Master thesis
‘Psychosocial screening in breast cancer patients:
Validation of the Psychological Distress
Questionnaire-Breast Cancer and predictors of
psychosocial problems’.
Name
Bernice Tonino
ANR
s828169
Organisation
University of Tilburg
Master
Medical Psychology
Supervisors
1. drs. L. van Esch
2. prof. dr. J. de Vries
Date
11-08-2009
Number of words
3919
Abstract
The Psychosocial Distress Questionnaire-Breast Cancer (PDQ-BC) is a screening instrument to
determine psychosocial problems in breast cancer patients. Only based on high scores on one
or several psychosocial aspects patients will be referred to Medical Psychology. One aim of this
study was to evaluate the psychometric properties of the PDQ-BC to determine its usefulness.
Another aim of this research was, by using the PDQ-BC, to define the criteria that determine
which women could be considered at risk for psychosocial problems and therefore should be
referred to Medical Psychology.
Breast cancer patients who receive adjuvant chemotherapy in the Máxima Medical Centre
Veldhoven and Eindhoven were approached to participate in the study. They were asked to
complete several self-report questionnaires, including the PDQ-BC, at three predefined
measurements: before the start of chemotherapy, in the days before finishing chemotherapy
and three months after finishing chemotherapy. Of 35 participants who agreed to participate, 21
participants (60%) dropped out during the study. All three questionnaires were completed by 9
participants (25.7%).
The results indicated a reliable and valid instrument. Test-retest reliability analyses showed
significant correlations for all subscales except depressive symptoms. All correlations were
classified as good or excellent. With respect to convergent validity, the PDQ-BC appeared to be
valid for the subscales body-image, depressive symptoms and state anxiety. With respect to
divergent validity, the PDQ-BC appeared to be valid for the subscales body-image, depressive
symptoms, financial problems, sexual problems, social problems, state anxiety and support.
Furthermore, results indicated that younger age, having children living at home and having
employment were not significant predictors of referral to Medical Psychology because of
psychosocial problems. However, it is possible that the effect of age becomes apparent in the
months after completion of adjuvant chemotherapy and therefore was not found in the current
study.
In the future, the PDQ-BC can and should be used to determine psychosocial problems and
refer breast cancer patients to Medical Psychology when indicated. Further research is
encouraged to assess the predictors of psychosocial problems, using the PDQ-BC.
2
Introduction
The prevalence of cancer in the Netherlands is estimated at 400.000 people. That is
approximately 2.5 percent of the Dutch population. The prevalence of breast cancer is the
highest, a quarter of the total prevalence (1). The diagnosis of cancer affects multiple aspects of
people’s lives (2). Besides physical consequences, the Health Council of The Netherlands
concludes that severe problems can occur in a psychological area (3). Therefore, high quality
oncological care also focuses on preventing psychosocial problems, as far as possible, by
pointing them out in time and offering help where necessary. This help can consist of referral to
the departments of Psychiatry, Medical Psychology or Medical Social Work within the hospital.
Pointing out psychosocial problems means that the complaints should be systematically and
structurally determined at multiple times in the treatment trajectory and in the recovery phase
afterwards. Only then a good referral is possible and will every patient receive suitable
psychosocial care (4). Therefore, a screening instrument to determine psychosocial problems in
cancer patients is indicated.
Based on existing literature, the St. Elisabeth hospital in Tilburg developed such a screening
instrument for breast cancer patients, named: ‘Psychosocial Distress Questionnaire-Breast
Cancer’ (PDQ-BC). This screening instrument concerns all psychosocial aspects of importance
to breast cancer patients. Based on the scores patients will be referred to the departments of
Psychiatry, Medical Psychology or Medical Social Work within the hospital. Only based on high
scores on one or several psychosocial aspects, patients will be referred to Medical Psychology
(4). To determine the usefulness of the PDQ-BC, one aim of this study was to evaluate its
psychometric properties in the target population it was developed for.
