Psycho-Oncology Psycho-Oncology 24: 1456–1462 (2015) Published online 6 April 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/pon.3811
Performance status and depressive symptoms as predictors of quality of life in cancer patients. A structural equation modeling analysis Hermann Faller1*, Elmar Brähler2,3, Martin Härter4, Monika Keller5, Holger Schulz4, Karl Wegscheider6, Joachim Weis7, Anna Boehncke7, Matthias Richard1, Susanne Sehner6, Uwe Koch4 and Anja Mehnert2,4 1
Department of Medical Psychology and Psychotherapy, Medical Sociology and Rehabilitation Sciences, and Comprehensive Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany 2 Department of Medical Psychology and Medical Sociology, Section of Psychosocial Oncology, University Medical Center Leipzig, Leipzig, Germany 3 Department of Psychosomatic Medicine and Psychotherapy, University Medical Center Mainz, Mainz, Germany 4 Department and Outpatient Clinic of Medical Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany 5 Division of Psychooncology, Department for Psychosomatic and General Clinical Medicine, University Hospital Heidelberg, Heidelberg, Germany 6 Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany 7 Department of Psychooncology, Tumor Biology Center, University of Freiburg, Freiburg, Germany *Correspondence to: Department of Medical Psychology and Psychotherapy, Medical Sociology and Rehabilitation Sciences, University of Würzburg, Klinikstr. 3, D 97070 Würzburg, Germany. E-mail: [email protected]
Received: 1 October 2014 Revised: 9 February 2015 Accepted: 28 February 2015
Abstract Objective: This study aimed to examine whether depressive symptoms and performance status are independent predictors of both the physical and psychological domains of health-related quality of life (HRQoL) in cancer patients. Methods: A sample of 4020 cancer patients (mean age 58 years, 51% women) was evaluated. Depressive symptoms were measured with the patient health questionnaire and HRQoL with the European Organisation for Research and Treatment of Cancer quality of life questionnaire core 30. The impact of the illness on everyday activities was assessed with physician ratings of both the Karnofsky performance status and the Eastern Cooperative Oncology Group performance status. The simultaneous effects of depression and performance status on quality of life outcomes were estimated using structural equation modeling. Results: Both depressive symptoms and performance status independently predicted the physical and psychological domains of HRQoL. However, the impact of depressive symptoms on the physical HRQoL was stronger than the impact of performance status on the psychological HRQoL. Conclusion: Our results suggest that comorbid depressive symptoms are independently associated with both physical and psychological HRQoL in cancer patients after controlling for the physicianrated performance status. Thus, comorbid depression should be taken into account when evaluating reduced HRQoL in cancer patients. To support a causal impact of depression on HRQoL, intervention studies are needed to show that improving depression enhances cancer patients’ HRQoL. Copyright © 2015 John Wiley & Sons, Ltd.
Background Cancer patients may suffer from both emotional distress [1–4] and impaired quality of life [2,5]. Although previous studies have examined the association between emotional distress and health-related quality of life (HRQoL) [6–16], only scarce evidence exists regarding the question whether reduced HRQoL is primarily a consequence of the severity of the condition or rather of patients’ emotional distress associated with having received a diagnosis of cancer. While both stage of cancer [17,18] and somatic symptoms, such as pain, fatigue, and lymphedema [5], have been linked to reduced HRQoL, the impact of the illness on patients’ activities of daily living may best be grasped by measures of performance status. The performance Copyright © 2015 John Wiley & Sons, Ltd.
