Validation of general linear modeling for identifying factors associated with Quality of Life: A comparison with structural equation modeling ()
Abstract
Purpose: General linear modeling (GLM) is usually applied to investigate factors associated with the domains of Quality of Life (QOL). A summation score in a specific sub-domain is regressed by a statistical model including factors that are associated with the sub-domain. However, using the summation score ignores the influence of individual questions. Structural equation modeling (SEM) can account for the influence of each question’s score by compositing a latent variable from each question of a sub-domain. The objective of this study is to determine whether a conventional approach such as GLM, with its use of the summation score, is valid from the standpoint of the SEM approach. Method: We used the Japanese version of the Maugeri Foundation Respiratory Failure Questionnaire, a QOL measure, on 94 patients with heart failure. The daily activity sub-domain of the questionnaire was selected together with its four accompanying factors, namely, living together, occupation, gender, and the New York Heart Association’s cardiac function scale (NYHA). The association level between individual factors and the daily activity sub-domain was estimated using SEM and GLM, respectively. The standard partial regression coefficients of GLM and standardized path coefficients of SEM were compared. If these coefficients were similar (absolute value of the difference <0.05), we concluded that GLM was valid, as well as the SEM approach. Results: The estimates of living together were －0.06 and －0.07 for the GLM and SEM. Likewise, the estimates of occupation, gender, and NYHA were －0.18 and －0.20, －0.08 and －0.08, 0.51 and 0.54, respectively. The absolute values of the difference for each factor were 0.01, 0.02, 0.00, and 0.03, respectively. All differences were less than 0.05. This means that these two approaches lead to similar conclusions. Conclusion: GLM is a valid method for exploring association factors with a domain in QOL.
1. INTRODUCTION
In medical treatment, QOL has been defined as a personal sense of well-being and a multidimensional factor that generally includes physical, psychological, social, and spiritual dimensions or domains [1]. The distinctive feature of the research objectives of QOL is that the focus is typically on broad questions [2]. These questions are made up of multiple scales, such as the binary scale, with “yes or no” questions, graded scales including options such as, “very bad,” “bad,” “average,” “good,” and “very good”; as well as continuous scales such as the Visual Analogue Scale (VAS).
For a variety of QOL questionnaires, the general linear model, such as analysis of variance, is typically used to identify factors that are associated with a certain domain of QOL. Examples of these include research on the identification of a domain and related factors among HIVpositive individuals, as well as correlation studies on asymptomatic vertebral fractures and quality of life [3,4]. However, general liner modeling (GLM) uses the summative score obtained from scores on each question in a given sub-domain. This is because GLM cannot be used with multiple response variables. However, using the summation score ignores the influence of individual questions. In contrast, structural equation modeling (SEM) can deal with multiple responses and accounts for the influence of each question’s score by compositing a latent variable from each question of a domain. The objective of this study is to determine the validity of a conventional approach involving the use of the summation score and GLM, as compared to the SEM approach.
2. METHODS
2.1. Materials and Subjects
2.1.1. Materials
The Japanese version of the Maugeri Foundation Respiratory Failure (MRF-28) Questionnaire is a 28-item, disease-specific, health-related QOL questionnaire for patients with chronic respiratory failure due to pulmonary diseases. The questionnaire is self-administered and easy to complete, with all items requiring either a “yes” or “no” answer [5]. It consists of four domains, namely, daily activity, cognitive function, invalidity, “other,” and two general questions about the patient’s health status [5].
2.1.2. Subjects
The sample included in-patients and out-patients with symptomatic and previous, asymptomatic heart failure at the University of Toyama Hospital in Japan. Participants were recruited between December 2005 and November 2006. The study was approved by the Ethics Committee at the University of Toyama; all the participants provided written, informed consent to take part [5]. We used this database. A total of 94 subjects enrolled for this study.
2.2. Independent Variables and Response Variables
For this study, we used one of four domains of the MRF-28 questionnaire as a response variable, namely, the daily activity domain (See Table 1). In addition, we used four factors as independent variables, namely, living together (cohabitation status), occupation, gender, and the New York Heart Association’s cardiac function scale (NYHA). The associations between the daily activity domain and the four factors were estimated using GLM and SEM. The daily activity domain consists of 11 questions that require a “yes” or “no” answer. “Yes” was assigned a score of 1, while “no” was assigned a score of 0. More “yes” answers indicated a greater burden from daily activity. A summation score was obtained from adding the scores on all 11 questions. With regard to living together, individuals staying with someone obtained a score of 1, while those living alone obtained 0. Currently employed individuals obtained a score of 1, while the unemployed obtained 0. Males were assigned a score of 1, while females were assigned a score of 0. Scores on the NYHA were divided into two groups; Class 2 was assigned a score of 0, while Class 3 and 4 were each assigned a score of 1. These were shown in Table 2 as Patent Characteristics.
Table 1. The 11 items in daily activity domain—Maugeri Foundation Respiratory Failure Questionnaire (MRF-28).
2.3. Statistical Analysis
2.3.1. GLM
GLM was a special case of SEM and could be expressed as Figure 1. The summation score was regressed by a model that included four factors, namely, living together, occupation, gender, and NYHA. Standard partial regression coefficients were estimated.
2.3.2. SEM
We plotted a path from the latent variable to each question and made a latent variable of daily activity (See
Figure 2); the association with a latent variable was estimated on the basis of Kendall’s correlation. The goodness-of-fit of the SEM was evaluated using the Standardized Root Mean Square Residual (SRMR) (good