To assess the clinical value of the screening instrument, another aim of this study was to define
the criteria that determine which women could be considered at risk for psychosocial problems
and therefore should be referred to Medical Psychology, by using the PDQ-BC. Previous
research has shown that certain demographic characteristics, such as age, are associated with
how well individuals adjust to having cancer (2). Even though the incidence of cancer increases
with age (5), it seems that older patients are likely to be less distressed by the diagnosis of
cancer than younger patients (6).The review of Mor et al. (7) confirms this, showing that older
persons with cancer manifest fewer and less severe psychosocial problems than younger
persons. In the study of Parker et al. (2) older patients reported less anxiety, less depressive
symptoms and better overall quality of life (QoL) in the mental health domain than younger
3
cancer patients. The association between age and QoL remained after controlling for other
demographic and medical characteristics and for social support.
In breast cancer patients the same association is found. In their study, Mor et al. (7) found that
older breast cancer patients were calmer and happier than their younger counterparts. Older
age enhanced emotional well-being and older women appeared less depressed than younger
women. The researchers argue that economic and social factors play a role in the observed age
differences. Younger impaired patients face multiple responsibilities in life; such as having
employment and raising children. In contrast, older patients often have accomplished these
tasks (7). Another study suggests that younger women with breast cancer manifest greater QoL
disruption when compared with their older counterparts (8). This concerns global QoL,
emotional well-being, breast carcinoma specific concerns, and symptoms of depression and
disease specific intrusive thoughts.
The study of Broeckel et al. (9) evaluated the QoL of breast cancer patients previously treated
with adjuvant chemotherapy. Younger age was positively related to poorer mental well-being
and greater depressive symptomatology and accounted for eight and six percent of the
variability respectively. Another study found that younger women faired worse on a broad range
of QoL dimensions such as global QoL, psychical functioning, social functioning, emotional
functioning and pain. However, there did not appear to be an age effect among women who had
adjuvant chemotherapy (10).
To evaluate the psychometric properties of the screening instrument PDQ-BC, this study
conducted a test-retest reliability analysis and a validity analysis with respect to convergent and
divergent validity. The research question was: ‘What are the psychometric properties of the
PDQ-BC in breast cancer patients that receive adjuvant chemotherapy?’ The study
hypothesized that the test-retest reliability and the convergent and divergent validity of the PDQBC are good.
Because of the inconsistency in previous research concerning the role of age in breast cancer
patients treated with adjuvant chemotherapy, this study examined the effect of age on
psychosocial problems particularly in that population. The research question was the following:
‘What are the predictors of referral to Medical Psychology because of psychosocial problems in
breast cancer patients that receive adjuvant chemotherapy?’ It hypothesized that younger age,
having children living at home and having employment are predictors of referral to Medical
Psychology.
4
Methods
Patients
All breast cancer patients who receive adjuvant chemotherapy in the Máxima Medical Centre
Veldhoven and Eindhoven were approached to participate in the study. They were asked to
complete a set of self-report questionnaires. Exclusion criteria were insufficient knowledge of
the Dutch language and a psychiatric disorder (11).
Measures
Participants were asked to complete several self-report questionnaires, including the
Psychosocial Distress Questionnaire-Breast Cancer (PDQ-BC), the European Organisation for
Research and Treatment of Cancer Quality of Life Questionnaire-Breast Cancer (EORTC QLQBR23) and the Hospital Anxiety and Depression Scale (HADS).
The PDQ-BC consists of 35 items on body-image, depressive symptoms, financial problems,
physical problems, sexual problems, social problems, support and on state and trait anxiety.
Little is known about the psychometric properties of the PDQ-BC. The reliability measured by
Cronbach’s alpha coefficient appears to be good (12).
The EORTC QLQ-BR23 is a disease specific questionnaire that consists of 26 items. It
incorporates systemic therapy side effects, arm symptoms, breast symptoms, body-image,
sexual functioning, sexual enjoyment, hair loss and future perspectives (13). The reliability and
validity of the questionnaire are good (14). The EORTC QLQ-BR23 was completed to validate
the items of the PDQ-BC on body-image and sexual problems.
The HADS consists of 14 items on anxiety and depression (15). The questionnaire is developed
to detect anxiety and depression, independent from the somatic symptoms. The reliability and
validity of the questionnaire are good (16). The HADS was completed to validate items of the
PDQ-BC on anxiety and depressive symptoms.
