Agreement was not found in adolescents’ quality of life rated by parents and adolescents

Abstract

Objective

Limited evidence examines agreement between the ratings of adolescents’ health-related quality of life (HRQOL) by parents and adolescents especially accounting for measurement properties. This study aimed to test whether agreement exists between the dyads using a methodology of measurement invariance.

Study Design and Setting

We conducted a telephone survey to collect data from parents and their adolescents enrolled in Florida’s Children’s Medical Services (376 pairs) using the PedsQL 4.0. We used multi-group confirmatory factor analysis to test measurement invariance, including configural (equivalent HRQOL construct), metric (equivalent item–domain associations), scalar (equivalent starting value of ratings), and residual (equivalent nonsystematic measurement errors of ratings) invariance. We also investigated correlates of discrepancies in the dyadic ratings.

Results

There were equivalent HRQOL constructs and item–domain associations between the dyads. However, some items show different starting values and nonsystematic errors in the dyadic ratings. After adjusting for noninvariant items, adolescents reported significant higher HRQOL scores than parents in all domains (P < 0.05). Parents’ rating of adolescents’ health significantly contributed to discrepancies in the dyadic ratings (P < 0.05).

Conclusions

Adolescents rated HRQOL higher than their parents. This discrepancy was associated with severe health conditions. Without assuring measurement invariance, comparisons of the dyadic HRQOL ratings can be misleading.

1. Introduction

Pediatric health-related quality of life (HRQOL) is an important indicator, complementing objective clinical measures, to assess pediatric health outcomes [1,2]. Parents’ ratings of pediatric HRQOL are often used as proxies, especially if children are too young or cognitively impaired to understand HRQOL concepts [3,4]. The use of a parent’s rating, however, raises the question of what level of agreement and discrepancy exists between the dyadic HRQOL reports [4–6].

A recent review suggests that the agreement between parents’ and children’s HRQOL ratings is inconsistent [7]. Some studies show that parents report higher HRQOL scores than do their children across all domains [6,8]. However, others show that compared to children’s self-ratings, parents tend to report better physical and social functioning, but more depression and bodily pain [7]. In contrast to children’s own ratings, parents may underestimate HRQOL for sick children [9], but overestimate for healthy children [6,10].

In extant studies, parents’ and children’s pediatric HRQOL ratings are often compared using t-tests or regression methods [6,11–14]. Methodologically, these comparisons are limited because they cannot assure whether HRQOL measures between the dyads are comparable. It is possible that parents and children perceive the construct of HRQOL or meanings of specific items differently. If we are unable to assure the measurement constructs and metrics between the parents’ and children’s ratings are comparable (i.e., measurement invariance), our dyadic comparisons will be meaningless and interpretations may be misleading [15,16].

Meredith proposed an analytic framework explicitly testing whether measurements between groups are invariant, and included the components of configural, metric, scalar, and residual measurement invariance [15]. Configural invariance means the conceptual frameworks between parents’ and children’s assessments of pediatric HRQOL are equivalent. Metric invariance means, given configural invariance, the associations of items with the underlying HRQOL domain are equivalent in dyads. Scalar metric means, given metric invariance, parents’ and children’s ratings of the item are based on the same starting value. Residual invariance means, given scalar invariance, parents and children are subject to the same form of nonsystematic measurement errors in item response. In HRQOL studies, the application of measurement invariance methods is limited [16,17]. Most of the HRQOL studies merely emphasized the metric invariance [18–22] although residual invariance is a necessary condition for meaningful comparisons among groups [23–25].

The overall aim of this study was to investigate measurement invariance in adolescents’ HRQOL rated by parents and their adolescents. We focused on adolescents between 15 and 18 years who have special health care needs. Our selection of this age group is important because they are involving in “transition planning,” where providers begin to assist adolescents in making more independent decision making, in conjunction with their parents, about their health care when adolescents are becoming young adults [26]. During the transition-planning phase, input of adolescents’ HRQOL can be useful to promote shared decision making with the provider and to plan care. We used Meredith’s analytic framework [15] to test measurement invariance. We also examined, after adjusting for noninvariant items, whether the discrepancy persists between the dyadic ratings. Finally, we investigated the correlates contributing to the discrepancy in the dyadic ratings.

