TY - JOUR
T1 - Using group-based trajectory modeling to examine heterogeneity of symptom burden in patients with head and neck cancer undergoing aggressive non-surgical therapy
AU - Shi, Qiuling
AU - Mendoza, Tito R.
AU - Gunn, G. Brandon
AU - Wang, Xin Shelley
AU - Rosenthal, David I.
AU - Cleeland, Charles S.
N1 - Funding Information:
Acknowledgments The project described was supported by awards from the National Institutes of Health: Award Number CA016672, a Cancer Center Support Grant to The University of Texas MD Anderson Cancer Center, and Award Number CA026582 to Charles S. Cleeland, PhD. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. The authors acknowledge the editorial assistance of Jeanie F. Woodruff, BS, ELS.
PY - 2013/11
Y1 - 2013/11
N2 - Purpose: Treatment-related symptom burden varies significantly among patients undergoing radiotherapy or chemoradiotherapy, yet such variation is typically not reflected in the results from single-group studies. We applied groupbased trajectory modeling (GBTM) to describe the heterogeneity of symptom burden among patients with head and neck cancer and to identify subgroups with distinct symptom-development trajectories. Methods: Patients (n = 130) were recruited pretherapy and rated multiple symptoms weekly for 10 weeks via the M. D. Anderson Symptom Inventory. With the mean of five most severe symptoms over time as an outcome measure, GBTM was used to identify patient subgroups with distinct symptom trajectories. Linear mixed-effects modeling (LMM) was applied to compare with GBTM's ability to describe the longitudinal symptom data. Results: The five most severe symptoms were problems with taste, difficulty swallowing or chewing, problems with mucus, fatigue, and dry mouth. A two-group GBTM model identified 68 % of patients as having high symptom burden, associated with older age, worse baseline performance status, and chemoradiotherapy treatment. A four-group GBTM model generated one stable group (4 % of patients) and three groups varying in symptom severity with both linear and quadratic functions over time. LMM revealed symptom-change patterns similar to that produced by GBTM but was inferior in identifying risk factors for high symptom burden. Conclusions: For cancer patients undergoing aggressive therapy, GBTM is capable of identifying various symptomburden trajectories and provides severity groupings that will aid research and may be of clinical utility. These results may be generalizable to other cancer types and treatments.
AB - Purpose: Treatment-related symptom burden varies significantly among patients undergoing radiotherapy or chemoradiotherapy, yet such variation is typically not reflected in the results from single-group studies. We applied groupbased trajectory modeling (GBTM) to describe the heterogeneity of symptom burden among patients with head and neck cancer and to identify subgroups with distinct symptom-development trajectories. Methods: Patients (n = 130) were recruited pretherapy and rated multiple symptoms weekly for 10 weeks via the M. D. Anderson Symptom Inventory. With the mean of five most severe symptoms over time as an outcome measure, GBTM was used to identify patient subgroups with distinct symptom trajectories. Linear mixed-effects modeling (LMM) was applied to compare with GBTM's ability to describe the longitudinal symptom data. Results: The five most severe symptoms were problems with taste, difficulty swallowing or chewing, problems with mucus, fatigue, and dry mouth. A two-group GBTM model identified 68 % of patients as having high symptom burden, associated with older age, worse baseline performance status, and chemoradiotherapy treatment. A four-group GBTM model generated one stable group (4 % of patients) and three groups varying in symptom severity with both linear and quadratic functions over time. LMM revealed symptom-change patterns similar to that produced by GBTM but was inferior in identifying risk factors for high symptom burden. Conclusions: For cancer patients undergoing aggressive therapy, GBTM is capable of identifying various symptomburden trajectories and provides severity groupings that will aid research and may be of clinical utility. These results may be generalizable to other cancer types and treatments.
KW - Group-based trajectory model
KW - Head and neck cancer
KW - MDASI
KW - Symptom burden
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U2 - 10.1007/s11136-013-0380-2
DO - 10.1007/s11136-013-0380-2
M3 - Article
C2 - 23475689
AN - SCOPUS:84892796393
SN - 0962-9343
VL - 22
SP - 2331
EP - 2339
JO - Quality of Life Research
JF - Quality of Life Research
IS - 9
ER -