Abstract
Background: 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.
Objectives: 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.
Methods: 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.
Results: 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.
Discussion: 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.