Different methods to assess quality of life from multiple follow-ups in a longitudinal asthma study.
Academic Article
Overview
abstract
BACKGROUND AND OBJECTIVE: Serial measurements obtained during observational longitudinal studies offer the opportunity to describe the effects of chronic diseases on patient-centered outcomes such as quality of life. The purpose of this study was to assess serial Asthma Quality of Life Questionnaire (AQLQ) and SF-36 scores against a transition item using three methods of data analysis-final minus initial scores, maximum minus minimum scores, and regression line slopes through all scores. METHODS: Using receiver operating characteristic (ROC) curves, each method of analysis was compared against patients' responses to a global transition question about change in asthma status with responses dichotomized as "stayed the same or got worse" or "improved." A total of 185 patients, mean age 41+/-11 years, 83% women, completed the AQLQ and SF-36 three to seven times at approximately 8-month intervals over a mean of 24.8+/-3.9 months. For the AQLQ, all three methods of data analysis performed well against the transition item with ROC areas highest for the symptoms, activities, and the summary AQLQ scores (0.74-0.78). RESULTS: Overall, ROC areas increased as the number of observations increased, ranging from 0.78 to 0.93 for the AQLQ summary score for patients with three to six or more assessments, respectively (P =.02). As part of the AQLQ, patients cited specific activities in which they were limited because of asthma. A total of 66 different activities were cited, including limitations in stair climbing, walking, interacting with others, sleeping, and working. In ROC analysis, serial measurements of these items also performed well against the transition item with areas ranging from 0.72 to 0.75 for all three methods of analysis. In contrast, ROC areas for the SF-36 Physical and Mental Component Summary scores were significantly lower, ranging from 0.59 to 0.66 compared to the AQLQ areas, indicating that the generic scale was less responsive than the disease-specific scale (P< or = .01). The three different methods of analysis also provided unique information about the cohort. The final minus initial analysis showed that 63% of patients had clinically important improvements, the maximum minus minimum analysis showed that over 90% of patients had fluctuations in scores that were clinically important, and the slope analysis showed that 79% of patients had an overall trend of improvement. CONCLUSIONS: This study described possible methods to analyze and present serial data. Additional techniques to assess and interpret serial longitudinal data are needed to comprehensively describe long-term effects of chronic diseases on quality of life.
