Full analysis

Forest plot

Funnel plot

Egger’s test

## [1] "No replication of infants across vaccines or significant vaccine effect, therefore combined test performed"
## 
## Regression Test for Funnel Plot Asymmetry
## 
## model:     mixed-effects meta-regression model
## predictor: standard error
## 
## test for funnel plot asymmetry: z = 8.2020, p < .0001

Meta-analysis output

## 
## Random-Effects Model (k = 6; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc  
##  -3.6799    7.3597   11.3597   10.5786   17.3597  
## 
## tau^2 (estimated amount of total heterogeneity): 0.2230 (SE = 0.1506)
## tau (square root of estimated tau^2 value):      0.4722
## I^2 (total heterogeneity / total variability):   99.15%
## H^2 (total variability / sampling variability):  118.18
## 
## Test for Heterogeneity: 
## Q(df = 5) = 97.1976, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub   
##   0.4311  0.1995  2.1615  0.0307  0.0402  0.8221  *
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Meta-regression output

## [1] "Insufficient studies for meta-regression (either <2 studies or only 1 vaccine type)"

Intervention-specific outputs

Summary of cholera studies

## [1] "Insufficient studies (n<2)"

Summary of rotavirus studies

## [1] "Insufficient studies (n<2)"

Summary of PV3 studies

## 
## Random-Effects Model (k = 6; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc  
##  -3.6799    7.3597   11.3597   10.5786   17.3597  
## 
## tau^2 (estimated amount of total heterogeneity): 0.2230 (SE = 0.1506)
## tau (square root of estimated tau^2 value):      0.4722
## I^2 (total heterogeneity / total variability):   99.15%
## H^2 (total variability / sampling variability):  118.18
## 
## Test for Heterogeneity: 
## Q(df = 5) = 97.1976, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub   
##   0.4311  0.1995  2.1615  0.0307  0.0402  0.8221  *
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Meta-regression: secondary moderators

Age group

## [1] "No variation in moderator among studies"

Income setting

## 
##  low_lowermiddle uppermiddle_high 
##                2                4
## 
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.2792 (SE = 0.2099)
## tau (square root of estimated tau^2 value):             0.5284
## I^2 (residual heterogeneity / unaccounted variability): 99.18%
## H^2 (unaccounted variability / sampling variability):   122.26
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity: 
## QE(df = 4) = 80.0459, p-val < .0001
## 
## Test of Moderators (coefficient(s) 2): 
## QM(df = 1) = 0.2373, p-val = 0.6261
## 
## Model Results:
## 
##                                       estimate      se    zval    pval
## intrcpt                                 0.2902  0.3764  0.7708  0.4408
## factor(Income_group)uppermiddle_high    0.2270  0.4660  0.4872  0.6261
##                                         ci.lb   ci.ub   
## intrcpt                               -0.4476  1.0279   
## factor(Income_group)uppermiddle_high  -0.6863  1.1403   
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Background immunogenicity (seroconversion rate in the control group)

## 
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0117 (SE = 0.0120)
## tau (square root of estimated tau^2 value):             0.1081
## I^2 (residual heterogeneity / unaccounted variability): 81.51%
## H^2 (unaccounted variability / sampling variability):   5.41
## R^2 (amount of heterogeneity accounted for):            94.76%
## 
## Test for Residual Heterogeneity: 
## QE(df = 4) = 22.0549, p-val = 0.0002
## 
## Test of Moderators (coefficient(s) 2): 
## QM(df = 1) = 35.9598, p-val < .0001
## 
## Model Results:
## 
##                                      estimate      se     zval    pval
## intrcpt                                1.3892  0.1994   6.9680  <.0001
## asin(sqrt(Baseline_seroconversion))   -1.0354  0.1727  -5.9967  <.0001
##                                        ci.lb    ci.ub     
## intrcpt                               0.9984   1.7800  ***
## asin(sqrt(Baseline_seroconversion))  -1.3738  -0.6970  ***
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

OPV-specific analysis

Forest plot

Funnel plot

Egger’s test

Infants replicated across vaccines, therefore separate tests performed

## [1] "OPV1:"
## 
## Regression Test for Funnel Plot Asymmetry
## 
## model:     mixed-effects meta-regression model
## predictor: standard error
## 
## test for funnel plot asymmetry: z = 2.8675, p = 0.0041
## [1] "OPV2:"
## [1] "Insufficient studies (n<3)"
## [1] "OPV3:"
## 
## Regression Test for Funnel Plot Asymmetry
## 
## model:     mixed-effects meta-regression model
## predictor: standard error
## 
## test for funnel plot asymmetry: z = 8.2020, p < .0001

Meta-analysis output

## 
## Multivariate Meta-Analysis Model (k = 14; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc  
##  -0.1142    0.2284    4.2284    5.3583    5.4284  
## 
## Variance Components: 
## 
##             estim    sqrt  nlvls  fixed     factor
## sigma^2    0.1132  0.3364      9     no  Reference
## 
## Test for Heterogeneity: 
## Q(df = 13) = 207.3903, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub     
##   0.4136  0.1190  3.4751  0.0005  0.1803  0.6469  ***
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Meta-regression output

## 
## Multivariate Meta-Analysis Model (k = 14; method: REML)
## 
## Variance Components: 
## 
##             estim    sqrt  nlvls  fixed     factor
## sigma^2    0.1134  0.3367      9     no  Reference
## 
## Test for Residual Heterogeneity: 
## QE(df = 12) = 203.5819, p-val < .0001
## 
## Test of Moderators (coefficient(s) 2): 
## QM(df = 1) = 0.0076, p-val = 0.9303
## 
## Model Results:
## 
##                         estimate      se    zval    pval    ci.lb   ci.ub
## intrcpt                   0.4130  0.1194  3.4601  0.0005   0.1791  0.6470
## Measure_of_SCOPV3 N-AB    0.0017  0.0198  0.0874  0.9303  -0.0371  0.0406
##                            
## intrcpt                 ***
## Measure_of_SCOPV3 N-AB     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Serotype-specific outputs

Summary of PV1 studies

## 
## Random-Effects Model (k = 8; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc  
##  -0.8036    1.6071    5.6071    5.4990    8.6071  
## 
## tau^2 (estimated amount of total heterogeneity): 0.0611 (SE = 0.0399)
## tau (square root of estimated tau^2 value):      0.2472
## I^2 (total heterogeneity / total variability):   96.26%
## H^2 (total variability / sampling variability):  26.74
## 
## Test for Heterogeneity: 
## Q(df = 7) = 106.3843, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub     
##   0.3610  0.0976  3.7008  0.0002  0.1698  0.5522  ***
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Summary of PV2 studies

## [1] "Insufficient studies (n<2)"

Summary of PV3 studies

## 
## Random-Effects Model (k = 6; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc  
##  -3.6799    7.3597   11.3597   10.5786   17.3597  
## 
## tau^2 (estimated amount of total heterogeneity): 0.2230 (SE = 0.1506)
## tau (square root of estimated tau^2 value):      0.4722
## I^2 (total heterogeneity / total variability):   99.15%
## H^2 (total variability / sampling variability):  118.18
## 
## Test for Heterogeneity: 
## Q(df = 5) = 97.1976, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub   
##   0.4311  0.1995  2.1615  0.0307  0.0402  0.8221  *
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1