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Translational methods in biostatistics: linear mixed effect regression models of alcohol consumption and HIV disease progression over time.

Finucane MM, Samet JH, Horton NJ - Epidemiol Perspect Innov (2007)

Bottom Line: We apply the linear mixed effects model to a previously published dataset of HIV infected individuals with a history of alcohol problems who are receiving HAART (n = 197).The researchers were interested in determining the effect of alcohol use on HIV disease progression over time.A significant interaction between alcohol use and adherence to HAART is found: subjects who use alcohol and are not fully adherent to their HIV medications had higher log RNA (ribonucleic acid) viral load levels than fully adherent non-drinkers, fully adherent alcohol users, and non-drinkers who were not fully adherent.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics and Statistics, Smith College, Northampton, MA 01063, USA.

ABSTRACT
Longitudinal studies are helpful in understanding how subtle associations between factors of interest change over time. Our goal is to apply statistical methods which are appropriate for analyzing longitudinal data to a repeated measures epidemiological study as a tutorial in the appropriate use and interpretation of random effects models. To motivate their use, we study the association of alcohol consumption on markers of HIV disease progression in an observational cohort. To make valid inferences, the association among measurements correlated within a subject must be taken into account. We describe a linear mixed effects regression framework that accounts for the clustering of longitudinal data and that can be fit using standard statistical software. We apply the linear mixed effects model to a previously published dataset of HIV infected individuals with a history of alcohol problems who are receiving HAART (n = 197). The researchers were interested in determining the effect of alcohol use on HIV disease progression over time. Fitting a linear mixed effects multiple regression model with a random intercept and random slope for each subject accounts for the association of observations within subjects and yields parameters interpretable as in ordinary multiple regression. A significant interaction between alcohol use and adherence to HAART is found: subjects who use alcohol and are not fully adherent to their HIV medications had higher log RNA (ribonucleic acid) viral load levels than fully adherent non-drinkers, fully adherent alcohol users, and non-drinkers who were not fully adherent. Longitudinal studies are increasingly common in epidemiological research. Software routines that account for correlation between repeated measures using linear mixed effects methods are now generally available and straightforward to utilize. These models allow the relaxation of assumptions needed for approaches such as repeated measures ANOVA, and should be routinely incorporated into the analysis of cohort studies.

No MeSH data available.


Related in: MedlinePlus

Hypothetical observed and predicted lines for two subjects from random intercept and random slope model.
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Figure 2: Hypothetical observed and predicted lines for two subjects from random intercept and random slope model.

Mentions: In Figure 2, we use an illustration due to Fitzmaurice et al. [9] to demonstrate how this model could be applied in a simple example. In this example, as shown on the left side of the Figure, person A's measurements are consistently higher than person B. Thus, person A would have a positive random intercept term (bA > 0) whereas person B's random intercept would be negative (bB < 0). The individuals' observed responses are allowed to vary randomly above and below their conditional mean trajectories because of the inclusion of the error terms eij. (In this example, eA1 is positive whereas eA2 is negative.) By averaging over these random effects, we obtain the marginal mean response trajectory, M, the predicted outcome trajectory for an 'average' subject in the population, described using the fixed effects parameters.


Translational methods in biostatistics: linear mixed effect regression models of alcohol consumption and HIV disease progression over time.

Finucane MM, Samet JH, Horton NJ - Epidemiol Perspect Innov (2007)

Hypothetical observed and predicted lines for two subjects from random intercept and random slope model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2147003&req=5

Figure 2: Hypothetical observed and predicted lines for two subjects from random intercept and random slope model.
Mentions: In Figure 2, we use an illustration due to Fitzmaurice et al. [9] to demonstrate how this model could be applied in a simple example. In this example, as shown on the left side of the Figure, person A's measurements are consistently higher than person B. Thus, person A would have a positive random intercept term (bA > 0) whereas person B's random intercept would be negative (bB < 0). The individuals' observed responses are allowed to vary randomly above and below their conditional mean trajectories because of the inclusion of the error terms eij. (In this example, eA1 is positive whereas eA2 is negative.) By averaging over these random effects, we obtain the marginal mean response trajectory, M, the predicted outcome trajectory for an 'average' subject in the population, described using the fixed effects parameters.

Bottom Line: We apply the linear mixed effects model to a previously published dataset of HIV infected individuals with a history of alcohol problems who are receiving HAART (n = 197).The researchers were interested in determining the effect of alcohol use on HIV disease progression over time.A significant interaction between alcohol use and adherence to HAART is found: subjects who use alcohol and are not fully adherent to their HIV medications had higher log RNA (ribonucleic acid) viral load levels than fully adherent non-drinkers, fully adherent alcohol users, and non-drinkers who were not fully adherent.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics and Statistics, Smith College, Northampton, MA 01063, USA.

ABSTRACT
Longitudinal studies are helpful in understanding how subtle associations between factors of interest change over time. Our goal is to apply statistical methods which are appropriate for analyzing longitudinal data to a repeated measures epidemiological study as a tutorial in the appropriate use and interpretation of random effects models. To motivate their use, we study the association of alcohol consumption on markers of HIV disease progression in an observational cohort. To make valid inferences, the association among measurements correlated within a subject must be taken into account. We describe a linear mixed effects regression framework that accounts for the clustering of longitudinal data and that can be fit using standard statistical software. We apply the linear mixed effects model to a previously published dataset of HIV infected individuals with a history of alcohol problems who are receiving HAART (n = 197). The researchers were interested in determining the effect of alcohol use on HIV disease progression over time. Fitting a linear mixed effects multiple regression model with a random intercept and random slope for each subject accounts for the association of observations within subjects and yields parameters interpretable as in ordinary multiple regression. A significant interaction between alcohol use and adherence to HAART is found: subjects who use alcohol and are not fully adherent to their HIV medications had higher log RNA (ribonucleic acid) viral load levels than fully adherent non-drinkers, fully adherent alcohol users, and non-drinkers who were not fully adherent. Longitudinal studies are increasingly common in epidemiological research. Software routines that account for correlation between repeated measures using linear mixed effects methods are now generally available and straightforward to utilize. These models allow the relaxation of assumptions needed for approaches such as repeated measures ANOVA, and should be routinely incorporated into the analysis of cohort studies.

No MeSH data available.


Related in: MedlinePlus