Mediation and Moderation: Testing Relationships Between Symptom Status, Functional Health, and Quality of Life in HIV Patients

Abstract

We extended Wilson and Cleary's (1995) health-related quality of life model to examine the relationships among symptom status (Symptoms), functional health (Disability), and quality of life (QOL). Using a community sample (N = 956) of male HIV positive patients, we tested a mediation model in which the relationship between Symptoms and QOL is partially mediated by Disability. Common and unique ideas from 3 approaches to examining moderation of effects in mediational models (Edwards & Lambert, 2007; MacKinnon, 2008; Preacher, Rucker, & Hayes, 2007) were used to test whether (a) the direct relationship of Symptoms to QOL and (b) the relationship of Disability to QOL are moderated by age. In the mediation model, both the direct and the indirect (mediated) effects were significant. The direct relationship of Symptoms to QOL was significantly moderated by age, but the relationship of Disability to QOL was not. High Symptoms were associated with lower QOL at all ages, but this relationship became stronger at older ages. We compare the 3 approaches and consider their advantages over traditional approaches to combining mediation and moderation.

ACKNOWLEDGMENTS

This research was supported by National Institute of Nursing Research Grant NRO4817 to Karen Sousa and National Institute on Drug Abuse Grant DA09757 to David MacKinnon.

An earlier version of this paper was presented at the graduate student preconference, Society for Multivariate Experimental Psychology, Chapel Hill, NC, October, 2007.

Notes

¹ The data were collected from six locations corresponding to large geographic areas (communities) within these cities. We calculated the intraclass correlations for Symptoms, Disability, QOL, and Age. The intraclass correlations ranged from 0.04 to 0.08. We used the Complex procedure in Mplus to produce correct standard errors that take into account this dependency in the data. Because of the concerns about participant anonymity, identification numbers for individual clinics were not typically available in the data set.

² The indirect or mediated effect can be calculated in two ways: (a) difference between total effect (regression of Y on X) and direct effect (regression of Y on X controlling for M) and (b) product of path coefficient from X to M and path coefficient from M to Y. These two methods yield identical estimates of mediated effect when the dependent variable is continuous and ordinary least squares regression is used (MacKinnon, Warsi, & Dwyer, 1995).

³ We thank an anonymous reviewer for suggesting this test.

⁴ Other statistical programs (e.g., SPSS, SAS, LISREL, EQS) can be used to estimate the models presented here. Edwards and Lambert (2007) provide SPSS syntax in their appendix; SPSS macros to perform analyses from Preacher et al. (2007) are available at www.quantpsy.org; and the CD accompanying MacKinnon (2008) contains computer script and outputs from SPSS, SAS, LISREL, EQS, MPLUS, and CALIS programs.