Procedure
During their first visit to the nurse practitioner, patients received information about the study. If
they gave their consent to participate in the study, the first set of questionnaires was given to
them. If patients did not want to participate, their data was put on a non-participation list with
their permission. Participants received the questionnaires from the nurse practitioner at three
predefined moments of measurement. The first measurement took place before the start of the
5
chemotherapy. The second measurement took place in the days before finishing chemotherapy.
The third and last measurement took place three months after finishing chemotherapy.
Participants had to complete the questionnaires before their next visit to the nurse practitioner.
After every measurement, the nurse practitioner discussed the results of the PDQ-BC with the
participants. Only based on high scores on one or several subscales of the PDQ-BC,
participants were referred to the department of Medical Psychology (11).
Statistical procedure
Psychometric properties
To determine the test-retest reliability of the PDQ-BC in breast cancer patients that receive
adjuvant chemotherapy, psychosocial problems were measured several moments in the same
sample. The test-retest reliability analysis was done between the scores on the second and third
measurement using SPSS 13.0. The time between these measurements was three months,
which was comparable for all participants. Those participants who were not referred to Medical
Psychology due to their scores on the PDQ-BC at the two measurements were selected, so that
the sample did not or not sufficiently suffer from psychosocial problems throughout time. An
Intraclass correlation coefficient (ICC) was used in this test-retest reliability analysis. A high
correlation between the subscales of the PDQ-BC on the two measurements would indicate a
reliable instrument. According to Fleiss, an ICC above .75 is excellent, between .40 and .75 is
good and below .40 is bad (17). Expected was a significant ICC higher than .40 for each
analysis.
To assess the convergent and divergent validity of the PDQ-BC in breast cancer patients that
receive adjuvant chemotherapy, the correlations between subscales of the PDQ-BC and
subscales of other questionnaires were assessed. For this purpose, the Pearson correlation
coefficient (r) was used. The analyses were done for each measurement using SPSS 13.0.
‘Convergent validity’ assesses the correlation between the total scores on the subscales of the
PDQ-BC and total scores on comparable subscales of other questionnaires. If the scores on the
comparable subscales correlate highly with the scores on the PDQ-BC subscales, the PDQ-BC
appears to be valid. If the scores do not correlate highly, the PDQ-BC is considered to be less
valid. This study compared the subscales body-image, depressive symptoms, sexual problems
and state anxiety of the PDQ-BC with comparable subscales of the HADS and the EORTC
QLQ-BR23. Significant correlations with large effects (r ≥ .5) were expected.
6
‘Divergent validity’ assesses the correlation between the total scores on the subscales of the
PDQ-BC and total scores on different subscales of other questionnaires. If the scores on the
different subscales do not correlate highly with the scores on the subscales of the PDQ-BC, the
PDQ-BC appears to be more valid. If the scores correlate highly, the PDQ-BC is considered to
be less valid. This study compared the subscales body-image, depressive symptoms, financial
problems, physical problems, sexual problems, social problems, support and state anxiety of the
PDQ-BC with different subscales of the HADS and the EORTC QLQ-BR23. Non significant
correlations with small effects (r < .3) were expected.
Predictors
To assess the hypothesis whether younger age, having children living at home and having
employment are predictors of referral to Medical Psychology, univariate and multivariate logistic
regression analyses were done separately for each measurement using SPSS 13.0. The ‘Enter’
method was used to include the predictors. Expected was that younger age, having children
living at home and having employment would affect the predictive power of the model and would
be predictors of referral to Medical Psychology.
For the significant model, standardized residuals were checked to look for cases that might be
influencing the logistic regression model. Few signs of influential cases were expected.
7
Results
Demographic and medical characteristics of the sample
Data collection was conducted between January 2008 and June 2009. This study approached
35 patients to participate in the study. In Eindhoven 5 participants entered the study (14.3%)
and in Veldhoven 30 participants entered (85.7%). Of the 35 patients who gave their consent,
30 participants (85.7%) completed and returned the first questionnaire and 9 participants
completed all three questionnaires (25.7%). In total 21 participants (60%) dropped out of the
study. Motives not to return the questionnaires were practical, such as lack of time. Also, not all
participants received the questionnaires in time due to logistic reasons. Therefore, these could
not be included in the study.
