Skip to main content
Download PDF

Responsiveness and construct validity of EPIC-26, AQoL-6D and SF-6D following treatment in prostate cancer

BMC Cancer volume 23, Article number: 297 (2023) Cite this article

Abstract

Purpose

To assess construct validity and responsiveness of the Expanded Prostate Cancer Index Composite Instrument (EPIC-26) relative to the Short-Form Six-Dimension (SF-6D) and Assessment of Quality of Life 6-Dimension (AQoL-6D) in patients following treatment for prostate cancer.

Methods

Retrospective prostate cancer registry data were used. The SF-6D, AQoL-6D, and EPIC-26 were collected at baseline and one year post treatment. Analyses were based on Spearman's correlation coefficient, Bland–Altman plots and intra-class correlation coefficient, Kruskal Wallis, and Effect Size and the Standardised Response Mean for responsiveness.

Results

The study sample was comprised of 1915 patients. Complete case analysis of 3,697 observations showed moderate evidence of convergent validity between EPIC-26 vitality/hormonal domain and AQoL-6D (r = 0.45 and 0.54) and SF-6D (r = 0.52 and 0.56) at both timepoints. Vitality/hormonal domain also showed moderate convergent validity with coping domain of AQoL-6D (r = 0.45 and 0.54) and with role (r = 0.41 and 0.49) and social function (r = 0.47 and 0.50) domains of SF-6D at both timepoints, and with independent living (r = 0.40) and mental health (r = 0.43) of AQoL-6D at one year. EPIC-26 sexual domain had moderate convergent validity with relationship domain (r = 0.42 and 0.41) of AQoL-6D at both timepoints. Both AQoL-6D and SF-6D did not discriminate between age groups and tumour stage at both timepoints but AQoL-6D discriminated between outcomes for different treatments at one year. All EPIC-26 domains discriminated between age groups and treatment at both timepoints. The EPIC-26 was more responsive than AQoL-6D and SF-6D between baseline and one year following treatment.

Conclusions

AQoL-6D can be used in combination with EPIC-26 in place of SF-12. Although EPIC-26 is not utility based, its popularity amongst clinicians and ability to discriminate between disease-specific characteristics and post-treatment outcomes in clinical trials makes it a candidate for use within cost-effectiveness analyses. The generic measure provides a holistic assessment of quality of life and is suitable for generating quality adjusted life years (QALYs).

Peer Review reports

Background

Globally, prostate cancer is the second most common cancer in men and one of the top four cancers overall [1, 2]. Australia is ranked among countries with the highest rates of prostate cancer, affecting up to 1 in 6 Australian men by age 85 and accounting for 25% of new cancer cases and 12% of cancer deaths among men [2, 3]. The associated healthcare cost of managing prostate cancer in Australia is estimated to rise by 42%, from US$270.9 million in 2016 to US$384.3 million in 2025 [4]. With rising costs, it is vital to demonstrate the benefits of the different treatments in terms of clinical effectiveness and health-related quality of life (HRQoL) through health economic modelling. Health economic evaluation is widely applied in the appraisal of health care interventions by comparing their costs and outcomes. Leading international organisations for health technology assessment such as the Pharmaceutical Benefits Advisory Committee (PBAC) in Australia and the National Institute for Health and Care Excellence (NICE) in the UK, recommend the use of cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) as preferred methods of evaluation [5, 6]. Outcomes in CEA are measured in terms of clinical effectiveness and quality adjusted life years (QALYs) in CUA. A QALY is a measure of disease burden for use in health economic modelling that combines both the quantity of life attributed to a given health state or intervention and the value individuals place on that life when comparing two or more interventions [7]. QALYs are derived from responses to preference-based measures (PBM) of HRQoL. These measures comprise a descriptive system for classifying all possible health states defined by the instrument and an off-the-shelf scoring algorithm or value set comprising values on the 0 (dead) to 1 (full health) scale. Unlike clinical effectiveness metrics that are specific to a condition, the QALY is a generic measure that facilitates the comparison of outcomes between different disease conditions. Preference based measures can be generic such as the Short Form 6-Dimesnions, SF-6D [8] and Assessment of Quality of Life 6 dimension, AQoL-6D [9] or disease-specific, such as the cancer-specific PBMs derived from responses to the European Organization for Research and Treatment of Cancer core quality of life questionnaire (EORTC QLQ-C30), EORTC Quality of Life Utility Measure-Core 10 dimensions, QLU-C10D [10] and EORTC-8D [11] Other PBMs for specific conditions include the prostate cancer-specific Patient-Oriented Prostate Utility Scale (PORPUS) [12] and the patient-reported Short Bowel Syndrome-specific quality of life scale (SBS-QoL) [13].