status, that is, a patient’s ability to perform everyday activities, is routinely evaluated in oncological care by attending physicians. In contrast to objective measures such as tumor stage, the physician-rated performance status is closer to patients’ subjective health perception, but at the same time not confounded with self-reports of HRQoL because performance status ratings come from a different source, that is, physicians instead of patients. Patientreported somatic symptoms, on the other hand, may partially overlap with HRQoL ratings. We therefore selected the physician-rated performance status to account for the impact of cancer severity on patients’ everyday activities. While studies have found bivariate small-to-medium correlations of both performance status and depressive symptoms with HRQoL [19,20], multivariable predictions of HRQoL have rarely been performed. One study
Performance status, depressive symptoms, and quality of life in cancer
demonstrated that both performance status, as measured by the Karnofsky performance status (KPS) [21], and depressive symptoms independently predicted a global rating of HRQoL [22]. Other studies confirming these associations used various HRQoL dimensions as the dependent variables but examined each individual subscale in turn [23,24]. In the first study, both predictors, that is, performance status as measured by the Eastern Cooperative Oncology Group (ECOG) performance status [25] and depressive symptoms, were not included simultaneously, but sequentially [23]. In the second study, the predictive value of both ECOG performance status and depressive symptoms varied across the HRQoL subscales assessed [24].Thus, a thorough test of the respective impact of performance status and depressive symptoms on HRQoL is still lacking. We therefore conducted a study examining the predictive value of both performance status and depressive symptoms on HRQoL in a large, representative sample of cancer patients. Using structural equation modeling, we aimed to estimate the simultaneous impact of both performance status and depressive symptoms on the physical and psychological domains of HRQoL. Structural equation modeling was used because it allows for a simultaneous analysis of the impact of multiple independent variables (i.e., performance status and depressive symptoms) on several dependent variables (i.e., physical HRQoL and psychological HRQoL). Consequently, a direct comparison of the respective impact of the independent variables on the dependent variables is feasible. In particular, we aimed to clarify whether the psychological and physical domains of HRQoL were differentially affected by depressive symptoms, simultaneously accounting for performance status. Likewise, we examined the effect of performance status on the physical and psychological domains of HRQoL, simultaneously adjusting for depressive symptoms. To avoid contamination of the somatic symptoms of depression with the symptoms of cancer as reflected in both performance status and HRQoL, we focused only on cognitive–affective symptoms of depression.
Methods The methods of the study are described in detail elsewhere [26]. In this multicenter, epidemiological cross-sectional study, cancer patients were enrolled from acute care hospitals, outpatient facilities, and cancer rehabilitation clinics in five study centers in Germany (Freiburg, Hamburg, Heidelberg, Leipzig, and Würzburg).
Study participants Patient inclusion criteria comprised the evidence of a malignant tumor and age between 18 and 75 years. Patients Copyright © 2015 John Wiley & Sons, Ltd.
1457
across all tumor entities and disease stages were included and stratified by nationwide incidence of cancer diagnoses. Patient exclusion criteria comprised the presence of severe physical, cognitive, and/or verbal impairments that would interfere with a patient’s ability to give informed consent. All patients who fulfilled the inclusion criteria were contacted by research assistants and consecutively recruited at the participating institutions. All participants were screened for depressive symptoms employing the patient health questionnaire depression module (PHQ-9) [27] and then asked to provide self-report data that form the basis of this study. A subsample also took part in a structured clinical interview, the results of which are presented elsewhere [28]. The study complied with the Declaration of Helsinki and was approved by the ethics committees of all participating centers [26]. All participants provided written informed consent.
Measures Depressive symptoms were measured using the German version of the PHQ-9 [27,29]. The PHQ-9 compared favorably with other screening instruments when evaluated with diagnostic criteria provided by the Diagnostic and Statistical Manual of Mental Disorders, 4th Revision, as reference standard [29]. Higher values indicate more severe symptoms. We construed a latent variable depressive symptom for use in structural equation modeling. This variable was represented by three items from the PHQ-9. Using at least three items for measuring a latent variable is generally deemed preferable as doing so allows for the identification of the measurement model. The items selected were as follows (abbreviations in brackets): little interest or pleasure in doing things (anhedonia); feeling down, depressed, or hopeless (depressed mood); and feeling bad about yourself or that you are a failure or have let yourself or your family down (worthlessness). These three items include the two core symptoms of major depression that also constitute the two-item version of the PHQ plus an additional item covering a cognitive feature of the depressive syndrome. We only selected cognitive–affective items to measure depressive symptoms. We refrained from selecting the somatic–affective items of the PHQ-9, such as trouble sleeping, feeling tired, change in appetite, trouble concentrating, or feeling slowed down or restless. By selecting only items that do not address bodily complaints, we avoided confounding of the latent variable depressive symptoms with the HRQoL limitations conveyed by the physical status of the patients (see succeeding text). Patients’ HRQoL was assessed using the self-reported cancer-specific European Organisation for Research and Treatment of Cancer quality of life questionnaire core 30 [30]. This instrument is widely used and has a good reliability and validity. Higher values indicate better HRQoL. Psycho-Oncology 24: 1456–1462 (2015) DOI: 10.1002/pon