The demographic and medical characteristics of the sample are summarized in Table 1. The
age range of participants was 37-69 years with a mean of 53 years (SD = 9). Half of the
participants were living with their partner and children (50.0%) and most participants were not
employed (56.7%). Most participants underwent a breast saving operation (63.6%) and were
having a combination of chemotherapy with one or more other treatments (70.0%).
Psychosocial characteristics of the total sample are summarized in Table 2. Trait anxiety was
assessed only at measurement one. The mean score was 17.40 (SD = 3.18) and varied from 11
to 23.
Psychometric properties
Table 3 shows the test-retest reliability analysis done between the scores on the second and
third measurement. Significant results were found on all subscales except depressive symptoms
(ICC = .47; p (one-tailed) > .05). The correlations on the subscales physical problems (ICC =
.86), sexual problems (ICC = .88), social problems (ICC = .80) state anxiety (ICC = .80) and
support (ICC = .77) were classified as excellent. Correlations on all other subscales could be
classified as good.
Tables 4 and 5 show the Pearson correlations between subscales of the PDQ-BC and
subscales of other questionnaires. With respect to convergent validity (Table 4), the PDQ-BC
appeared to be valid at measurements one and two for the subscales body-image, depressive
symptoms and state anxiety. At measurement one, there were significant correlations with large
effects between the subscales state anxiety (r = .58), depressive symptoms (r = .74) and bodyimage (r = .76) of the PDQ-BC and comparable subscales. There was one non significant
8
correlation with small effect (sexual problems r = -.21). At measurement two, a similar pattern of
results was obtained. At measurement three, there were significant correlations with large effect
between the subscales body-image (r = .87) and sexual problems (r = -.71) of the PDQ-BC and
comparable subscales of the EORTC QLQ-BR23. Other correlations had large or medium
effects, but were not significant.
With respect to divergent validity (Table 5), the PDQ-BC appeared to be valid at measurements
one and two. The subscales body-image, financial problems, sexual problems, social problems
and support of the PDQ-BC had multiple non significant correlations with small or medium effect
sizes (r < .5) compared with different subscales. At measurements two and three, the subscales
depressive symptoms and state anxiety had multiple correlations with small or medium effect
sizes (r < .5) when compared to the subscales of the EORTC QLQ-BR23. At measurement
three, the subscale body-image (r < .3) appeared to be valid as well.
Predictors
To assess the hypothesis whether younger age, having children living at home and having
employment are predictors of referral to Medical Psychology, univariate and multivariate logistic
regression analyses were done (Tables 6 and 7). For measurement one, univariate analyses
showed that younger age (Wald statistic = 0.38; p > .05), having children living at home (Wald
statistic = 0.15; p > .05) and having employment (Wald statistic = 3.42; p > .05) were not
significant predictors of referral to Medical Psychology. The model including all predictors did
not significantly predict the outcome better than the most basic model with only the constant
2
included (model x = 5.39; p > .05). In this model, none of the predictors were significant.
For measurement two, the influence of only one predictor could be assessed because of the
small sample size (N = 18). Univariate analyses showed that younger age (Wald statistic = 0.58;
p > .05), having children living at home (Wald statistic = 1.52; p > .05) and having employment
(Wald statistic = 0.06; p > .05) were not significant predictors of referral to Medical Psychology.
For measurement three, logistic analyses could not be performed because of the small sample
size (N = 9). Therefore, no results are known for this measurement.
9
Discussion
Psychometric properties
To determine the usefulness of the screening instrument PDQ-BC, one aim of this research was
to evaluate its psychometric properties. Therefore, this study conducted a test-retest reliability
analysis and a validity analysis with respect to convergent and divergent validity. The study
hypothesized that these psychometric properties of the PDQ-BC are good.
Results indicated a reliable instrument. The test-retest reliability analysis showed significant
correlations for all subscales except depressive symptoms. All correlations were classified as
good or excellent. Also, the instrument appeared to be valid at measurements one and two.