2. Methods

2.1. Data collection, sample, and data sources

This is a cross-sectional study using data collected from the parents and their adolescents enrolled in 2005 Florida’s Children’s Medical Services Network program. Children’s Medical Services Network is Florida’s Title V Program for Children with Special Health Care Needs and is designed to provide specialty health care for children who are less than 19 years old. All children in this sample were also enrolled in Medicaid.

We identified 700 families and adolescents from enrollment files maintained by Children’s Medical Services Network. We conducted a telephone survey using a statewide random sample of families that had an adolescent 15 through 18 years old and lived with them between 12/2005 and 03/2006. Multiple callbacks (at maximum of 10 times) were performed if phone numbers were busy or not answered. Twenty-five percent of parents reported that their children were physically or mentally unable to complete the survey and 6% refused to allow their adolescents to be interviewed. Additionally, 4% of the cases adolescents subsequently refused to participate or their parents refused after initially granting permission. The overall response rate was about 65%, which is similar to other telephone surveys with Medicaid populations [27,28]. The study sample consisted of 376 matched pairs of parents and their adolescents who completed the survey. However, 12 dyads that did not complete HRQOL survey or had over 50% of missing items were further excluded, leaving 364 dyads for final analyses.

Florida’s Agency for Health Care Administration provided claims and encounter data for these children for the year preceding the survey. The claims and encounter data were used to classify the children’s health status. Specifically, we used the clinical risk groups (CRGs) systems [29–31], an ICD-9-CM-based severity classification system that groups diagnoses into the following categories based on predicted resource consumption: (1) nonacute health care needs, (2) significant acute conditions, (3) minor chronic conditions (e.g., attention deficit/hyperactivity disorder), (4) moderate chronic conditions (e.g., asthma), or (5) major chronic conditions (e.g., cystic fibrosis) [29].

2.2. HRQOL measure

We used the PedsQL 4.0 to assess the child’s HRQOL [32,33]. An adolescent self-report and a parent proxy-report module, designed for adolescents between 13 and 18 years old, was used. The PedsQL consists of 23 items measuring four domains—physical functioning (eight items), emotional functioning (five items), social functioning (five items), and school functioning (five items). Each item has five response categories. A specific domain score is calculated for corresponding items, ranging from 0 (worst HRQOL) to 100 (best HRQOL). The PedsQL has acceptable psychometric properties and is suitable for healthy children and children with acute and chronic conditions [33,34].

2.3. An analytic framework for testing measurement invariance

HRQOL is a latent variable that cannot be directly observed, but can be measured using observed variables (i.e., designed items). A latent variable is assumed to influence a subject’s response to the designed items. We applied the methodology of multiple-group confirmatory factor analysis (MG-CFA) to test measurement invariance in HRQOL measure across groups. We conducted serial tests of nested models, which began with the less constrained model (i.e., configural invariance), then sequentially placed cross-group equality constraints on the parameters, and ended up with the most constrained model (i.e., residual invariance) (see Fig. 1) [15,35].

Fig. 1.

Tests of measurement invariance in the framework of multi-group confirmatory factor analysis. (a) Configure invariance, (b) Metric invariance, (c) Scalar invariance, and (d) Residual invariance. Where, Y, item score; η, domain (or factor) score; λ, factor loading; ζ, intercept of item score; γ, residuals.

Table 1 shows the interpretations for invariance tests using MG-CFA. Specifically, configural measurement invariance means that the conceptual frameworks of HRQOL used by parents and adolescents to rate the adolescent’s HRQOL are equivalent. MG-CFA investigates this invariance by testing equality in the item clusters of a specific HRQOL domain between the dyads.

Table 1.

Taxonomy of measurement invariance

Measurement invariance	Interpretations	Statistical tests in multiple-group confirmatory factor analysis
Configural invariance	There is an equivalent HRQOL construct between two groups	Whether item clusters are identical between two groups?
Metric invariance	Given configure invariance, there are equivalent associations between items and underlying HRQOL between two groups (or the same unit of measurement)	Given configure invariance, whether factor loadings of specific items are identical between two groups?
Scalar invariance	Given metric invariance, there are equivalent starting values of item ratings between two groups	Given metric invariance, whether intercepts of specific items are identical between two groups?
Residual invariance	Given scalar invariance, there are equivalent nonsystematic measurement errors in the item responses between two groups	Given scalar invariance, whether residuals of specific items are identical between two groups?