H. Faller et al.
1458
As with the selection of depressive symptoms described earlier, we carefully examined the subscales for representing the physical and psychological components of HRQoL to avoid confounding between both components. The subscales physical functioning and role functioning were selected as indicators of the latent variable physical HRQoL because they cover restrictions of functioning related to physical performance. In contrast, the subscales emotional functioning, social functioning, and global quality of life cover psychosocial aspects of HRQoL as well as life satisfaction and therefore were selected as indicators of the latent variable psychological HRQoL. We did not select cognitive functioning as indicator of either HRQoL component because it may be associated with both physical and psychological aspects of HRQoL, thus precluding an unambiguous assignment. We did not select the symptom subscales and items of the European Organisation for Research and Treatment of Cancer quality of life questionnaire core 30 as they may rather be considered causes, instead of indicators, of reduced HRQoL. As indicators of performance status, we employed two performance ratings obtained from the attending physicians, namely the KPS [21] and the ECOG performance status [25]. The KPS measures limitations of daily activities and self-care in 10-point steps ranging from 0 = dead to 100 = no limitations. The ECOG performance status measures patients’ abilities to perform activities of daily living using five grades ranging from 0 = fully active to 5 = dead. Both instruments are well established, validated measures of performance status in oncological patients [31,32]. By employing physician ratings, we avoided the danger of confounding by measurement as would have been the case, had both HRQoL and performance status been measured with self-reports. Both KPS and ECOG performance status represented the latent variable ‘performance status’.
used as a fit criterion as the sample size was very large thus producing significant test results even with good model fit [33,34].
Results Sample characteristics Figure 1 shows the study enrolment. A total of 5889 cancer patients fulfilling the inclusion criteria were identified at 30 hospitals, cancer care clinics, and rehabilitation centers in Germany during the survey period. Across these institutions, 84 different oncology departments and hospital wards were included. The response rate was 69.5%, which led to a total of 4091 participants who were screened for depressive symptoms using the PHQ-9. Of these, 4020 provided additional self-report data. Sample characteristics are detailed in Table 1. The mean age of the participants was 58 years (standard deviation (SD) = 11), and about half of them were female. Of the participants, 71% were married. About 70% had received more than basic education. The most frequent tumor sites were breast (23%), digestive organs (20%), and male genital organs (17%). In most patients, the tumors were in complete (41%) or partial (12%) remission. Illness duration amounted to 5 months as a median (interquartile range 2–14). Participants were recruited in acute care hospitals (43%), outpatient units (33%), and rehabilitation clinics (24%). The frequencies of cancer sites varied across treatment settings. Relative to other settings, breast cancer was less frequent in acute care hospitals, cancer of digestive organs in outpatient units, and cancer of respiratory organs in rehabilitation clinics (data not shown). We analyzed differences in age, sex, education, and setting between study participants (4020) and non-
Structural equation modeling A structural equation model was created with two independent latent variables, performance status and depressive symptoms, and two dependent latent variables, the physical and psychological domains of HRQoL. All analyses were carried out using SPSS statistical software version 19 (IBM, Armonk, NY, USA) and MPLUS software (Muthén & Muthén, Los Angeles, CA, USA). To estimate parameters, the maximum-likelihood estimation procedure was applied. Missing data were handled using the full information maximum likelihood estimates. The following goodness-of-fit statistics were computed: comparative fit index (CFI), Tucker–Lewis index (TLI), root-mean-square error of approximation (RMSEA), and standardized root-mean-square residual (SRMR). The chi-squared test was also computed, but it should not be Copyright © 2015 John Wiley & Sons, Ltd.
Figure 1. Patient flow. PHQ, patient health questionnaire Psycho-Oncology 24: 1456–1462 (2015) DOI: 10.1002/pon
participants (1798). Study participants were younger (M = 58.1, SD = 11.3 vs M = 62.0, SD = 10.1) (p < 0.001), had a higher school education (p < 0.001), and were more likely treated in a rehabilitation clinic then during inpatient acute care (p < 0.001). No sex differences were found (p = 0.10).