Prostate cancer diagnosis and treatment affect both disease-specific and generic HRQoL, varying based on the stage of the disease, type of treatment received and time post-treatment. Men with localised disease experience disease-specific detriments to HRQoL, such as problems with urinary function, sexual function and bowel function but fewer problems with the generic HRQoL. Generic HRQoL is more affected in men with advanced disease, specifically physical functioning, social well-being, pain, and psychological well-being associated with the impending possibility of death at this stage [14,15,16]. Therefore, the challenge for quality of life measurement is that although generic HRQoL measures are suitable for generating utilities for application in CUA, they may not be as sensitive to changes in disease-specific quality of life [17,18,19]. Although disease-specific PBMs have been developed, when used in an economic evaluation, such results cannot be generalised or compared to other conditions, and therefore the need to still use the generic measures [20, 21]. However, the level of divergence in validity and responsiveness between disease-specific and generic PBMs when used in prostate cancer research is unclear.

Previous studies have assessed the construct validity of AQoL-6D and SF-6D in different patient populations [22, 23], but no studies have yet compared them to the Expanded Prostate Cancer Index Composite Instrument (EPIC-26), one of the most widely validated prostate cancer-specific measures of QoL [24]. This present study aimed to explore the construct (convergent and known groups validity and responsiveness of the generic preference-based HRQoL measures, SF-6D and AQoL-6D when compared to EPIC-26 in men with prostate cancer.

Methods

Data

The South Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC) is a longitudinal dataset with linked public and private hospital admissions for approximately 17,000 men with prostate cancer since 1998. The database commenced in 1998 and currently recruits over 90% of newly diagnosed prostate cancer cases in South Australia, which has approximately 1.67 million people. The database captures information about patient demographics (age at diagnosis, date of diagnosis, postcode), diagnosis (cancer tumour stage), biochemistry (Gleason score and grade), treatment received (radical or palliative external beam radiation therapy—EBRT, brachytherapy, robot-assisted laparoscopic radical prostatectomy, open retropubic radical prostatectomy and active surveillance or watchful waiting) at baseline. HRQoL is assessed through self-completion of a paper-based survey that is mailed-out at the start of treatment or baseline, one year and 5-years post-treatment initiation. Data from the returned surveys is manually entered into the database. Patients over 18 years of age diagnosed with prostate cancer and commencing treatment between February 2010 and March 2020 were included in this study. HRQoL data on the Short Form Health Survey (SF-12) (from which the SF-6D was derived) [8], AQoL-6D [9] and the EPIC-26, collected at baseline and one year following treatment, were included in this study. Baseline measurements are taken between the date of diagnosis and the date of first treatment (or documented management plan). At any given time point, patients completed either EPIC-26 and SF-12 or EPIC-26 and AQoL-6D. No patients completed both generic measures simultaneously.

Measures/Instruments

The EPIC-26 is a prostate cancer-specific measure of HRQoL with five multi-item domains, namely urinary incontinence (four items), urinary irritation/obstruction (four items), bowel (six items), sexual (six items) and vitality/hormonal function (five items) and a single-item measure of overall urinary bother [25]. No total score for the entire measure can be calculated, but domain summary scores are generated on a 0–100 scale.

The AQoL-6D is a preference-based measure of quality of life with scoring algorithms or value sets based on Australian general adult and adolescent populations [9]. The AQoL-6D has six dimensions (independent living (four items), mental health, coping (four items), relationships (three items), pain (three items) and senses). Each dimension has five levels ranging from no difficultiescompleting tasks to severe difficulty. AQoL-6D domain and utility scores were generated based on the Australian adult general population scoring algorithm.

The SF-12 is a shorter version of the widely used SF-36 that includes only 12 items whose scores can be aggregated into two summary scales, the Physical Component Summary (PCS) and the Mental Component Summary (MCS) [8]. Although not preference-based, responses to the SF-12 can be used to generate SF-6D utility index scores using the algorithm developed by Brazier and Roberts [26]. Therefore, utility and six domain scores (for physical function, role function, social function, pain, mental health, and vitality) were generated from the SF-12.

Analysis

AQoL-6D and SF-6D utilities and EPIC-26 domain summary scores were tested for normality using the Shapiro-Francia test [27] with parametric or non-parametric tests applied as appropriate.

Responsiveness

Responsiveness assesses whether an instrument detects change where change is expected over time. Responsiveness was assessed for patients who completed both baseline and one-year assessments for each instrument.

