Table displays the differences in the distributions of baseline covariates between the HAART users and HAART-naïve groups before and after matching. Prior to propensity score matching, the distributions of risk factors affecting HAART initiation were compared between 1,271 HAART exposed (the last visits before matching) and 555 HAART naïve participants (at candidate matching visits). Thirteen out of the 24 background covariates, including education level, race/ethnicity, income, insurance, CD4+ cell counts, viral load, AIDS diagnosis, number of symptoms, outpatient visits and medications, physical functioning, perceived health index and health rating, were significantly different between the groups, which necessitated the matching of these covariates in our study. Using a tolerance of 0.1% in the propensity score, we were able to obtain 458 matched pairs of HAART initiators and HAART naïve women. No statistically significant differences were observed for any of these background covariates after matching (Table ), which demonstrated a success in matching the covariates as expected. The resulting distributions of propensity scores for the two groups before and after matching are displayed in Figure . Before matching, the average propensity scores for HAART using and naïve groups were 0.42 and 0.22 respectively. However, after propensity score matching, the distributions of propensity scores were nearly identical (mean: 0.36; standard deviation: 0.17 for both groups).
Study Participant Characteristics Before and After Propensity Score Matching. Numbers indicate mean value unless otherwise noted.
Boxplots of QOL summary score between HAART users and HAART naïve groups before and after propensity-score matching. Box widths are proportional to the number of observations in each group.
The 916 matched participants had a mean age of 38.5 years at baseline and contributed a total of 4,292 person visits, with a median follow-up time of 4 years (interquartile range (IQR): 1–6 years). Among these women, about 58% were Black, non-Hispanics, 60% completed high school and 42% had an AIDS history at the matching visits. At baseline, the average CD4+ cell count was approximately 340 cells/mm3, the mean viral load was approximately 10,000 copies/ml and the mean QOL summary score was 62. About 63% of HAART naïve women dropped during the first two years, while the percentage was only 11% for women using HAART. In contrast, only 11% of HAART naïve women were followed for 6 or more years whereas the percentage for the women using HAART was 38%.
To evaluate how HAART affected QOL change, we fit a series of pattern mixture models with different subsets of covariates (Table ). In each model, HAART use and time from matching visits were included. We first examined whether there were any significant interactions between time and HAART use to assess any long-term HAART effect on QOL score changes. As the interaction terms were not statistically significant in any model (though its direction was positive), it was dropped out from our analyses. Then, we evaluated the overall effect of HAART on changes of QOL scores (the summary score and nine specific QOL domain scores) without time varying intermediate variables (models 1–2) and the direct effects of HAART after adjusting for different possible mediating covariates (models 3–5).
Estimates of the Impact of HAART on the Mean Change in QOL Scores from Propensity-Score Matched Pattern Mixture Models.
Compared with the HAART naïve group in the bivariate model (Model 1) with HAART use and time as the only covariates, the HAART users had improved QOL scores from the matching visits for almost all domains except for energy/fatigue, with those for role functioning (mean change: 5.08; P = 0.01), social functioning (4.33; P = 0.01), pain (4.53; P = 0.01) and perceived health index (4.25; P < 0.01) reaching a statistically significant level. A second model (Model 2) was fit by adding fixed personal characteristics, including age at baseline, race/ethnicity and education at study enrollment, into the bivariate model. The model estimates for HAART and time changed slightly except for cognitive functioning, which became statistically significant (3.51; P = 0.02). In Model 3, we included time-dependent socioeconomic variables – income, employment and health insurance into the Model 2. No significant change of HAART effect was observed. After further adding markers for disease progression (CD4+ cell counts and HIV viral load) as in the Model 4, the HAART effects remained stable except for health perception (3.43; P = 0.04). In the final model (Model 5), the clinical variables (number of symptoms, outpatient visits, hospitalizations and medications, history of AIDS diagnosis and clinical depression) were added as covariates into the Model 4. Except for cognitive functioning, health perception and perceived health index, adding clinical variables into the models was associated with biggest changes in HAART effect estimates. In addition, the direct HAART effect on summary QOL change became significant (3.25; P = 0.02). Furthermore, though the QOL scores decreased over time for almost all domains in all models, only the decreases of summary QOL, role functioning, emotional well-being and health perception were statistically significant in the final model after controlling many time varying covariates.
As the HIV-infected individuals at different disease stages might have different responses to HAART, we further examined the association of HAART and QOL among women who were AIDS-free at the matching visits (Table ). Again, all QOL domain scores remained stable or decreased (for health perception) during follow-up, and HAART use did not modify these trends. Compared to the Table , fewer QOL domains were significant for short-term HAART effects (social function, pain and health rating) and it was negative for the energy/fatigue domain.
Estimates of the Impact of HAART on the Mean Change in QOL Scores from Propensity-Score Matched Pattern Mixture Models among AIDS-free Women at Matching Visits
In addition to HAART use and time, a number of the covariates were significantly associated with QOL changes from baseline. Evaluating the results from Model 5 for the summary QOL change, women having less than high school education had slightly higher summary QOL change (3.12; P = 0.02) compared to women with college education at study enrollment. In addition, all clinical variables were significantly associated with summary QOL change. Having one more symptom, outpatient visit, hospitalization or medication was associated with a decrease of 2.17 (P < 0.01), 0.11 (P < 0.02), 1.57 (P < 0.01) or 0.24 (P < 0.01) in summary QOL change respectively. Depression was strongly related to a decline in summary QOL change (-9.78; P < 0.01), while having a history of clinical AIDS was associated with improved QOL change (2.13; P = 0.04). All other demographic, socioeconomic and biological (CD4+ cell counts and HIV viral load) variables were not significantly associated with QOL changes from baseline.