BCG: Dataset on the Effectiveness of the BCG Vaccine for...

Description Usage Details Source References Examples

Description

This dataset includes 13 studies on the effectiveness of the Bacillus Calmette-Guerin (BCG) vaccine for preventing tuberculosis (see van Houwelingen, Arends, & Stijnen (2002) for details).

Usage

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Details

A list of data with the following structure:

Trial

Number of the trials

Author

Authors of the original studies

Year

Year of publication

VD

Vaccinated group with disease

VWD

Vaccinated group without the disease

NVD

Not vaccinated group with disease

NVWD

Not vaccinated group without the disease

Latitude

Geographic latitude of the place where the study was done

Allocation

Method of treatment allocation

ln_OR

Natural logarithm of the odds ratio: log((VD/VWD)/(NVD/NVWD))

v_ln_OR

Sampling variance of ln_OR: 1/VD+1/VWD+1/NVD+1/NVWD

ln_Odd_V

Natural logarithm of the odds of the vaccinated group: log(VD/VWD)

ln_Odd_NV

Natural logarithm of the odds of the not vaccinated group: log(NVD/NVWD)

v_ln_Odd_V

Sampling variance of ln_Odd_V: 1/VD+1/VWD

cov_V_NV

Sampling covariance between ln_Odd_V and ln_Odd_NV: It is always 0

v_ln_Odd_NV

Sampling variance of ln_Odd_NV: 1/NVD+1/NVWD

Source

Colditz, G. A., Brewer, T. F., Berkey, C. S., Wilson, M. E., Burdick, E., Fineberg, H. V., & Mosteller, F. (1994). Efficacy of BCG vaccine in the prevention of tuberculosis: Meta-analysis of the published literature. Journal of the American Medical Association, 271, 698–702.

References

Berkey, C. S., Hoaglin, D. C., Mosteller, F., & Colditz, G. A. (1995). A random-effects regression model for meta-analysis. Statistics in Medicine, 14, 395–411.

van Houwelingen, H. C., Arends, L. R., & Stijnen, T. (2002). Advanced methods in meta-analysis: Multivariate approach and meta-regression. Statistics in Medicine, 21, 589–624.

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://www.jstatsoft.org/v36/i03/.

Examples

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data(BCG)

## Univariate meta-analysis on the log of the odds ratio
summary( meta(y=ln_OR, v=v_ln_OR, data=BCG,
              x=cbind(scale(Latitude,scale=FALSE),
              scale(Year,scale=FALSE))) )

## Multivariate meta-analysis on the log of the odds
## The conditional sampling covariance is 0
bcg <- meta(y=cbind(ln_Odd_V, ln_Odd_NV), data=BCG,
            v=cbind(v_ln_Odd_V, cov_V_NV, v_ln_Odd_NV))
summary(bcg)

plot(bcg)

Example output

Loading required package: OpenMx
To take full advantage of multiple cores, use:
  mxOption(NULL, 'Number of Threads', parallel::detectCores())
"SLSQP" is set as the default optimizer in OpenMx.
mxOption(NULL, "Gradient algorithm") is set at "central".
mxOption(NULL, "Optimality tolerance") is set at "6.3e-14".
mxOption(NULL, "Gradient iterations") is set at "2".
sh: 1: wc: Permission denied
sh: 1: cannot create /dev/null: Permission denied

Call:
meta(y = ln_OR, v = v_ln_OR, x = cbind(scale(Latitude, scale = FALSE), 
    scale(Year, scale = FALSE)), data = BCG)

95% confidence intervals: z statistic approximation
Coefficients:
             Estimate  Std.Error     lbound     ubound z value  Pr(>|z|)    
Intercept1 -0.7166884  0.0766950 -0.8670079 -0.5663688 -9.3446 < 2.2e-16 ***
Slope1_1   -0.0335019  0.0054079 -0.0441013 -0.0229026 -6.1949 5.831e-10 ***
Slope1_2   -0.0013515  0.0068862 -0.0148483  0.0121453 -0.1963    0.8444    
Tau2_1_1    0.0020944  0.0184838 -0.0341331  0.0383219  0.1133    0.9098    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Q statistic on the homogeneity of effect sizes: 163.1649
Degrees of freedom of the Q statistic: 12
P value of the Q statistic: 0

Explained variances (R2):
                           y1
Tau2 (no predictor)    0.3025
Tau2 (with predictors) 0.0021
R2                     0.9931

Number of studies (or clusters): 13
Number of observed statistics: 13
Number of estimated parameters: 4
Degrees of freedom: 9
-2 log likelihood: 13.89208 
OpenMx status1: 0 ("0" or "1": The optimization is considered fine.
Other values may indicate problems.)

Call:
meta(y = cbind(ln_Odd_V, ln_Odd_NV), v = cbind(v_ln_Odd_V, cov_V_NV, 
    v_ln_Odd_NV), data = BCG)

95% confidence intervals: z statistic approximation
Coefficients:
           Estimate Std.Error   lbound   ubound  z value Pr(>|z|)    
Intercept1 -4.83374   0.34020 -5.50052 -4.16697 -14.2086  < 2e-16 ***
Intercept2 -4.09597   0.43475 -4.94806 -3.24389  -9.4216  < 2e-16 ***
Tau2_1_1    1.43137   0.58304  0.28863  2.57411   2.4550  0.01409 *  
Tau2_2_1    1.75733   0.72425  0.33781  3.17684   2.4264  0.01525 *  
Tau2_2_2    2.40733   0.96742  0.51122  4.30344   2.4884  0.01283 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Q statistic on the homogeneity of effect sizes: 5270.386
Degrees of freedom of the Q statistic: 24
P value of the Q statistic: 0

Heterogeneity indices (based on the estimated Tau2):
                             Estimate
Intercept1: I2 (Q statistic)   0.9887
Intercept2: I2 (Q statistic)   0.9955

Number of studies (or clusters): 13
Number of observed statistics: 26
Number of estimated parameters: 5
Degrees of freedom: 21
-2 log likelihood: 66.17587 
OpenMx status1: 0 ("0" or "1": The optimization is considered fine.
Other values may indicate problems.)

metaSEM documentation built on May 18, 2021, 1:06 a.m.