Two statistical tests were applied: Effect Size (ES) and the Standardised Response Mean (SRM) [28]. These tests measure the change in index scores relative to the variation among the sample or the average change in scores normalised by a measure of deviation, in this case, the standard deviation. Effect size demonstrates the extent to which an instrument measures changes in quality of life relevant to an intervention.

$$\begin{array}{c}\mathrm{Effect}\;\mathrm{size}=\frac{\mathrm{mean}\;\mathrm{change}\;\mathrm{from}\;\mathrm{baseline}\;\mathrm{to}\;\mathrm{one}\;\mathrm{year}}{\mathrm{standard}\;\mathrm{deviation}\;\mathrm{of}\;\mathrm{scores}\;\mathrm{at}\;\mathrm{baseline}}\\\mathrm{Standardised}\;\mathrm{Response}\;\mathrm{Mean}=\frac{\mathrm{mean}\;\mathrm{change}\;\mathrm{from}\;\mathrm{baseline}\;\mathrm{to}\;\mathrm{one}\;\mathrm{year}}{\mathrm{standard}\;\mathrm{deviation}\;\mathrm{of}\;\mathrm{the}\;\mathrm{mean}\;\mathrm{change}}\end{array}$$

For both ES and SRM, scores of < 0.20 represent a trivial effect, 0.20–0.49 a small effect, 0.50–0.80 a moderate effect and > 0.80 a large effect [29].

For these measures, we also assessed if the minimum clinically important difference (MCID) or clinically significant mean change was achieved over time. The MCID varies between EPIC-26 domains. A mean change of 4–6 points is considered clinically significant for the bowel and vitality/hormonal domains, a change of 10–12 points for the sexual domain, 6–9 points for urinary incontinence and 5–7 points for the urinary irritation/obstruction domain [30]. When used in general population samples, a mean change between baseline and follow-up of 0.13 or more reflect important differences between groups, and a change of 0.06 utility points over time is clinically significant for AQoL-6D [31], while a mean change of 0.012 for SF-6D has been reported in populations with incontinence, which may be similar to the sample in this study [32].

Construct validity

Construct validity “…refers to whether the instrument provides the expected scores, based on existing knowledge about the construct it is measuring” [33]. It consists of both convergent validity and known groups validity. Convergent validity assesses the correlation (or lack thereof) between constructs measured by two or more instruments, while known groups validity is an instrument’s ability to measure expected differences between subgroups of patients [33, 34]. All patients who completed the questionnaires at baseline and at one year were included in this analysis.

Convergent validity

For convergent validity, analysis at each time point is presented. (Analysis of the pooled HRQoL assessments is provided in additional file 1). The correlations between EPIC-26 domain scores and the dimensions of each of the utility based measures as well as the utility scores were examined using Spearman’s correlation coefficient. Statistical significance was considered at the 5% significance level. Correlations < 0.40 were considered weak, those between 0.40 and 0.70 were moderate, and those ≥ 0.70 were strong [35]. Strong correlations indicate that the measures or dimensions are measuring similar constructs. We hypothesised that there would be convergent validity in similar domains, such as EPIC-26 vitality/hormonal and sexual domains with vitality dimension on the SF-6D.

Modified Bland–Altman plots were also used to study further the limits of agreement between utility scores and the EPIC-26 domain scores. Because the instruments use different rating scales, leading to marked differences in the magnitude of the scores (i.e. 0–1 scale for SF-6D and AQoL-6D while EPIC-26 domain scores are on the 0–100 scale), standardised Z scores were calculated for the modified Bland–Altman plots. Similar to other studies, utilities and summary scores were power transformed to follow a normal distribution before calculating Z scores [36]. Good agreement was defined as only 5% of points being outside of the limits of agreement (LOA) [37].

To assess the agreement between individual measures (AQoL-6D, SF-6D and EPIC-26 individual domains), for each respondent, the intra-class correlation coefficient (ICC) was estimated using the z scores. The ICC was estimated using a two-way mixed-effect model with absolute agreement, where the individual effect was random, and the effect of the HRQoL measure was fixed. The strength of agreement was based on the following thresholds: ICC = 0–0.2 (poor),), ICC > 0.2–0.4 (fair),, ICC =  > 0.4–0.6 (moderate), ICC =  > 0.6–0.8 (strong) and ICC > 0.8 (almost perfect) [38].

Known groups validity

Known groups validity and was assessed using the Kruskal Wallis test. We hypothesised that there would be differences in HRQoL based on age at diagnosis [39], tumour/cancer stage [40, 41], and treatment group [40, 41]. Quality of life would be lower in the older age groups, in men with more advanced cancer and those receiving more invasive treatments such as radical prostatectomy compared to active surveillance or EBRT.

For all the analysis, a p-value of 0.05 was considered significant.

Results

There was a total of 2587 patients, 1915 had complete data on all domains of the EPIC-26 and were included in this analysis. For both time points, 735 completed the EPIC-26, 206 completed both the EPIC-26 and AQoL-6D and 88 completed both EPIC-26 and the SF-12, none of the patients completed only AQoL-6D or SF-12. At baseline, 1454 completed EPIC-26 with 444 and 270 completing it in combination with the AQoL-6D and SF-12, respectively. At one year 1196 completed EPIC-26, with 598 and 437 completing in combination with AQoL-6D and SF-12 respectively. Table 1 presents the demographic and clinical characteristics of all the respondents who completed the questionnaires at baseline and at one year.

