dat.debruin2009.RdResults from 13 trials providing information about standard care quality and HAART-adherence in control groups.
dat.debruin2009The data frame contains the following columns:
| author | character | (first) author of study |
| year | numeric | publication year |
| scq | numeric | standard care quality |
| ni | numeric | number of patients in the standard care group |
| xi | numeric | number of patients with an undetectable viral load in standard care group |
| mi | numeric | number of patients with a detectable viral load in standard care group |
| ethnicity | character | dominant ethnicity of the patients in the standard care group |
| patients | character | inclusion of patients continuing or starting (a new) treatment |
| select | character | baseline selection of patients with adherence problems or no selection |
| sens | character | sensitivity of viral load assessments (<400 vs. >=400 copies/ml) |
Highly active antiretroviral therapy (HAART) refers to a combination of multiple antiretroviral drugs that can effectively suppress the HIV virus. However, achieving viral suppression (to the point that the virus becomes essentially undetectable in a blood sample) requires high levels of adherence to an often complicated medication regimen. A number of trials have examined various interventions that aim to increase adherence levels. In each trial, patients receiving the intervention are compared to patients in a control group receiving standard care (often referred to as ‘care as usual’). However, the quality of standard care can vary substantially between these studies. de Bruin et al. (2009) assessed the quality of standard care provided (based on a quantification of the number of behavior change techniques applied) and examined to what extent the quality of standard care was related to the proportion of patients achieving effective viral suppression in the control groups.
de Bruin, M., Viechtbauer, W., Hospers, H. J., Schaalma, H. P., & Kok, G. (2009). Standard care quality determines treatment outcomes in control groups of HAART-adherence intervention studies: Implications for the interpretation and comparison of intervention effects. Health Psychology, 28(6), 668–674. https://doi.org/10.1037/a0015989
psychology, medicine, proportions, single-arm studies, meta-regression
### copy data into 'dat' and examine data
dat <- dat.debruin2009
dat
#> author year scq ni xi mi ethnicity patients select sens
#> 1 Andrade et al. 2005 21.00 29 11 18 other starting no <400
#> 2 Fairley et al. 2003 22.67 33 24 9 caucasian continuing yes <400
#> 3 Goujard et al. 2003 15.07 326 179 147 caucasian continuing no <400
#> 4 Holzemer et al. 2006 3.67 240 82 158 other continuing no 400
#> 5 Knobel et al. 1999 8.60 110 60 50 caucasian starting no <400
#> 6 Murphy et al. 2002 27.80 29 11 18 other continuing yes 400
#> 7 Pradier et al. 2003 4.80 244 99 145 caucasian continuing no <400
#> 8 Rathbun et al. 2005 12.07 17 9 8 caucasian starting no <400
#> 9 Remien et al. 2005 20.00 215 88 127 other continuing yes <400
#> 10 Tuldra et al. 2000 7.47 26 17 9 caucasian starting no 400
#> 11 van Servellen 2005 7.80 138 26 112 other continuing yes 400
#> 12 Wagner et al. 2006 10.47 55 23 32 other starting yes 400
#> 13 Wohl et al. 2006 22.80 54 40 14 other starting no 400
### load metafor package
library(metafor)
### calculate proportions and corresponding sampling variances
dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat)
dat
#>
#> author year scq ni xi mi ethnicity patients select sens yi vi
#> 1 Andrade et al. 2005 21.00 29 11 18 other starting no <400 0.3793 0.0081