Structural equation modeling Descriptive statistics as well as the zero-order correlations of the model variables are displayed in Table 2. The structural equation model can be seen in Figure 2 (with standardized estimates), in which rectangles represent observed variables, circles latent variables, single-headed arrows the impact of one variable on another, and double-headed arrows the covariance between pairs of variables. All paths are significant at p < 0.001. The overall goodness-of-fit statistics indicate an acceptable model fit (CFI = 0.95, TLI = 0.93, RMSEA = 0.08, SRMR = 0.04). Because of the large sample size, the chi-squared test showed a significant Copyright © 2015 John Wiley & Sons, Ltd.
1 0.54 1 0.51 0.53
All correlations p < 0.01, with the exception of †, not significant. ECOG-PS, Eastern Cooperative Oncology Group performance status; KPS, Karnofsky performance status; PHQ, patient health questionnaire; EORTC, European Organisation for Research and Treatment of Cancer; SD, standard deviation.
SD, standard deviation.
1 0.42 0.59 0.58
43.2 32.9 23.9
1 0.65 0.39 0.48 0.57
1735 1324 961
4020
1 0.15 0.15 0.35 0.23 0.21
40.7 12.4 17.6 9.3 20.0
1 0.40 0.29 0.31 0.55 0.36 0.39
1581 484 683 361 779
3888
1 0.48 0.26 0.32 0.32 0.40 0.32 0.36
22.5 19.7 16.8 8.9 7.9 7.6 5.5 2.8 2.0 1.8 1.6 2.8
1 0.17 0.18 0.06 0.41 0.28 0.14 0.22 0.28
906 790 677 359 317 305 221 113 80 73 65 114
4020
1 0.82 0.18 0.17 0.02† 0.39 0.26 0.12 0.21 0.27
31.0 29.7 39.0 0.2
0.82 13 0.89 0.75 0.60 24 35 26 33 23
1244 1191 1564 8
4007
0.70 86 0.85 0.67 0.27 70 57 65 61 56
12.2 70.7 10.8 6.3
3859 3855 3984 3944 3972 3820 3796 3814 3808 3804
459 2657 405 238
1459
ECOG performance status Karnofsky performance status PHQ anhedonia PHQ depressed mood PHQ worthlessness EORTC physical functioning EORTC role functioning EORTC emotional functioning EORTC social functioning EORTC global quality of life
% 51.4
KPS
n 2068
ECOG-PS
SD 11
SD
4020 3759
M 58
M
Woman Marital status Single Married Divorced Widowed Education Less than junior (
Performance status and depressive symptoms as predictors of quality of life in cancer patients. A structural equation modeling analysis Hermann Faller1*, Elmar Brähler2,3, Martin Härter4, Monika Keller5, Holger Schulz4, Karl Wegscheider6, Joachim Weis7, Anna Boehncke7, Matthias Richard1, Susanne Sehner6, Uwe Koch4 and Anja Mehnert2,4 1
Department of Medical Psychology and Psychotherapy, Medical Sociology and Rehabilitation Sciences, and Comprehensive Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany 2 Department of Medical Psychology and Medical Sociology, Section of Psychosocial Oncology, University Medical Center Leipzig, Leipzig, Germany 3 Department of Psychosomatic Medicine and Psychotherapy, University Medical Center Mainz, Mainz, Germany 4 Department and Outpatient Clinic of Medical Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany 5 Division of Psychooncology, Department for Psychosomatic and General Clinical Medicine, University Hospital Heidelberg, Heidelberg, Germany 6 Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany 7 Department of Psychooncology, Tumor Biology Center, University of Freiburg, Freiburg, Germany *Correspondence to: Department of Medical Psychology and Psychotherapy, Medical Sociology and Rehabilitation Sciences, University of Würzburg, Klinikstr. 3, D 97070 Würzburg, Germany. E-mail: [email protected]
Received: 1 October 2014 Revised: 9 February 2015 Accepted: 28 February 2015
Abstract Objective: This study aimed to examine whether depressive symptoms and performance status are independent predictors of both the physical and psychological domains of health-related quality of life (HRQoL) in cancer patients. Methods: A sample of 4020 cancer patients (mean age 58 years, 51% women) was evaluated. Depressive symptoms were measured with the patient health questionnaire and HRQoL with the European Organisation for Research and Treatment of Cancer quality of life questionnaire core 30. The impact of the illness on everyday activities was assessed with physician ratings of both the Karnofsky performance status and the Eastern Cooperative Oncology Group performance status. The simultaneous effects of depression and performance status on quality of life outcomes were estimated using structural equation modeling. Results: Both depressive symptoms and performance status independently predicted the physical and psychological domains of HRQoL. However, the impact of depressive symptoms on the physical HRQoL was stronger than the impact of performance status on the psychological HRQoL. Conclusion: Our results suggest that comorbid depressive symptoms are independently associated with both physical and psychological HRQoL in cancer patients after controlling for the physicianrated performance status. Thus, comorbid depression should be taken into account when evaluating reduced HRQoL in cancer patients. To support a causal impact of depression on HRQoL, intervention studies are needed to show that improving depression enhances cancer patients’ HRQoL. Copyright © 2015 John Wiley & Sons, Ltd.