With respect to convergent validity, the PDQ-BC appeared to be valid for the subscales bodyimage, depressive symptoms and state anxiety. With respect to divergent validity, the PDQ-BC
appeared to be valid for the subscales body-image, depressive symptoms, financial problems,
sexual problems, social problems, state anxiety and support.
Previous research has shown good reliability measured by a Cronbach’s alfa of .84 (12).
Together with the current results, it suggests a good reliability of the PDQ-BC. The current study
also showed a good convergent and divergent validity for measurements one and two. As in the
current study, previous research (12) found relatively high correlations between total scores on
the subscales body-image, depression and anxiety and total scores on comparable subscales of
the HADS and EORTC QLQ-BR23 and most of these correlations were significant. It also found
relatively low correlations between total scores on the subscales financial problems, social
problems and support of the PDQ-BC and total scores on different subscales of the HADS and
EORTC QLQ-BR23. The current study added low correlations for the subscales body-image
and sexual problems. Both studies suggest good convergent and divergent validity for the same
subscales of the PDQ-BC.
In the divergent validity analysis, significant correlations with large effect sizes were found
between the subscale depressive symptoms of the PDQ-BC and the subscale anxiety of the
HADS as well as the subscale state anxiety of the PDQ-BC and the subscale depression of the
HADS. This showed the known association between anxiety and depression, which is in
accordance with previous research. For example, Gurney et al. (18) found that depressive
disorders and anxiety disorders co-occur. This study showed that the association between
anxiety and depression also occurs in breast cancer patients that receive adjuvant
chemotherapy.
10
Predictors
To assess the clinical value of the screening instrument, another aim of this research was to
define the criteria that predict which women could be considered at risk for referral to Medical
Psychology because of psychosocial problems, by using the PDQ-BC. It hypothesized that
younger age, having children living at home and having employment are such predictors.
Results indicated that, for measurement one and two, younger age, having children living at
home and having employment were not significant predictors of referral to Medical Psychology
because of psychosocial problems.
The current study did not find younger age to be a predictor. Previous research has shown that
younger age is associated with developing psychosocial problems in cancer patients (2, 6, 7),
including breast cancer patients (7, 8). Results are mixed concerning breast cancer patients that
receive adjuvant chemotherapy. In the study of King et al. (10), younger women faired worse on
a broad range of QoL dimensions. However, there did not appear to be an age effect among
women who had adjuvant chemotherapy. In another study (9), younger age was positively
related to poorer mental well-being in breast cancer patient previously treated with adjuvant
chemotherapy. In this particular study, questionnaires were completed between three and 36
months following the completion of adjuvant chemotherapy, as opposed to the study of King et
al. (10) and the current study in which a large portion of the questionnaires were completed
during adjuvant chemotherapy. Even though the two latter studies did not find younger age to
be a predictor, it is possible that the effect of age becomes apparent in the months after
completion of adjuvant chemotherapy in stead of during chemotherapy. This could be an
explanation why the effect of age on referral to Medical Psychology because of psychosocial
problems was not found in the current study.
Limitations of the study
One limitation of the current study was its small sample size. The aim was to approach all
breast cancer patients who receive adjuvant chemotherapy in the Máxima Medical Centre for
this study. In the Máxima Medical Centre Eindhoven another study took place that had priority
over the current study. Therefore, mainly breast cancer patients in the Máxima Medical Centre
Veldhoven were approached. This limited the sample size and therefore the power of this study
and the generalisability of its results. Furthermore, the percentage of participants dropping out
of the study was 60%. This limited its power even more.
11
Although this study showed a good to excellent test-retest reliability, these results should be
interpretated with caution. To obtain a comparable amount of time between the measurements,
the test-retest reliability analysis was done between the second and third measurement. The
amount of time in between these measurements was three months. The possibility that events
took place that might influence the psychosocial state of participants could not be ruled out. For
example, around the second measurement participants received adjuvant chemotherapy, which
they did not at the third measurement. Therefore, there might have been an effect of the
moment of measurement on the test-retest reliability analysis. Another effect of this selection
was the reduction in sample size, because only participants that had completed the
questionnaires at all three measurements could be included in the analysis. Furthermore, those
participants who were not referred to Medical Psychology at the two measurements were
selected, so that the sample did not or not sufficiently suffer from psychosocial problems
throughout time. This reduced the sample size even more to a total of eight participants.