Metric measurement invariance means, given the configural invariance, the magnitudes of the associations between items and underlying HRQOL domain perceived by parents and adolescents are comparable. If metric invariance is achieved, the measurement units for the dyadic HRQOL ratings are equivalent because the differences in item responses associated with the differences in domain scores are equal between the two groups. MG-CFA investigates this invariance by testing equality in factor loadings of items between the dyads.

Scalar measurement invariance means, given the metric invariance, the item scores rated by parents and adolescents are based on the same starting value. It is possible that parents and adolescents may report different scores for the same items, in part due to differential acquiescence response style (e.g., higher expectation by parents for their child’s health than adolescents themselves) [36,37]. MG-CFA investigates this invariance by testing equality in the intercepts of item ratings between the dyads.

Residual measurement invariance means, given the scalar invariance, parents and adolescents have a similar degree of nonsystematic measurement errors in item responses. Very often, residuals of the item responses are correlated rather than conditionally independent. Therefore, the group differences cannot be truly attributed to differences in the common HRQOL factor [15,23].MG-CFA investigates this invariance by testing equality in the residuals of item responses between the dyads.

Several indicators were applied to interpret the goodness of model fit for the MG-CFA, including change in Satorra–Bentler scaled χ²(ΔSBχ²), root mean square error of approximation (RMSEA), comparative fit index (CFI), and change in CFI (ΔCFI) [38]. A model showing nonsignificant ΔSBχ², RMSEA ≤ 0.06, CFI ≥ 0.95, and ΔCFI ≤ 0.01 (i.e., the decrease of CFI for a more constrained vs. a less constrained model ≤0.01) will be considered as an acceptable model fit. It means that we should not reject the null hypothesis of measurement invariance between the dyads.

2.4. Partial measurement invariance

We specifically applied the concept of partial measurement invariance in the tests [24,39–41]. If all items in a HRQOL domain fail to demonstrate a specific level of measurement invariance (e.g., metric noninvariance), we sequentially removed the equality constraint on the parameter for each item between two groups in the MG-CFA to assess the degree of model improvement. If partial invariance was present, we calibrated items (i.e., allowing parameters of noninvariant items acting differently between groups, but treating parameters of invariant items equally between groups in MG-CFA) to calculate latent domain scores for parents’ and adolescents’ HRQOL.

2.5. Dyadic differences in HRQOL ratings

We compared the discrepancies in the dyadic ratings of HRQOL before and after adjusting for items with measurement noninvariance. The magnitude of the discrepancy was measured using effect size (defined as the difference in the dyadic HRQOL scores divided by a polled SD of both groups). We defined the magnitudes of < 0.2, 0.2–0.49, 0.5–0.79 and ≥0.8 as negligible, small, moderate, and large effect size, respectively [42,43].

We compared parents’ and adolescents’ domain scores through the Generalized Estimating Equations regression to explicitly addresses the influence of correlated dyadic HRQOL scores in the calculation of standard errors of regression coefficients [44]. We also investigated which factors were significantly associated with HRQOL ratings and the discrepancy in the dyadic ratings, including adolescents’ age, gender, race (White, Black, Hispanic, and others), health status, and parents’ level of education [4,45,46]. We measured adolescents’ health status using CRGs and parents’ self-reports (a single item with an option of excellent, very good, good, fair or poor). The use of a single item to measure health status has been shown to have good psychometric properties [47,48]. We conducted measurement invariance tests using Mplus [49] and other analyses using STATA 9.0 [50].

