Symptom clusters reflect the person's experience of multiple cooccurring symptoms. Although a variety of statistical methods are available to address the clustering of symptoms, latent transition analysis (LTA) characterizes patient membership in classes defined by the symptom experience and captures changes in class membership over time.
The purposes of this article are to demonstrate the application of LTA to cancer symptom data and to discuss the advantages and disadvantages of LTA relative to other methods of managing and interpreting data on multiple symptoms.
Data from a total of 495 adult cancer patients who participated in randomized clinical trials of two symptom management interventions were analyzed. Eight cancer- and treatment-related symptoms reflected the symptom experience. Latent transition analysis was employed to identify symptom classes and evaluate changes in symptom class membership from baseline to the end of the interventions.
Three classes, “A (mild symptoms),” “B (physical symptoms),” and “C (physical and emotional symptoms),” were identified. Class A patients had less comorbidity, better physical and emotional role effect, and better physical function than the other classes did. The number of symptoms, general health perceptions, and social functioning were significantly different across the three classes and were poorest in Class C. Emotional role functioning was poorest in Class C. Older adults were more likely to be in Class B than younger adults were. Younger adults were more likely to be in Class C (p < .01). Among patients in Class C at baseline, 41.8% and 29.0%, respectively, transitioned to Classes A and B at the end of the interventions.
These results demonstrate that symptom class membership characterizes differences in the patient symptom experience, function, and quality of life. Changes in class membership represent longitudinal changes in the course of symptom management. Latent class analysis overcomes the problem of multiple statistical testing that separately addresses each symptom.
Sangchoon Jeon, PhD, is Research Scientist, Division of Acute Care and Health Systems, Yale School of Nursing, West Haven, Connecticut.
Alla Sikorskii, PhD, is Professor, Department of Psychiatry & Statistics and Probability, Michigan State University, East Lansing.
Barbara Given, PhD, RN, FAAN, is Professor Emeritus, College of Nursing, Michigan State University, East Lansing.
Charles W. Given, PhD, is Professor Emeritus, Department of Family Medicine, Michigan State University, East Lansing.
Nancy S. Redeker, PhD, RN, FAHA, FAAN, is the Beatrice Renfield Professor of Nursing, Division of Acute Care and Health Systems, Yale School of Nursing, and Professor, Department of Internal Medicine, Yale School of Medicine, West Haven, Connecticut.
Accepted for publication July 27, 2018.
This research was supported by two National Cancer Institute (NCI) grants (R01CA030724 and R01CA79280) to Drs. Barbara and Bill Given and a National Institute of Nursing Research (NINR) grant (5P20NR014126) to Dr. Redeker.
The institutional review board (IRB) of the Michigan State University approved the Family Home Care Study (IRB 03-247) and the Automated Telephone Monitoring Symptom Management (IRB 03-242).
Automated Telephone Monitoring for Symptom Management (Clinical Trials.gov identifier: NCT00799084, https://clinicaltrials.gov/ct2/show/NCT00799084) and Family Home Care for Cancer: A Community-Based Model (Clinical Trials.gov identifier: NCT00006253, https://clinicaltrials.gov/ct2/show/NCT00006253?term=Family+Home+Care+for+Cancer%3A+A.+Community-Based+Model&rank=1).
The authors have no conflicts of interest to disclose.
Corresponding author: Nancy S. Redeker, PhD, RN, FAHA, FAAN, Yale University School of Nursing, P.O. Box 27399, West Haven, CT 06516 (e-mail: email@example.com).