Background Cancer patients may suffer from both emotional distress [1–4] and impaired quality of life [2,5]. Although previous studies have examined the association between emotional distress and health-related quality of life (HRQoL) [6–16], only scarce evidence exists regarding the question whether reduced HRQoL is primarily a consequence of the severity of the condition or rather of patients’ emotional distress associated with having received a diagnosis of cancer. While both stage of cancer [17,18] and somatic symptoms, such as pain, fatigue, and lymphedema [5], have been linked to reduced HRQoL, the impact of the illness on patients’ activities of daily living may best be grasped by measures of performance status. The performance Copyright © 2015 John Wiley & Sons, Ltd.
status, that is, a patient’s ability to perform everyday activities, is routinely evaluated in oncological care by attending physicians. In contrast to objective measures such as tumor stage, the physician-rated performance status is closer to patients’ subjective health perception, but at the same time not confounded with self-reports of HRQoL because performance status ratings come from a different source, that is, physicians instead of patients. Patientreported somatic symptoms, on the other hand, may partially overlap with HRQoL ratings. We therefore selected the physician-rated performance status to account for the impact of cancer severity on patients’ everyday activities. While studies have found bivariate small-to-medium correlations of both performance status and depressive symptoms with HRQoL [19,20], multivariable predictions of HRQoL have rarely been performed. One study
Performance status, depressive symptoms, and quality of life in cancer
demonstrated that both performance status, as measured by the Karnofsky performance status (KPS) [21], and depressive symptoms independently predicted a global rating of HRQoL [22]. Other studies confirming these associations used various HRQoL dimensions as the dependent variables but examined each individual subscale in turn [23,24]. In the first study, both predictors, that is, performance status as measured by the Eastern Cooperative Oncology Group (ECOG) performance status [25] and depressive symptoms, were not included simultaneously, but sequentially [23]. In the second study, the predictive value of both ECOG performance status and depressive symptoms varied across the HRQoL subscales assessed [24].Thus, a thorough test of the respective impact of performance status and depressive symptoms on HRQoL is still lacking. We therefore conducted a study examining the predictive value of both performance status and depressive symptoms on HRQoL in a large, representative sample of cancer patients. Using structural equation modeling, we aimed to estimate the simultaneous impact of both performance status and depressive symptoms on the physical and psychological domains of HRQoL. Structural equation modeling was used because it allows for a simultaneous analysis of the impact of multiple independent variables (i.e., performance status and depressive symptoms) on several dependent variables (i.e., physical HRQoL and psychological HRQoL). Consequently, a direct comparison of the respective impact of the independent variables on the dependent variables is feasible. In particular, we aimed to clarify whether the psychological and physical domains of HRQoL were differentially affected by depressive symptoms, simultaneously accounting for performance status. Likewise, we examined the effect of performance status on the physical and psychological domains of HRQoL, simultaneously adjusting for depressive symptoms. To avoid contamination of the somatic symptoms of depression with the symptoms of cancer as reflected in both performance status and HRQoL, we focused only on cognitive–affective symptoms of depression.
Methods The methods of the study are described in detail elsewhere [26]. In this multicenter, epidemiological cross-sectional study, cancer patients were enrolled from acute care hospitals, outpatient facilities, and cancer rehabilitation clinics in five study centers in Germany (Freiburg, Hamburg, Heidelberg, Leipzig, and Würzburg).
Study participants Patient inclusion criteria comprised the evidence of a malignant tumor and age between 18 and 75 years. Patients Copyright © 2015 John Wiley & Sons, Ltd.