3. Results

3.1. Characteristics of subjects

Of the 364 matched pairs of parents and adolescents analyzed in this study, the mean age of parents and adolescents were 47 (SD = 9.9) and 17 (SD = 1.1) years, respectively. Fifty-one percent of adolescents were boys. For adolescents’ race/ethnicity, 37% were white, 40% were black, 18% were Hispanics, and 6% were other. Thirty-two percent of parents had an education level below high school, 37% had a high school education, and 31% had some college, associate degree, or above. For parent-reported adolescent’s health status, 13% were excellent, 18% were very good, 33% were good, 28% were fair, and 8% were poor. For CRGs, 23% of adolescents had nonacute health care needs, 5% had significant acute conditions, 10% had minor chronic conditions, 34% had moderate chronic conditions, and 28% had major chronic conditions.

3.2. Measurement invariance tests

Table 2 shows the findings of measurement invariance tests. The first model (M1), which places equality constraints of item clusters on HRQOL domains across two groups, reveals acceptable model fit in all domains. This suggests that the configural invariance of HRQOL measurements was held between the dyads.

Table 2.

Tests of measurement invariance in four domains of the PedsQL 4.0

Models of MI of testing	Reference model	ΔSBχ2	P-value of ΔSBχ2	RMSEA	CFI	ΔCFI	No (%) of items with MI
Physical
M1: Configural	—	—	—	0.06	0.97	—	8 (100%)
M2: Metric	M1	3.48	0.75	0.05	0.97	0	8 (100%)
M3: Scalar	M2	99.24	<0.05	0.08	0.92	0.05	—
M4: Partial scalar	M2	0.20	0.90	0.05	0.97	0	3 (38%)
M5: Residual	M4	54.38	<0.05	0.08	0.93	0.04	—
M6: Partial residual	M4	3.13	0.37	0.05	0.97	0	4 (50%)
Emotional
M1: Configural	—	—	—	0.05	0.99	—	5 (100%)
M2: Metric	M1	6.10	0.19	0.04	0.99	0	5 (100%)
M3: Scalar	M2	13.74	<0.05	0.05	0.98	0.01	—
M4: Partial scalar	M2	2.26	0.32	0.04	0.99	0	3 (60%)
M5: Residual	M4	12.4	<0.05	0.05	0.98	0.01	—
M6: Partial residual	M4	3.55	0.47	0.04	0.99	0	4 (80%)
Social
M1: Configural	—	—	—	0.06	0.97	—	5 (100%)
M2: Metric	M1	4.59	0.33	0.06	0.97	0	5 (100%)
M3: Scalar	M2	30.76	<0.05	0.08	0.95	0.02	—
M4: Partial scalar	M2	0.61	0.44	0.06	0.97	0	2 (40%)
M5: Residual	M4	39.23	<0.05	0.09	0.92	0.05	—
M6: Partial residual	M4	5.28	0.15	0.06	0.97	0	3 (60%)
School
M1: Configural	—	—	—	0.02	0.99	—	5 (100%)
M2: Metric	M1	1.31	0.96	0.01	1.00	−0.01	5 (100%)
M3: Scalar	M2	18.19	<0.05	0.04	0.98	0.02	—
M4: Partial scalar	M2	4.13	0.13	0.01	1.00	0	3 (60%)
M5: Residual	M4	0.32	0.99	0.01	1.00	0	5 (100%)

Abbreviation: MI, measurement invariance.

Poor model fits were flagged in italic.

The second model (M2), which adds equality constraints of factor loadings of items on HRQOL domains to model 1, reveals acceptable model fit in all domains. This suggests that the metric invariance was held and the dyadic ratings of HRQOL were based on the same unit of measurement.

The third model (M3) shows, after additionally imposing equality constraints on the intercepts of items between two groups, the scalar invariance was rejected by all HRQOL domains. The P-values of ΔSBχ² were < 0.05 for all domains; RMSEAs were >0.06 for physical and social functioning; CFI was < 0.95 for physical functioning; and ΔCFIs were >0.01 for all domains. After freely estimating the intercepts of items with noninvariance (5, 2, 3, and 2 items in physical, emotional, social, and school functioning, respectively), the fourth model (M4) shows acceptable model fit. This suggests that there was a partial scalar invariance for HRQOL measurements between the dyads.