Table 1 Patient demographic characteristics
Full size table

The mean age of the participants was 65 years, and the majority (38% at baseline and 31% at the one-year time point) were in the 65–74 age group. Most of the men (45% at baseline and 36% at one year) were diagnosed with stage T2 cancer and underwent radical prostatectomy (61% in baseline and 44% in one-year group).

Table 2 presents the descriptive statistics for health status. The mean (sd) utility scores at baseline were 0.86 (0.13) and 0.81 (0.12) for AQoL-6D and SF-6D, respectively and 0.86 (0.14) and 0.82 (0.12) at one-year. Summary scores for the EPIC domains are shown in Table 2.

Table 2 Descriptive statistics for health status
Full size table

Responsiveness

AQoL-6D utility scores deteriorated, while those for the SF-6D improved between baseline and one year. However, the mean change in utility scores for both measures was not statistically significant (Table 3). Both effect size and SRM values for both measures were below 0.2, indicating low sensitivity to change. All EPIC domains deteriorated at one year, except for urinary irritation/obstruction, which improved. The mean changes in all EPIC domains were statistically significant, except for the bowel domain. The changes in urinary incontinence, urinary obstruction and sexual domains were clinically significant as they were above the MCID at 12.5, 5.1 and 28.5 points, respectively. All domains demonstrated low sensitivity to change except for urinary incontinence, which was moderately sensitive (ES = 0.65 and SRM = 0.46) and the sexual domain with high sensitivity to change (ES = 0.96 and SRM = 1.04).

Table 3 Responsiveness between baseline and 1 year
Full size table

Convergent validity of EPIC-26 with AQoL-6D and SF-6D

Table 4 shows the correlations between EPIC-26 domains and the dimensions of the AQoL-6D and SF-6D. Moderate evidence of convergent validity was observed between EPIC-26 sexual and vitality/hormonal domain with relationships and coping on AQoL-6D at both time points. The vitality/hormonal domain was also moderately correlated with independent living (r = 0.40) and mental health (r = 0.43) at one year. With SF-6D, the EPIC-26 vitality/hormonal domain showed moderate convergent validity with the role and social function at both baseline and one year. Weak correlations (r < 0.40) were observed between all other EPIC domains and the dimensions of the generic measures.

Table 4 Correlations between EPIC domains and utility and domain scores (Spearman)
Full size table

The modified Bland–Altman plots show the limits of agreement between EPIC-26 domains and AQoL-6D (Fig. 1a and b) and SF-6D (Fig. 2a and b) utility scores. A weak agreement was observed between the utility scores and most EPIC-26 domains. The sexual domain showed good agreement with both AQoL-6D and SF-6D utility scores at both time points. Good agreement was also observed between SF-6D utility score and urinary incontinence domain at baseline but not at one year.

Fig. 1
figure 1

a Modified Bland and Altman Plots of EPIC-26 domains and AQoL-6D utility score – Baseline. b Modified Bland and Altman Plots of EPIC-26 domain and AQoL-6D utility score – One-year

Full size image
Fig. 2
figure 2

a Modified Bland and Altman Plots of EPIC-26 domain and SF-6D utility score – Baseline. b Modified Bland and Altman Plots of EPIC-26 domain and SF-6D utility score – One year

Full size image

Results of the intraclass correlation are presented in Table 5.

Table 5 Intra-class correlation
Full size table

The absolute agreement between individual AQoL-6D utility scores and the domain scores of the EPIC-26 was low at baseline (0.27) and one year (0.31). Similarly, the agreement between the EPIC-26 and the SF-6D utility score was low at baseline (0.25) and one year (0.28). But the agreement between the mean scores was strong at 0.69 and 0.73 with AQoL-6D and 0.67 and 0.70 with SF-6D at baseline and one year, respectively.

Known groups validity

Table 6 shows the results of the known groups validity. Both AQoL-6D and SF-6D did not discriminate between age groups and tumour stages at both time points. Only AQoL-6D discriminated between treatment groups at one year. All EPIC-26 domains discriminated between age groups at both time points except the vitality/hormonal domain. Urinary incontinence and bowel domains did not discriminate between tumour stage, while all domains discriminated between treatment groups.

Table 6 Known groups validity between age groups, tumour/cancer stage and treatment [n, mean (sd)]
Full size table

Discussion

The past decade has seen an increase in the development of condition-specific patient-reported outcome measures for cancer [10, 24, 43]. However, their use in decision-making within the discipline of health economics remains limited. This study empirically compared a prostate cancer-specific patient-reported outcome measure (EPIC-26) against two generic utility measures (AQoL-6D and SF-6D) using a sample drawn from a longitudinal cohort of men diagnosed with prostate cancer between February 2010 and March 2020. The study analysed HRQoL outcomes at baseline and one-year post-treatment. Across Australia, radical prostatectomy is reported in 50% of newly diagnosed prostate cancer cases [44] and our dataset did not deviate from this expected proportion with majority of the men undergoing prostatectomy (77%) and 10% each on active surveillance and radiation therapy.