Conclusion
In conclusion, results indicated that the test-retest reliability and the convergent and divergent
validity of the PDQ-BC are good. Even though some of these results should be interpretated
with caution, together with previous research this study showed that the PDQ-BC is a reliable
and valid screening instrument. In the future, the PDQ-BC can and should be used to determine
psychosocial problems in breast cancer patients and refer them to Medical Psychology when
indicated. Furthermore, results of this study indicated that younger age, having children living at
home and having employment were not predictors of referral to Medical Psychology because of
psychosocial problems. However, it is possible that the effect of age becomes apparent in the
months after completion of adjuvant chemotherapy and therefore was not found in the current
study. This study encourages further research to assess the predictors of psychosocial
problems, using the PDQ-BC.
12
References
1
http://www.ikcnet.nl/page.php?id=1875&nav_id=114. Retrieved 2008.
2
Parker PA, Baile WF, De Moor C, & Cohen L. Psychosocial and demographic predictors
of quality of life in a large sample of cancer patients. Psychooncology 2003;12:183-93.
3
Health Council of the Netherlands. Nacontrole in de oncologie. Doelen onderscheiden,
inhoud onderbouwen. 2007.
4
Saint Elisabeth Hospital. Psychosocial screening in breast cancer patients: Validation of
the Psychological Distress Questionnaire-Breast Cancer (PDQ-BC). Research protocol
2007.
5
http://www.ikcnet.nl/page.php?id=1869&nav_id=114. Retrieved 2008.
6
Walker LG, Kohler CRD, Heys SD, & Eremin O. Psychosocial aspects of cancer in the
elderly. Eur J Surg Oncol 1998;24:375-8.
7
Mor V, Allen S, & Malin M. The psychosocial impact of cancer on older versus younger
patients and their families. Cancer 1994;74:2118-27.
8
Wenzel LB, Fairclough DL, Brady MJ, Cella D, Garrett KM, Kluhsman BC, Crane LA, &
Marcus AC. Age-related differences in the quality of life of breast carcinoma patients
after treatment. Cancer 1999;86:1768-74.
9
Broeckel JA, Jacobsen PB, Balducci L, Horton J & Lyman GH. Quality of life after
adjuvant chemotherapy for breast cancer. Breast Cancer Res Treat 2000;62:141-50.
10
King MT, Kenny P, Shiell A, Hall J, & Boyages J. Quality of life three months and one
year after first treatment for early stage breast cancer: influence of treatment and patient
characteristics. Qual life Res 2001; 9:789-800.
11
Máxima Medical Centre. Psychosocial screening in breast cancer patients: Validation of
the Psychological Distress Questionnaire-Breast Cancer (PDQ-BC). Research protocol
2007.
12
Smallegange C. Screening for psychosocial distress in breast cancer patients: a
validation of the Psychosocial Distress Questionnaire-Breast Cancer (PDQ-BC).
Masterthesis 2008.
13
EORTC. EORTC QLQ-C30 Scoring Manual. Third edition 2001.
14
Aaronson NK, Ahmedzai S, Bergman B, et al. The European Organization for Research
and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international
clinical trials in oncology. J Natl Cancer Inst 1993;85:365-76.
13
15
Snaith RP Commentary: The Hospital Depression and Anxiety Scale. Health Qual Life
Outcomes 2003;1:29.
16
Bjelland I, Dahl AA, Haug TT, & Neckelman D. The validity of the Hospital Depression
and Anxiety Scale: An updated literature review. J Psychosom Res 2001;52:69-77.
17
Fleiss JL. Design and Analysis of Clinical Experiments. New York: John Wiley & Sons
1986.
18
Gurney C, Roth M, Garside RF, Kerr TA, Schapira K. Studies in the classification of
affective disorders: the relationship between anxiety states and depressive illness. Br J
Psychiatry 1972;121:162-166.
14
Appendices
15
Table 1 Demographic and medical characteristics of the sample
Variables
Participants (n=30)
Demographic variables
Mean Age (SD)
53(9)
Age min. – max.