The fifth model (M5) shows, by additionally imposing equality constraints on the residuals of item ratings between the two groups, the residual invariance was rejected by three domains: physical, emotional, and social functioning. The P-values of ΔSBχ² were < 0.05 for all three domains; RMSEAs were >0.06, and CFIs were < 0.95 for physical and social functioning; ΔCFIs were >0.01 for all three domains. After freely estimating the residuals of noninvariant items (4, 1, and 2 items for physical, emotional, and social functioning, respectively), the sixth model (M6) shows acceptable model fit. This suggests that there was partial residual invariance for HRQOL measurements between the dyads. Those items identified with measurement noninvariance are shown in the Appendix.

3.3. Discrepancies in the dyadic HRQOL ratings before correlate adjustment

Table 3 shows the discrepancies in parents’ and adolescents’ HRQOL ratings. Before assuring measurement invariance, adolescents rated HRQOL higher than their parents in all domains (all with P < 0.001). The effect sizes in discrepancies across all domains were larger than 0.3, a cutoff indicating a minimally important difference [51,52]. The magnitudes in the discrepancies were small for school and emotional functioning (0.31 and 0.33, respectively), but moderate for physical and school functioning (0.54 and 0.72, respectively).

Table 3.

Mean (SD) and the discrepancy in adolescents’ HRQOL rated by the dyads^a

	Parent	Adolescent	Differenceb	Effect sizec
Before adjusting noninvariancd
Physical	66.5 (26.5)	77.9 (21.3)	11.5 (22.9)	0.54
Emotional	66.7 (22.9)	74.1 (22.4)	7.4 (25.5)	0.33
Social	66.1 (25.7)	81.0 (20.7)	14.7 (25.6)	0.72
School	59.9 (22.9)	65.9 (19.3)	5.7 (23.6)	0.31
After adjusting noninvariancee
Physical	73.7 (27.9)	82.3 (21.9)	8.6 (23.2)	0.40
Emotional	77.2 (15.7)	85.3 (15.5)	8.1 (17.6)	0.53
Social	70.5 (20.0)	83.0 (16.2)	12.4 (20.8)	0.77
School	60.5 (27.1)	74.1 (19.5)	13.6 (26.8)	0.70

^aUnadjustment for covariates.

^bAll discrepancy in HRQOL ratings between the dyads were statistically significance (P < 0.001).

^cMagnitude of the effect size: negligible (<0.2), small (0.2~0.49), moderate (0.5~0.79), and large (≥0.8).

^dHRQOL ratings unadjusted for measurement noninvariance.

^eHRQOL ratings adjusted for measurement noninvariance.

After adjusting for items with measurement noninvariance (see Section 2.4), adolescents also demonstrated higher HRQOL ratings compared to parents in all domains (all with P < 0.001). The effect sizes in discrepancies across all domains were larger than 0.3, indicating a minimally important difference. However, the magnitudes of the discrepancies were expanded, especially for emotional and school functioning.

3.4. Discrepancies in the dyadic HRQOL ratings after correlate adjustment

Table 4 shows the discrepancy in HRQOL after adjusting for items with measurement noninvariance and controlling for covariates (adolescent’s age, gender, race, health status, and parent’s level of education). We found that adolescents still demonstrated higher HRQOL ratings compared to parents in all domains (all with P < 0.001). The magnitudes of effect size were small for physical functioning (0.38), but moderate for emotional, school, and social functioning (0.50, 0.66, and 0.73, respectively).

Table 4.

Discrepancy in adolescents’ HRQOL rated by the dyads after adjusting for covariates

	Physical functioning	Emotional functioning	Social functioning	School functioning
Groupa	8.12*** (0.38)b	7.71*** (0.50)b	11.84*** (0.73)b	12.94*** (0.66)b
Age of child	−0.86	−0.44	0.62	1.43
Gender of childc	6.48**	4.43**	4.02**	−1.97
Race of child
Black	7.38**	2.40	4.45**	2.84
Others	3.84	0.26	−1.47	2.15
Health status report by parentsd
Very good	−1.21	−3.07	−2.67	−4.22
Good	−9.77**	−6.16***	−6.04*	−7.89*
Fair	−13.93***	−9.58***	−6.42*	−9.69*
Poor	−18.51***	−15.12***	−8.29*	−10.98*
Health status by CRGse
Acute	3.18	3.89	4.86	8.33
Mild chronic	2.59	−3.26	−5.53	−9.41*
Moderate chronic	−3 13	−1.56	−2.86	−2.59
Severe chronic	−11.76***	−2.05	−1.37	−2.55

^*P < 0.05;

^**P < 0.01;

^***P < 0.001.