EPIC-26 domain scores reduced over time, similar to other longitudinal studies applying the EPIC-26 in a similar population [45, 46]. When assessing responsiveness the generic measures were not expected to be sensitive to change [32], but high sensitivity and responsiveness were expected for the EPIC-26 domains [24, 46]. However, the EPIC-26 effect size and SRM were small except for the sexual domain.

Although it is recommended that EPIC-26 is administered in combination with the generic SF-12 [47], no studies have investigated the convergent validity of EPIC-26 domains with the domains of the SF-12 or its preference-based SF-6D. This study showed positive correlations between AQoL-6D and SF-6D utility scores and all EPIC-26 domains. Utility scores for both generic measures were moderately correlated with the hormonal domain but weakly correlated with all the other domains. Similarly, for the individual dimensions of AQoL-6D and SF-6D, the vitality/hormonal domain was moderately correlated with independent living, mental health and coping on AQoL-6D, and with the role and social function on SF-6D. Studies have shown that, treatment for prostate cancer is associated with depression and poorer HRQoL due to mental factors [48, 49]. It is therefore not surprising that the vitality/hormonal domain, although not strongly correlated with the mental health domain of both AQoL and SF-6D, was correlated with nearly all domains of both measures. The breadth of dimensions this domain is correlated with may imply the far-reaching effects of hormonal impairment on overall quality of life.

As with other studies investigating convergent validity between generic and condition-specific instruments [19, 36, 43], we hypothesised that EPIC-26 domains would be highly correlated with AQoL-6D and SF-6D dimensions that measure similar constructs, such as the sexual domain with vitality on the SF-6D, but this correlation was weak. However, the sexual domain was moderately correlated with the relationship dimension of AQoL-6D and the Bland Altman plots showed strong agreement between both utility scores and the sexual domain at both time points.

Both generic measures did not discriminate between age groups and tumour stage, while only AQoL-6D discriminated treatment groups and is recommended for assessing HRQoL outcomes following treatment. All EPIC domains discriminated between age groups (except the vitality/hormonal domain) and treatment group, but only one domain discriminated between tumour stage at baseline and three domains at one year. Being disease-specific, we hypothesised that EPIC-26 domains would discriminate between both tumour stage and treatment groups, but this was only observed for the latter. It can be argued, therefore, that disease-specific EPIC-26 domains are more suitable for discriminating in outcomes following treatment.

It is also important to note that similar to the generic measures, only the vitality/hormonal domain did not discriminate between age groups. This observation, in addition to the moderate correlations between this domain and the utility scores noted above, may suggest a possible relationship between vitality/hormonal and overall quality of life.

In addition to previous studies, this study supports the utility of the disease-specific EPIC-26 in cost-effectiveness analysis, a type of economic evaluations. All three instruments comparably discriminate between general characteristics, but the disease-specific measure is better at discriminating between disease-specific characteristics and post-treatment outcomes. For purposes of economic evaluation, the choice of instrument(s) depends on the questions being asked and the outcomes of interest. If urinary incontinence (leakage incidents, use of pads) is the outcome of interest, then the EPIC-26 has to be used. Even if SF-6D and AQoL-6D scores correlated highly with the urinary incontinence scores, these measures do not elicit enough information on the disease-specific impairments. On the other hand, if utility scores are needed for generating QALYs in a health economic model, knowing how many pads the patients used may be less important.

The developers of the EPIC-26 recommend that it is used in combination with SF-12 to provide a holistic assessment of HRQoL. Both AQoL-6D and SF-6D were comparable in discriminating between disease-specific characteristics, but only AQoL-6D could discriminate between outcomes following treatment. Results from this study posit the AQoL-6D as a potential (maybe even better) compliment to EPIC-26. This proposition requires further study with a larger sample size.

The main strength of this study is that it provides the first head-to-head empirical assessments of the construct validity and responsiveness of two generic utility measures, AQoL-6D and SF-6D, relative to the EPIC-26. However, the main weakness was that while data on the EPIC-26 vs AQoL-6D and EPIC-26 vs SF-6D head-to-head comparisons were collected, none were collected for the AQOL-6D vs SF-6D correlation. Consequently, head-to-head comparisons between the AQoL-6D and SF-6D were not possible.

Conclusion

This study provides insights into whether generic preference-based measures of HRQoL can be used interchangeably or as a complement to the disease-specific EPIC-26. All three instruments are comparable in discriminating between general characteristics, but the disease-specific measure is better at discriminating in disease-specific characteristics and post-treatment outcomes. Using the disease-specific EPIC-26 and a generic measure provides a holistic assessment of quality of life in this population. In contrast, the generic measure is suitable for generating QALYs for use in CUA.