#> 2 Fairley et al. 2003 22.67 33 24 9 caucasian continuing yes <400 0.7273 0.0060
#> 3 Goujard et al. 2003 15.07 326 179 147 caucasian continuing no <400 0.5491 0.0008
#> 4 Holzemer et al. 2006 3.67 240 82 158 other continuing no 400 0.3417 0.0009
#> 5 Knobel et al. 1999 8.60 110 60 50 caucasian starting no <400 0.5455 0.0023
#> 6 Murphy et al. 2002 27.80 29 11 18 other continuing yes 400 0.3793 0.0081
#> 7 Pradier et al. 2003 4.80 244 99 145 caucasian continuing no <400 0.4057 0.0010
#> 8 Rathbun et al. 2005 12.07 17 9 8 caucasian starting no <400 0.5294 0.0147
#> 9 Remien et al. 2005 20.00 215 88 127 other continuing yes <400 0.4093 0.0011
#> 10 Tuldra et al. 2000 7.47 26 17 9 caucasian starting no 400 0.6538 0.0087
#> 11 van Servellen 2005 7.80 138 26 112 other continuing yes 400 0.1884 0.0011
#> 12 Wagner et al. 2006 10.47 55 23 32 other starting yes 400 0.4182 0.0044
#> 13 Wohl et al. 2006 22.80 54 40 14 other starting no 400 0.7407 0.0036
#>
### random-effects model
res <- rma(yi, vi, data=dat)
print(res, digits=2)
#>
#> Random-Effects Model (k = 13; tau^2 estimator: REML)
#>
#> tau^2 (estimated amount of total heterogeneity): 0.02 (SE = 0.01)
#> tau (square root of estimated tau^2 value): 0.15
#> I^2 (total heterogeneity / total variability): 91.77%
#> H^2 (total variability / sampling variability): 12.14
#>
#> Test for Heterogeneity:
#> Q(df = 12) = 135.51, p-val < .01
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> 0.48 0.05 10.45 <.01 0.39 0.57 ***
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
### mixed-effects meta-regression model with all predictors/covariates
res <- rma(yi, vi, mods = ~ scq + ethnicity + patients + select + sens, data=dat)
print(res, digits=3)
#>
#> Mixed-Effects Model (k = 13; tau^2 estimator: REML)
#>
#> tau^2 (estimated amount of residual heterogeneity): 0.007 (SE = 0.006)
#> tau (square root of estimated tau^2 value): 0.085
#> I^2 (residual heterogeneity / unaccounted variability): 72.43%
#> H^2 (unaccounted variability / sampling variability): 3.63
#> R^2 (amount of heterogeneity accounted for): 68.55%
#>
#> Test for Residual Heterogeneity:
#> QE(df = 7) = 22.309, p-val = 0.002
#>
#> Test of Moderators (coefficients 2:6):
#> QM(df = 5) = 21.884, p-val < .001
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> intrcpt 0.458 0.087 5.243 <.001 0.287 0.629 ***
#> scq 0.014 0.005 3.168 0.002 0.005 0.023 **
#> ethnicityother -0.231 0.084 -2.759 0.006 -0.395 -0.067 **
#> patientsstarting 0.081 0.066 1.230 0.219 -0.048 0.209
#> selectyes -0.073 0.075 -0.983 0.325 -0.219 0.073
#> sens<400 -0.104 0.080 -1.307 0.191 -0.261 0.052
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
### mixed-effects meta-regression model with scq and ethnicity as predictors/covariates
res <- rma(yi, vi, mods = ~ scq + ethnicity, data=dat)
print(res, digits=3)
#>
#> Mixed-Effects Model (k = 13; tau^2 estimator: REML)
#>
#> tau^2 (estimated amount of residual heterogeneity): 0.012 (SE = 0.007)
#> tau (square root of estimated tau^2 value): 0.109
#> I^2 (residual heterogeneity / unaccounted variability): 83.24%
#> H^2 (unaccounted variability / sampling variability): 5.97
#> R^2 (amount of heterogeneity accounted for): 48.39%
#>
#> Test for Residual Heterogeneity:
#> QE(df = 10) = 53.525, p-val < .001
#>
#> Test of Moderators (coefficients 2:3):
#> QM(df = 2) = 10.333, p-val = 0.006
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> intrcpt 0.430 0.075 5.698 <.001 0.282 0.577 ***
#> scq 0.011 0.005 2.324 0.020 0.002 0.020 *
#> ethnicityother -0.196 0.072 -2.725 0.006 -0.337 -0.055 **
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>