^aParent is the reference group.

^bValue in parenthesis is the effect size: negligible (<0.2), small (0.2~0.49), moderate (0.5~0.79), and large (≥0.8).

^cGirl is the reference group.

^dExcellent health is the reference group.

^eHealthy is the reference group.

Adolescents’ gender and parent-reported health status were significantly correlated with HRQOL ratings in all domains. Compared to girls, boys had significantly higher HRQOL scores in all domains (P < 0.01), except school functioning. Adolescents with more impaired parent-reported health status had significantly lower HRQOL scores in all domains than healthy adolescents. This association was obvious for physical and emotional functioning (P < 0.001). Health status as classified by the CRGs system was less significantly associated with HRQOL ratings than parent-reported health status.

3.5. Correlates of discrepancy in the dyadic HRQOL ratings

Parents’ report of their child’s health status was the only variable significantly associated with the discrepancies in the dyadic ratings of HRQOL. Figure 2 shows that when the levels of parent-reported adolescent health status were more impaired, the discrepancies in the dyadic HRQOL ratings increased. This pattern was obvious for physical, social, and emotional functioning. For example, the effect sizes in physical functioning were large (above 0.80) for poor health status, and moderate (0.5–0.8) for fair health status.

Fig. 2.

Discrepancy in adolescents’ HRQOL rated by the dyads vs. level of health status. Effect size: negligible (< 0.2), small (0.2~0.49), moderate (0.5~0.79), and large (≥0.8).

4. Discussion

In this study, we demonstrated pediatric HRQOL measured by the PedsQL was not invariant between parents and adolescents. Although all items across all domains show equivalent HRQOL structure (configural invariance) and equivalent magnitude in the association between the items and underlying HRQOL (metric invariance), only some items show equivalent starting value in item ratings and equivalent nonsystematic measurement errors in item responses. We also demonstrated that after adjusting for items with measurement noninvariance, HRQOL across all domains were rated more poorly by parents than their adolescents. Parents’ rating of adolescent health, especially those with more impaired health, was the most important predictor for the discrepancies in the dyadic ratings.

Our findings are consistent with earlier studies showing a higher level of discrepancy in the dyadic ratings when items ask about abstract concepts (e.g., emotional well being) or when the dyads have access to different information (e.g., social and school activities) [4,5,7]. The possible interpretations for the discrepancy in the dyadic ratings can be due to the fact that parents and adolescents may interpret the same item in different ways [53]. One qualitative study reported that for items relevant to psychosocial functioning (e.g., asking “happy at school” or “friends help each other”), children related these items to one simple example/setting in their daily life, whereas parents often referred to several examples/settings [37]. Second, the discrepancy may reflect different expectations by parents and children about the child’s health status. It means, parents tend to incorporate concerns and worries about their child’s health conditions in the HRQOL ratings, and perceive an illness to have more negative consequences than their child experiences [54,55]. This phenomenon is especially significant for children with life-limiting conditions; for example, parents show greater concerns for the future impact of pediatric cancer, whereas children show greater concerns about the immediate consequence, such as lose of friends or hair [54].

We suggest that parents’ and adolescents’ ratings of adolescents’ HRQOL may provide different information, which should be considered as complementary rather than substitutive. The use of dyadic reports are important because we found the discrepancies in the dyadic ratings were statistically significant (in all domains), and the effect sizes in the discrepancies were meaningfully important difference with the range between small (physical functioning) and moderate (other domains). The combined use of the dyadic ratings is particularly important for children with severe health conditions because we found that the discrepancy in the dyadic ratings was largest for children with more severe health conditions. Parents’ perceptions of children’s HRQOL determine the utilization of health services, and both parents’ and adolescents’ ratings influence the decision-making process with providers for treatments [34,56,57]. Psychometric evidence also shows that parents’ and children’s reports have different merits. Parents’ ratings are expected to have greater reliability, whereas children’s ratings are likely to have greater validity [58,59].