Availability of data and materials

Data can be made available on request from the Corresponding author.

Abbreviations

AQoL-6D:

Assesment of Quality of Life 6 Dimensions

CEA:

Cost Effectiveness Analysis

CUA:

Cost Utility Analysis

EPIC-26:

Expanded Prostate Cancer Index Composite Instrument

ES:

Effect Size

HRQoL:

Health Related Quality of Life

ICC:

Intra-class correlation coefficient

LOA:

Limits of Agreement

MAUIs:

Multi-Attribute Utility Instruments

MCID:

Minimum clinically important difference

MCS:

Mental Component Summary

MSAC:

Medical Services Advisory Committee

NICE:

National Institute for Health and Care Excellence

PBAC:

Pharmaceutical Benefits Advisory Committee

PCS:

Physical Component Summary

QALYs:

Quality Adjusted Life Years

QLU-C10D:

Quality of Life Utility Measure-Core 10 dimensions

SA-PCCOC:

South Australian Prostate Cancer Clinical Outcomes Collaborative

SF-12:

Short Form Health Survey

SF-6D:

Short Form 6 Dimensions

SRM:

Standardized Response Mean

References

  1. Culp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent global patterns in prostate cancer incidence and mortality rates. Eur Urol. 2020;77(1):38–52.

    Article  PubMed  Google Scholar 

  2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  PubMed  Google Scholar 

  3. Cancer Australia. Prostate cancer in Australia: Australian Government 2019. Available from: https://prostate-cancer.canceraustralia.gov.au/statistics.

    Google Scholar 

  4. Gordon LG, Tuffaha HW, James R, Keller AT, Lowe A, Scuffham PA, et al. Estimating the healthcare costs of treating prostate cancer in Australia: a Markov modelling analysis. Urol Oncol. 2018;36(3):91.e7-.e15.

    Article  Google Scholar 

  5. Harris A, Bulfone L. Getting value for money: the Australian experience. In: Jost TS, editor. Health Care Coverage Determinations: An International Comparative Study. Maidenhead: Open University Press, McGraw-Hill International; 2004.

    Google Scholar 

  6. National Institute for Health and Care Excellence. Guide to the methods of technology appraisal 2013. In: NICE, editor. 2013.

  7. Brazier J, Ratcliffe J, Salomon AJ, Tsuchiya A. Valuing Health Measuring and Valuing Health Benefits for Economic Evaluation. New York: Oxford University Press; 2007.

    Google Scholar 

  8. Ware J Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220–33.

    Article  PubMed  Google Scholar 

  9. Hawthorne G, Richardson J, Day N, McNeil H. Construction and utility scaling of the assessment of quality of life (AQoL) instrument. Melbourne: Centre for health program evaluation, Monash University, 2000. p. 25.

  10. King MT, Costa DS, Aaronson NK, Brazier JE, Cella DF, Fayers PM, et al. QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30. Qual Life Res. 2016;25(3):625–36.

    Article  CAS  PubMed  Google Scholar 

  11. Rowen D, Brazier J, Young T, Gaugris S, Craig BM, King MT, et al. Deriving a preference-based measure for cancer using the EORTC QLQ-C30. Value Health. 2011;14(5):721–31.

    Article  PubMed  Google Scholar 

  12. Krahn M, Ritvo P, Irvine J, Tomlinson G, Bezjak A, Trachtenberg J, et al. Construction of the Patient-Oriented Prostate Utility Scale (PORPUS): a multiattribute health state classification system for prostate cancer. J Clin Epidemiol. 2000;53(9):920–30.

    Article  CAS  PubMed  Google Scholar 

  13. Lloyd A, Kerr C, Breheny K, Brazier J, Ortiz A, Borg E. Economic evaluation in short bowel syndrome (SBS): an algorithm to estimate utility scores for a patient-reported SBS-specific quality of life scale (SBS-QoL™). Qual Life Res. 2014;23(2):449–58.

    Article  PubMed  Google Scholar 

  14. Holm M, Doveson S, Lindqvist O, Wennman-Larsen A, Fransson P. Quality of life in men with metastatic prostate cancer in their final years before death – a retrospective analysis of prospective data. BMC Palliat Care. 2018;17(1):126.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kao YL, Tsai YS, Ou FY, Syu YJ, Ou CH, Yang WH, et al. Determinants of quality of life in prostate cancer patients: a single institute analysis. Urol Sci. 2015;26(4):254–8.

    Article  Google Scholar 

  16. Torvinen S, Färkkilä N, Sintonen H, Saarto T, Roine RP, Taari K. Health-related quality of life in prostate cancer. Acta Oncol. 2013;52(6):1094–101.

