of Patient-Reported
Outcome Measures (PROMs) user manual. 2018.
7. Terwee CB, etal. COSMIN methodology for assessing the content validity of PROMs. Available at https://
www.cosmin.nl/wp- content/uploads/COSMINmethodology- for- content- validity- user- manual- v1.
pdf. Accessed 4 Oct 2022.
8. Terwee CB, etal. COSMIN methodology for evaluating the content validity of patient-reported outcome measures: a Delphi study. Qual Life Res.
2018;27:1159–70.
9. GRADE Home. Available at https://www.grade-
workinggroup.org/. Accessed 4 Oct 2022.
10. Mokkink LB. COSMIN Risk of Bias checklist
[PDF File]. The Amsterdam Public Health Research
Institute; 2018. p.1–37.
11. Page MJ, et al. The PRISMA 2020 statement: an
updated guideline for reporting systematic reviews.
BMJ. 2021;372:n71.
12. McKenna SP, Heaney A. Setting and maintaining
standards for patient-reported outcome measures: can
we rely on the COSMIN checklists? 2021;24:502–11.
https://doi.org/10.1080/13696998.2021.1907092.
Quality ofLife asEndpoint
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inSurgical Randomised Controlled
Trials
AthinaA.Samara
The quality of life is more important than life itself.
Alexis Carrel, Nobel Prize in Physiology or Medicine in 1912
5
Introduction
Over the past two decades there has been great
scientic progress in medicine and particularly in
surgical oncology. Since the turn of the century,
quality of life (QoL) in surgical patients has been
considered sporadically throughout the literature
as an outcome with secondary importance after
mortality and morbidity [1]. New technologies
and techniques in the operating room have ushered in a new era where cancer surgery has ourished [2]. Furthermore, the emergence of new
chemotherapy drugs and innovative radiotherapy
techniques, together with improved screening
programs allowing for diagnosis of cancer in earlier stages, has enhanced the prognosis of cancer
patients and increased postoperative overall survival [3, 4]. In line with these improvements,
attention has been directed towards Patient
A. A. Samara (*)
Department of Surgery, University Hospital of
Larissa, Larissa, Greece
Reported Outcomes Measures (PROMS) and
Health-related Quality of life (HR-QoL) as a key
outcome measures in the assessment of cancer
treatment efcacy [5, 6]. Dening HR-QoL has
proven quite challenging, with at least four denitions used extensively in the literature [7].
Table5.1 outlines four widely used denitions of
this measure [7–11].
Randomised Controlled Trials (RCTs) are
interventional studies where patients are randomly allocated to one or multiple treatment
groups or a control group (placebo treatment), to
create comparison groups which are as similar as
possible, while reducing the occurrence of systematic bias that may affect the outcome [12].
Theoretically, the only variations between treatment and control groups are the assignment of
treatment and any differences that are identied
[13]. RCTs are described as the “gold standard”
method to evaluate treatment efcacy [14]. The
rst RCT was conducted in 1948 to evaluate the
use of streptomycin for the treatment of tuberculosis [15]. Since then, the highest level of evidence (Level A) is associated with meta-analyses
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
T. Athanasiou et al. (eds.), Patient Reported Outcomes and Quality of Life in Surgery,
https://doi.org/10.1007/978-3-031-27597-5_5
55
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