Although we recommend the dual use of parent’s and adolescent’s HRQOL ratings, the time required for both parents and adolescents to complete the forms may limit its application in busy clinical settings. In addition, clinicians may be unclear about how to manage potentially disparate scores derived from the dyadic ratings. The use of item response theory to develop item banks containing measurement invariant items, further combined with computerized adaptive test technology, has the potential to provide a solution [60,61]. Instead of completing the entire set of items, this methodology guides parents and adolescents in selecting a subset of items from item banks to better represent the adolescent’s HRQOL. As a result, the dyadic HRQOL ratings can be efficient and precise, both vitally important in busy clinical practice. Because the items used by parents and adolescents are selected from the same metric (a calibrated item bank), clinicians can interpret and manage the estimated scores easily (e.g., taking an average score) when planning care.

Our study has limitations that merit attention. First, this study is restricted to children who were between 15 through 18 years old with health conditions of sufficient severity to warrant enrollment in the State Title V Children with Special Health Care Needs Program. This limits the generalizability of our findings to other pediatric populations. Second, we only tested measurement invariance in HRQOL using the PedsQL. Its items and domains, however, may be different from other pediatric HRQOL instruments [62]. Third, we did not explicitly control for parents’ mental health status, which may confound the dyadic comparisons [63–65]. Finally, our methods for measurement invariance tests can be improved. In this study, we treated item response as a continuous variable. Further studies should specify item response as a categorical variable in the MG-CFA tests.

5. Conclusions

Measurement invariance is a necessary condition for meaningful comparisons of pediatric HRQOL rated by parents and children. Using the PedsQL, we demonstrated that some items show measurement noninvariance. After adjusting for items with noninvariant features, adolescents significantly reported higher HRQOL scores than their parents in all domains. Given the fact that there were significant discrepancies in the dyadic ratings, and both parents and children provide the most comprehensive picture regarding the impacts of disease on children’s well being, we recommend the use of dyadic HRQOL ratings to better measure children’s HRQOL.

What is new?

1. Key findings:

   • Agreements between the ratings of adolescents’ health-related quality of life (HRQOL) by parents and adolescents were poor.
   • Adolescents reported significantly higher HRQOL scores than parents in the domains of physical, emotional, social, and school functioning.
2. What this adds to the existing literature:

   • Standard statistical methods (e.g., t-test or linear regression) used to compare adolescents’ HRQOL as reported by parents and adolescents are limited because they cannot assure whether measurements between the dyads are comparable (i.e., measurement noninvariance). This lack of comparability between adolescents and parents might be due to differences in perceived constructs of HRQOL or item meanings.
   • This study demonstrated the use of multi-group confirmatory factor analysis to detect inconsistent measurement properties in HRQOL ratings and provided solutions to adjust this measurement issue. These adjustments will allow for meaningful comparisons of adolescents’ HRQOL by parents and adolescents.
3. What is the implication:

   • Given the poor agreement in adolescents’ HRQOL as reported by parents and adolescents, this study suggests that dyadic ratings should be used rather than parents’ or adolescents’ ratings alone because both may provide different, yet valuable information.

Appendix

Items of the PedsQL show measurement noninvariance

	Scalar noninvariance	Residual noninvariance
Physical functioning
1. Walking more than one block
2. Running	X	X
3. Participating in sports activity or exercise
4. Lifting something heavy		X
5. Taking a bath or shower by him or herself	X	X
6. Doing chores around the house	X	X
7. Having hurts or aches	X
8. Low energy level	X
Emotional functioning
9. Feeling afraid or scared		X
10. Feeling sad or blue
11. Feeling angry
12. Trouble sleeping	X
13. Worrying about what will happen to him or her	X
Social functioning
14. Getting along with other teens		X
15. Other teens not wanting to be his or her friend	X
16. Getting teased by other teens
17. Not able to do things that other teens his or her age can do	X
18. Keeping up with other teens	X	X
School functioning
19. Paying attention in class
20. Forgetting things	X
21. Keeping up with schoolwork
22. Missing school because of not feeling well
23. Missing school to go to the doctor or hospital	X

All items show configural and metric invariance.