1457
across all tumor entities and disease stages were included and stratified by nationwide incidence of cancer diagnoses. Patient exclusion criteria comprised the presence of severe physical, cognitive, and/or verbal impairments that would interfere with a patient’s ability to give informed consent. All patients who fulfilled the inclusion criteria were contacted by research assistants and consecutively recruited at the participating institutions. All participants were screened for depressive symptoms employing the patient health questionnaire depression module (PHQ-9) [27] and then asked to provide self-report data that form the basis of this study. A subsample also took part in a structured clinical interview, the results of which are presented elsewhere [28]. The study complied with the Declaration of Helsinki and was approved by the ethics committees of all participating centers [26]. All participants provided written informed consent.
Measures Depressive symptoms were measured using the German version of the PHQ-9 [27,29]. The PHQ-9 compared favorably with other screening instruments when evaluated with diagnostic criteria provided by the Diagnostic and Statistical Manual of Mental Disorders, 4th Revision, as reference standard [29]. Higher values indicate more severe symptoms. We construed a latent variable depressive symptom for use in structural equation modeling. This variable was represented by three items from the PHQ-9. Using at least three items for measuring a latent variable is generally deemed preferable as doing so allows for the identification of the measurement model. The items selected were as follows (abbreviations in brackets): little interest or pleasure in doing things (anhedonia); feeling down, depressed, or hopeless (depressed mood); and feeling bad about yourself or that you are a failure or have let yourself or your family down (worthlessness). These three items include the two core symptoms of major depression that also constitute the two-item version of the PHQ plus an additional item covering a cognitive feature of the depressive syndrome. We only selected cognitive–affective items to measure depressive symptoms. We refrained from selecting the somatic–affective items of the PHQ-9, such as trouble sleeping, feeling tired, change in appetite, trouble concentrating, or feeling slowed down or restless. By selecting only items that do not address bodily complaints, we avoided confounding of the latent variable depressive symptoms with the HRQoL limitations conveyed by the physical status of the patients (see succeeding text). Patients’ HRQoL was assessed using the self-reported cancer-specific European Organisation for Research and Treatment of Cancer quality of life questionnaire core 30 [30]. This instrument is widely used and has a good reliability and validity. Higher values indicate better HRQoL. Psycho-Oncology 24: 1456–1462 (2015) DOI: 10.1002/pon
H. Faller et al.
1458
As with the selection of depressive symptoms described earlier, we carefully examined the subscales for representing the physical and psychological components of HRQoL to avoid confounding between both components. The subscales physical functioning and role functioning were selected as indicators of the latent variable physical HRQoL because they cover restrictions of functioning related to physical performance. In contrast, the subscales emotional functioning, social functioning, and global quality of life cover psychosocial aspects of HRQoL as well as life satisfaction and therefore were selected as indicators of the latent variable psychological HRQoL. We did not select cognitive functioning as indicator of either HRQoL component because it may be associated with both physical and psychological aspects of HRQoL, thus precluding an unambiguous assignment. We did not select the symptom subscales and items of the European Organisation for Research and Treatment of Cancer quality of life questionnaire core 30 as they may rather be considered causes, instead of indicators, of reduced HRQoL. As indicators of performance status, we employed two performance ratings obtained from the attending physicians, namely the KPS [21] and the ECOG performance status [25]. The KPS measures limitations of daily activities and self-care in 10-point steps ranging from 0 = dead to 100 = no limitations. The ECOG performance status measures patients’ abilities to perform activities of daily living using five grades ranging from 0 = fully active to 5 = dead. Both instruments are well established, validated measures of performance status in oncological patients [31,32]. By employing physician ratings, we avoided the danger of confounding by measurement as would have been the case, had both HRQoL and performance status been measured with self-reports. Both KPS and ECOG performance status represented the latent variable ‘performance status’.
used as a fit criterion as the sample size was very large thus producing significant test results even with good model fit [33,34].