37-69
Partner
-
yes (%)
90.0
-
no (%)
10.0
Children
-
yes (%)
90.0
-
no (%)
10.0
Having children living at home
-
yes (%)
56.7
-
no (%)
43.3
Having employment
-
yes (%)
40.0
-
no (%)
56.7
-
unknown (%)
3.3
Medical characteristics
Operation
-
breast saving (%)
63.6
-
breast amputation (%)
23.4
-
unknown (%)
13.3
Treatment
-
chemotherapy only (%)
20.0
-
chemotherapy in combination with
70.0
other treatments (%)
-
unknown (%)
10.0
Previous illness
-
yes (%)
23.3
-
no (%)
76.7
16
Table 2 Psychosocial characteristics of the sample
Variables
Measurement 1 (N=30)
Measurement 2 (N=18)
Measurement 3 (N=9)
Mean (SD)
min - max
Mean (SD)
min - max
Mean (SD)
min - max
Body-image
2.97 (0.97)
2-5
3.11 (1.32)
1-6
3.56 (1.42)
2-6
Depressive
10.03 (3.42)
1-18
10.61 (3.29)
6-18
10.44 (2.40)
7-14
Financial problems
1.40 (0.72)
1-4
1.39 (0.70)
1-3
1.33 (0.50)
1-2
Physical problems
6.83 (2.20)
4-12
7.33 (2.35)
4-12
6.56 (3.17)
4-13
Sexual problems
1.34 (0.72)
1-4
1.41 (0.62)
1-3
1.50 (0.76)
1-3
Social problems
4.40 (1.30)
3-8
4.56 (1.89)
2-9
3.89 (1.27)
2-6
State anxiety
10.93 (2.92)
6-17
10.50 (2.75)
6-17
10.11 (2.52)
6-13
Support
1.20 (0.41)
1-2
1.39 (0.50)
1-2
1.44 (0.53)
1-2
Trait anxiety
17.40 (3.18)
11-23
-
-
-
-
Body-image
6.32 (2.34)
4-13
6.39 (2.79)
4-13
7.33 (2.87)
4-12
Sexual functioning
4.22 (1.76)
2-10
3.50 (1.41)
2-6
4.50 (1.07)
3-6
Anxiety
4.73 (3.44)
0-13
3.72 (3.48)
0-11
3.44 (3.05)
0-9
Depression
3.10 (3.59)
0-14
3.61 (3.45)
0-13
1.67 (2.29)
0-6
PDQ-BC
symptoms
EORTC QLQ-BR23
HADS
Table 3 Test-retest reliability analyses
Subscale PDQ-BC
Intraclass correlation
Classification ICC
coefficient (ICC)
Body-image
.67*
Good
Depressive symptoms
.47
Good
Financial problems
.74**
Good
Physical problems
.86**
Excellent
Sexual problems
.88**
Excellent
Social problems
.80**
Excellent
State anxiety
.80**
Excellent
Support
.77**
Excellent
*p < .05, **p < .01 and ***p < .001
Table 4 Convergent validity
PDQ-BC subscales
Test statistics
Measurement1
N=30
Body-image
R / Effect size
Depressive symptoms
R / Effect size
Sexual problems
R / Effect size
State anxiety
R / Effect size
Measurement2
N=18
Body-image
R / Effect size
Depressive symptoms
R / Effect size
Sexual problems
R / Effect size
State anxiety
R / Effect size
Measurement3
N=9
Body-image
R / Effect size
Depressive symptoms
R / Effect size
Sexual problems
R / Effect size
State anxiety
R / Effect size
*p < .05, **p < .01 and ***p < .001
EORTC BI
EORTC SF
HADS A
HADS DE
.76** / Large
.74** / Large
-.21 / Small
.58** / Large
.93** / Large
.80** / Large
-.11 / Small
.77** / Large
.87** / Large
.39 / Medium
-.71* / Large
.50 / Large
Table 5 Divergent validity
PDQ-BC subscales
Test statistics
EORTC BI
EORTC SF
HADS A
HADS DE
Measurement1
N=30
Body-image
R / Effect size
-
-.02 / Small
.27 / Small
.22 / Small
Depressive symptoms
R / Effect size
.44** / Medium
-.12 / Small
.59** / Large
-
Financial problems
R / Effect size
.10 / Small
.10 / Small
.10 / Small
.33* / Medium
Physical problems