    Article  PubMed  Google Scholar 

  17. Teckle P, Peacock S, McTaggart-Cowan H, van der Hoek K, Chia S, Melosky B, et al. The ability of cancer-specific and generic preference-based instruments to discriminate across clinical and self-reported measures of cancer severities. Health Qual Life Outcomes. 2011;9:106.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Krahn M, Bremner KE, Tomlinson G, Ritvo P, Irvine J, Naglie G. Responsiveness of disease-specific and generic utility instruments in prostate cancer patients. Qual Life Res. 2007;16(3):509–22.

    Article  PubMed  Google Scholar 

  19. Bulamu NB, Vissapragada R, Chen G, Ratcliffe J, Mudge LA, Smithers BM, et al. The Australian Immunonutrition Study. Responsiveness and convergent validity of QLU-C10D and EQ-5D-3L in assessing short-term quality of life following esophagectomy. Health Qual Life Outcomes. 2021;19(1):233.

  20. Goodwin E, Green C. A systematic review of the literature on the development of condition-specific preference-based measures of health. Appl Health Econ Health Policy. 2016;14(2):161–83.

    Article  PubMed  Google Scholar 

  21. Rowen D, Brazier J, Ara R, Azzabi ZI. The role of condition-specific preference-based measures in health technology assessment. Pharmacoeconomics. 2017;35(1):33–41.

    Article  PubMed  Google Scholar 

  22. Skinner EH, Denehy L, Warrillow S, Hawthorne G. Comparison of the measurement properties of the AQoL and SF-6D in critical illness. Crit Care Resusc. 2013;15(3):205–12.

    PubMed  Google Scholar 

  23. Richardson J, Iezzi A, Khan MA, Chen G, Maxwell A. Measuring the sensitivity and construct validity of 6 utility instruments in 7 disease areas. Med Decis Making. 2016;36(2):147–59.

    Article  PubMed  Google Scholar 

  24. Schmidt S, Garin O, Pardo Y, Valderas JM, Alonso J, Rebollo P, et al. Assessing quality of life in patients with prostate cancer: a systematic and standardized comparison of available instruments. Qual Life Res. 2014;23(8):2169–81.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Szymanski KM, Wei JT, Dunn RL, Sanda MG. Development and validation of an abbreviated version of the expanded prostate cancer index composite instrument for measuring health-related quality of life among prostate cancer survivors. Urology. 2010;76(5):1245–50.

    Article  PubMed  Google Scholar 

  26. Brazier JE, Roberts J. The estimation of a preference-based measure of health from the SF-12. Med Care. 2004;42(9):851–9.

    Article  PubMed  Google Scholar 

  27. Shapiro SS, Francia RS. An approximate analysis of variance test for normality. J Am Stat Assoc. 1972;67(337):215–6.

    Article  Google Scholar 

  28. Beaton DE, Bombardier C, Katz JN, Wright JG. A taxonomy for responsiveness. J Clin Epidemiol. 2001;54(12):1204–17.

    Article  CAS  PubMed  Google Scholar 

  29. Cohen J. Statistical power analysis for the behavioral sciences (2nd ed.). Routledge; 1988. https://doi.org/10.4324/9780203771587.

  30. Skolarus TA, Dunn RL, Sanda MG, Chang P, Greenfield TK, Litwin MS, et al. Minimally important difference for the Expanded Prostate Cancer Index Composite Short Form. Urology. 2015;85(1):101–5.

    Article  PubMed  Google Scholar 

  31. Hawthorne G, Osborne R. Population norms and meaningful differences for the Assessment of Quality of Life (AQoL) measure. Aust N Z J Public Health. 2005;29(2):136–42.

    Article  PubMed  Google Scholar 

  32. Halme AS, Fritel X, Benedetti A, Eng K, Tannenbaum C. Implications of the minimal clinically important difference for health-related quality-of-life outcomes: a comparison of sample size requirements for an incontinence treatment trial. Value in Health. 2015;18(2):292–8.

    Article  PubMed  Google Scholar 

  33. De Vet HCW, Terwee CB, Mokkink LB, Knol DL. Validity. In: Measurement in medicine: a practical guide. University Printing House, Cambridge CB2 8BS, United Kingdom: Cambridge University PressPractical Guides to Biostatistics and Epidemiology; 2011. [170].

    Chapter  Google Scholar 

  34. Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol. 2010;63(7):737–45.

    Article  PubMed  Google Scholar 

  35. Cohen J. Set Correlation and Contingency Tables. Appl Psychol Meas. 1988;12(4):425–34.

    Article  Google Scholar 

  36. Kaambwa B, Gill L, McCaffrey N, Lancsar E, Cameron ID, Crotty M, et al. An empirical comparison of the OPQoL-Brief, EQ-5D-3 L and ASCOT in a community dwelling population of older people. Health Qual Life Outcomes. 2015;13(1):164.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Int J Nurs Stud. 2010;47(8):931–6.