Results Sample characteristics Figure 1 shows the study enrolment. A total of 5889 cancer patients fulfilling the inclusion criteria were identified at 30 hospitals, cancer care clinics, and rehabilitation centers in Germany during the survey period. Across these institutions, 84 different oncology departments and hospital wards were included. The response rate was 69.5%, which led to a total of 4091 participants who were screened for depressive symptoms using the PHQ-9. Of these, 4020 provided additional self-report data. Sample characteristics are detailed in Table 1. The mean age of the participants was 58 years (standard deviation (SD) = 11), and about half of them were female. Of the participants, 71% were married. About 70% had received more than basic education. The most frequent tumor sites were breast (23%), digestive organs (20%), and male genital organs (17%). In most patients, the tumors were in complete (41%) or partial (12%) remission. Illness duration amounted to 5 months as a median (interquartile range 2–14). Participants were recruited in acute care hospitals (43%), outpatient units (33%), and rehabilitation clinics (24%). The frequencies of cancer sites varied across treatment settings. Relative to other settings, breast cancer was less frequent in acute care hospitals, cancer of digestive organs in outpatient units, and cancer of respiratory organs in rehabilitation clinics (data not shown). We analyzed differences in age, sex, education, and setting between study participants (4020) and non-
Structural equation modeling A structural equation model was created with two independent latent variables, performance status and depressive symptoms, and two dependent latent variables, the physical and psychological domains of HRQoL. All analyses were carried out using SPSS statistical software version 19 (IBM, Armonk, NY, USA) and MPLUS software (Muthén & Muthén, Los Angeles, CA, USA). To estimate parameters, the maximum-likelihood estimation procedure was applied. Missing data were handled using the full information maximum likelihood estimates. The following goodness-of-fit statistics were computed: comparative fit index (CFI), Tucker–Lewis index (TLI), root-mean-square error of approximation (RMSEA), and standardized root-mean-square residual (SRMR). The chi-squared test was also computed, but it should not be Copyright © 2015 John Wiley & Sons, Ltd.
Figure 1. Patient flow. PHQ, patient health questionnaire Psycho-Oncology 24: 1456–1462 (2015) DOI: 10.1002/pon
participants (1798). Study participants were younger (M = 58.1, SD = 11.3 vs M = 62.0, SD = 10.1) (p < 0.001), had a higher school education (p < 0.001), and were more likely treated in a rehabilitation clinic then during inpatient acute care (p < 0.001). No sex differences were found (p = 0.10).
Structural equation modeling Descriptive statistics as well as the zero-order correlations of the model variables are displayed in Table 2. The structural equation model can be seen in Figure 2 (with standardized estimates), in which rectangles represent observed variables, circles latent variables, single-headed arrows the impact of one variable on another, and double-headed arrows the covariance between pairs of variables. All paths are significant at p < 0.001. The overall goodness-of-fit statistics indicate an acceptable model fit (CFI = 0.95, TLI = 0.93, RMSEA = 0.08, SRMR = 0.04). Because of the large sample size, the chi-squared test showed a significant Copyright © 2015 John Wiley & Sons, Ltd.
1 0.54 1 0.51 0.53
All correlations p < 0.01, with the exception of †, not significant. ECOG-PS, Eastern Cooperative Oncology Group performance status; KPS, Karnofsky performance status; PHQ, patient health questionnaire; EORTC, European Organisation for Research and Treatment of Cancer; SD, standard deviation.
SD, standard deviation.
1 0.42 0.59 0.58
43.2 32.9 23.9
1 0.65 0.39 0.48 0.57
1735 1324 961
4020
1 0.15 0.15 0.35 0.23 0.21
40.7 12.4 17.6 9.3 20.0
1 0.40 0.29 0.31 0.55 0.36 0.39
1581 484 683 361 779
3888
1 0.48 0.26 0.32 0.32 0.40 0.32 0.36
22.5 19.7 16.8 8.9 7.9 7.6 5.5 2.8 2.0 1.8 1.6 2.8
1 0.17 0.18 0.06 0.41 0.28 0.14 0.22 0.28
906 790 677 359 317 305 221 113 80 73 65 114
4020
1 0.82 0.18 0.17 0.02† 0.39 0.26 0.12 0.21 0.27
31.0 29.7 39.0 0.2
0.82 13 0.89 0.75 0.60 24 35 26 33 23
1244 1191 1564 8
4007
0.70 86 0.85 0.67 0.27 70 57 65 61 56
12.2 70.7 10.8 6.3
3859 3855 3984 3944 3972 3820 3796 3814 3808 3804
459 2657 405 238
1459
ECOG performance status Karnofsky performance status PHQ anhedonia PHQ depressed mood PHQ worthlessness EORTC physical functioning EORTC role functioning EORTC emotional functioning EORTC social functioning EORTC global quality of life
% 51.4
KPS
n 2068
ECOG-PS
SD 11
SD
4020 3759
M 58
M
Woman Marital status Single Married Divorced Widowed Education Less than junior (
