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Cancer-related fatigue in breast cancer patients: factor mixture models with continuous non-normal distributions

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Abstract

Objective

Fatigue is one of the most prevalent and significant symptoms experienced by breast cancer patients. This study aimed to investigate potential population heterogeneity in fatigue symptoms of the patients using the innovative non-normal mixture modeling.

Methods

A sample of 197 breast cancer patients completed the brief fatigue inventory and other measures on cancer symptoms. Non-normal factor mixture models were analyzed and compared using the normal, t, skew-normal, and skew-t distributions. Selection of the number of latent classes was based on the Bayesian information criterion (BIC). The identified classes were validated by comparing their demographic profiles, clinical characteristics, and cancer symptoms using a stepwise distal outcome approach.

Results

The observed fatigue items displayed slight skewness but evident negative kurtosis. Factor mixture models using the normal distribution pointed to a 3-class solution. The t distribution mixture models showed the lowest BIC for the 2-class model. The restored class (52.5 %) exhibited moderate severity (item mean = 2.8–3.2) and low interference (item mean = 1.1–1.9). The exhausted class (47.5 %) displayed high levels of fatigue severity and interference (item mean = 5.8–6.6). Compared to the restored class, the exhausted class reported significantly higher perceived stress, anxiety, depression, pain, sleep disturbance, and lower quality of life.

Conclusions

The non-normal factor mixture models suggest two distinct subgroups of patients on their fatigue symptoms. The presence of the exhausted class with exacerbated symptoms calls for a proactive assessment of the symptoms and development of tailored interventions for this subgroup.

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Acknowledgments

We would like to thank Hong Kong Cancer Fund, Queen Mary Hospital, Pamela Youde Nethersole Eastern Hospital, and Dr. M.Y. Luk for their help in patient recruitment and all the patients who participated in the study. The paper would not have been possible without the expert opinions and generous help offered by Dr. Bengt Muthén and the non-normal mixture modeling recently implemented in Mplus version 7.2.

Conflict of interest

The authors report no conflict of interests. The first author (R.T.H.H.) was supported by the Research Grants Council General Research Fund (HKU745110H) for this study.

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Authors and Affiliations

  1. Department of Social Work and Social Administration, Centre on Behavioral Health, The University of Hong Kong, 2/F, Hong Kong Jockey Club Building for Interdisciplinary Research, 5 Sassoon Road, Pokfulam, Hong Kong SAR

    Rainbow T. H. Ho, Ted C. T. Fong & Irene K. M. Cheung

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  1. Rainbow T. H. Ho
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  2. Ted C. T. Fong
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  3. Irene K. M. Cheung
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Correspondence to Ted C. T. Fong.

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Ho, R.T.H., Fong, T.C.T. & Cheung, I.K.M. Cancer-related fatigue in breast cancer patients: factor mixture models with continuous non-normal distributions. Qual Life Res 23, 2909–2916 (2014). https://doi.org/10.1007/s11136-014-0731-7

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