    Article  Google Scholar 

  38. Rosner BA. The intraclass correlation coefficient. Fundamentals of Biostatistics. B. A. Rosner. Pacific Grove: Duxbury Thomson Learning; 2011.

  39. Cimprich B, Ronis DL, Martinez-Ramos G. Age at diagnosis and quality of life in breast cancer survivors. Cancer Pract. 2002;10(2):85–93.

    Article  PubMed  Google Scholar 

  40. Eton DT, Lepore SJ. Prostate cancer and health-related quality of life: a review of the literature. Psychooncology. 2002;11(4):307–26.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Sanda MG, Dunn RL, Michalski J, Sandler HM, Northouse L, Hembroff L, et al. Quality of Life and Satisfaction with Outcome among Prostate-Cancer Survivors. N Engl J Med. 2008;358(12):1250–61.

    Article  CAS  PubMed  Google Scholar 

  42. Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess. 1994;6(4):284.

    Article  Google Scholar 

  43. Lorgelly PK, Doble B, Rowen D, Brazier J, Cancer i. Condition-specific or generic preference-based measures in oncology? A comparison of the EORTC-8D and the EQ-5D-3L. Qual Life Res. 2017;26(5):1163–76.

    Article  PubMed  Google Scholar 

  44. O’Callaghan M, Papa N, Pase M, Frydenberg M, Mark S, Moretti K, et al. Patterns of care for prostate cancer treatment and improving outcomes – are national registries the answer? BJU Int. 2021;128(S1):6–8.

    Article  PubMed  Google Scholar 

  45. Pearce SM, Wang CHE, Victorson DE, Helfand BT, Novakovic KR, Brendler CB, et al. A longitudinal study of predictors of sexual dysfunction in men on active surveillance for prostate cancer. Sex Med. 2015;3(3):156–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ávila M, Patel L, López S, Cortés-Sanabria L, Garin O, Pont À, et al. Patient-reported outcomes after treatment for clinically localized prostate cancer: a systematic review and meta-analysis. Cancer Treat Rev. 2018;66:23–44.

    Article  PubMed  Google Scholar 

  47. Wei JT, Dunn RL, Litwin MS, Sandler HM, Sanda MG. Development and validation of the expanded prostate cancer index composite (EPIC) for comprehensive assessment of health-related quality of life in men with prostate cancer. Urology. 2000;56(6):899–905.

    Article  CAS  PubMed  Google Scholar 

  48. Watts S, Leydon G, Birch B, Prescott P, Lai L, Eardley S, et al. Depression and anxiety in prostate cancer: a systematic review and meta-analysis of prevalence rates. BMJ Open. 2014;4(3):e003901.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Reinikainen P, Lehtonen M, Lehtinen I, Luukkaala T, Sintonen H, Kellokumpu-Lehtinen PL. Health-related quality of life of patients treated with different fractionation schedules for early prostate cancer compared to the age-standardized general male population. Clin Genitourin Cancer. 2023;21(1):146–54.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We acknowledge the participants and clinicians who contribute data to SA-PCCOC.

Code availability

Software code can be made available on request from the Corresponding author.

Funding

SA-PCCOC receives funding from The Movember Foundation, the Urological Society of Australia and New Zealand (SA-NT section), Genesis Care, Mundi Pharma, The Beat Cancer Initiative and the Hospital Research Foundation. No Funders were involved in this study’s conception, design or reporting.

Author information

Authors and Affiliations

  1. Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, Australia

    Norma B. Bulamu, Michael O’Callaghan & Billingsley Kaambwa

  2. Health and Social Care Economics Group, Caring Futures Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, Australia

    Christine Mpundu-Kaambwa

  3. SA-PCCOC: South Australian Prostate Cancer Clinical Outcomes Collaborative, Flinders Medical Centre, Urology Unit, Adelaide, Australia

    Michael O’Callaghan

  4. Discipline of Medicine, University of Adelaide, Adelaide, Australia

    Michael O’Callaghan

Authors
  1. Norma B. Bulamu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christine Mpundu-Kaambwa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael O’Callaghan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Billingsley Kaambwa
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. MO’C oversaw the data collection, NBB, CMK and BK were responsible for the data analysis. NBB wrote the first draft of the manuscript and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Norma B. Bulamu.

Ethics declarations

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Southern Adelaide Clinical Human Research Ethics Committee (307.14).

Informed consent, in the form of an ethics-approved opt-out recruitment system was obtained from all individual participants included in the study.

Consent for publication

Not applicable. No individual participant data is revealed in this study.

Competing interests

No conflict of interest to declare.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bulamu, N.B., Mpundu-Kaambwa, C., O’Callaghan, M. et al. Responsiveness and construct validity of EPIC-26, AQoL-6D and SF-6D following treatment in prostate cancer. BMC Cancer 23, 297 (2023). https://doi.org/10.1186/s12885-023-10732-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-023-10732-6

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us