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Predicting Health-related Quality of Life: Testing the Contextual Model Using Structural Equation Modeling

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Abstract

This study aims to investigate the utility of the Contextual Model of Health-Related Quality of Life (HRQOL) to explain the relationship among the domains of HRQOL with a diverse, population-based sample of breast cancer survivors (BCS). We employed a cross-sectional design to investigate HRQOL among 703 multiethnic, population-based BCS. The study methodology was guided by the Contextual Model of HRQOL. Structural Equation Modeling (SEM) was conducted to assess the hypothesized model. SEM identified significant relationships among the bio-psychological domain (general health status, cancer-related factors, and psychological factors), the cultural-socio-ecological domain (health care satisfaction, socio-ecological factor, and socio-economic status), and HRQOL. The best fitting model indicates direct pathways from ‘general health status’, ‘years since diagnosis’, ‘health care satisfaction’ and ‘socio-ecological factor’ to ‘HRQOL’ variables. Additionally, ‘socio-ecological factor’ and ‘socio-economic status’ variables were indirectly associated with HRQOL through ‘general health status’. Findings suggest that the Contextual Model of HRQOL adds valid factors to explain overall HRQOL and increases our understanding of the socio-ecological dimensions predicting HRQOL outcomes. The revelation of inter-relations among the dimensions of HRQOL may inform the translational and clinical utility of the HRQOL construct.

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Notes

  1. Small sample size contributes to over-fitting, which means that some of the relationships that appear statistically significant are actually just noise (Tomarken and Waller 2003). In general, Bentler and Chou (1987) allow as few as five cases per parameter estimate (including error terms as well as path coefficients) as a total of sample size for SEM. In the current study, the total of parameter is 57, thus we require at least 285 samples (5 × 57). As a result, we don’t have concerns about over-fitting, considering our total sample size (N = 703).

  2. Latent variables are referred to as factors or constructs (Kline 1998).

  3. As an observed variable that is presumed to measure a latent variable, it may be called a manifest variable or, more commonly, an indicator (Kline 1998).

Abbreviations

HRQOL:

Health-related quality of life

BCS:

Breast cancer survivors

SEM:

Structural equation modeling

SES:

Socio-economic status

FACT:

The functional assessment of cancer therapy

SF-36:

The RAND 36-item health survey

ADQ:

Adherence determinants questionnaire

RMSEA:

Root mean square error of approximation

CFI:

The comparative fit index

AIC:

The akaike information criterion

CFI:

Confirmatory factor analysis

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Acknowledgements

Funding for this research was supported by a grant from the Department of Defense 17-99-1-9106.

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Authors and Affiliations

  1. Center of Community Alliance for Research and Education (CCARE), Division of Population Sciences, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, 91010-3000, USA

    Kimlin T. Ashing-Giwa & Jung-Won Lim

  2. UCLA School of Medicine, Los Angeles, CA, USA

    Kimlin T. Ashing-Giwa

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  1. Kimlin T. Ashing-Giwa
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  2. Jung-Won Lim
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Correspondence to Jung-Won Lim.

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Ashing-Giwa, K.T., Lim, JW. Predicting Health-related Quality of Life: Testing the Contextual Model Using Structural Equation Modeling. Applied Research Quality Life 3, 215–230 (2008). https://doi.org/10.1007/s11482-009-9057-y

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