R / Effect size
.34* / Medium
-.07 / Small
.38* / Medium
.64** / Large
Sexual problems
R / Effect size
.43* / Medium
-
.10 / Small
.08 / Small
Social problems
R / Effect size
.23 / Small
.17 / Small
.20 / Small
.44** / Medium
State anxiety
R / Effect size
.38* / Medium
-.07 / Small
-
.75** / Large
Support
R / Effect size
-.19 / Small
.03 / Small
.24 / Small
.29 / Small
Measurement2
N=18
Body-image
R / Effect size
-
.09 / Small
.26 / Small
-.05 / Small
Depressive symptoms
R / Effect size
.12 / Small
-.31 / Medium
.72** / Large
-
Financial problems
R / Effect size
.04 / Small
.36 / Medium
.12 / Small
.14 / Small
Physical problems
R / Effect size
-.33 / Medium
-.31 / Medium
.12 / Small
.50* / Large
Sexual problems
R / Effect size
.84** / Large
-
.26 / Small
-.07 / Small
Social problems
R / Effect size
-.23 / Small
-.12 / Small
.39 / Medium
.55** / Large
State anxiety
R / Effect size
.07 / Small
-.47* / Medium
-
.78** / Large
Support
R / Effect size
.31 / Medium
-.19 / Small
-.07 / Small
-.21 / Small
20
Measurement3
N=9
Body-image
R / Effect size
-
-.27 / Small
.22 / Small
.10 / Small
Depressive symptoms
R / Effect size
.21 / Small
.37 / Medium
.69* / Large
-
Financial problems
R / Effect size
-.26 / Small
.58 / Large
.71* / Large
.22 / Small
Physical problems
R / Effect size
-.30 / Medium
.44 / Medium
.39 / Medium
.80** / Large
Sexual problems
R / Effect size
.56 / Large
-
-.48 / Medium
-.44 / Medium
Social problems
R / Effect size
-.26 / Small
.65* / Large
-.05 / Small
.54 / Large
State anxiety
R / Effect size
.43 / Medium
-.05 / Small
-
.46 / Medium
Support
R / Effect size
.80** / Large
-.75* / Large
-.29 / Small
.04 / Small
*p < .05, **p < .01 and ***p < .001
21
Table 6 univariate logistic regression analyses
Predictor statistics
Variables
B (SE)
Wald statistic
Expβ (95% CI)
(significance level)
Younger age
M1 (n = 30)
0.03 (0.05)
0.38 (p = .54)
1.03 (0.94-1.13)
M2 (n = 18)
-0.05 (0.06)
0.58 (p = .45)
0.95 (0.84-1.08)
M1 (n = 30)
-0.33 (0.85)
0.15 (p = .70)
0.72 (0.14-3.78)
M2 (n = 18)
-1.54 (1.25)
1.52 (p = .22)
0.21 (0.02-2.48)
M1 (n = 30)
2.13 (1.15)
3.42 (p = .07)
8.40 (0.88-80.27)
M2 (n = 18)
0.25 (1.07)
0.06 (p = .81)
1.29 (0.16-10.45)
Having children living at home
Having employment
CI: confidence interval
Table 7 multivariate logistic regression analysis of M1
Model statistics
Variables
Value
-2LL
29,41
Model x
2
5,39 (p = .15)
2
0.15
2
0.16
2
0.24
R (Hosmer & Lemeshow)
R (Cox & Snell)
R (Nagelkerke)
Non-significant predictors: age, having children living at home and having employment.
Predictor statistics
Variables
B (SD)
Wald statistic
Expβ (95% CI)
(significance level)
Younger age
0.04 (.08)
0.29 (p = .59)
1.04 (0.90-1.22)
Having children living at home
-1.03 (1.29)
0.64 (p = .42)
0.36 (0.03-4.44)
Having employment
2.02 (1.20)
2.83 (p = .09)
7.54 (0.72-79.41)
Constant
-4.25 (3.82)
1.24 (p = .27)
0.01
CI: confidence interval
Non-significant predictors: age, having children living at home and having employment.
23