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Breastfeeding_withheld.html
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<h1 class="title toc-ignore">Breastfeeding withheld</h1>
</div>
<div id="full-analysis" class="section level2">
<h2>Full analysis</h2>
<div id="forest-plot" class="section level3">
<h3>Forest plot</h3>
<p><img src="Breastfeeding_withheld_files/figure-html/unnamed-chunk-3-1.png" width="1248" /></p>
</div>
<div id="funnel-plot" class="section level3">
<h3>Funnel plot</h3>
<p><img src="Breastfeeding_withheld_files/figure-html/unnamed-chunk-4-1.png" width="576" /></p>
</div>
<div id="eggers-test" class="section level3">
<h3>Egger’s test</h3>
<pre><code>## [1] "No replication of infants across vaccines or significant vaccine effect, therefore combined test performed"</code></pre>
<pre><code>##
## Regression Test for Funnel Plot Asymmetry
##
## model: mixed-effects meta-regression model
## predictor: standard error
##
## test for funnel plot asymmetry: z = -0.9152, p = 0.3601</code></pre>
</div>
<div id="meta-analysis-output" class="section level3">
<h3>Meta-analysis output</h3>
<pre><code>##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -0.9362 1.8723 5.8723 5.4558 9.8723
##
## tau^2 (estimated amount of total heterogeneity): 0.0481 (SE = 0.0452)
## tau (square root of estimated tau^2 value): 0.2192
## I^2 (total heterogeneity / total variability): 63.04%
## H^2 (total variability / sampling variability): 2.71
##
## Test for Heterogeneity:
## Q(df = 6) = 15.7046, p-val = 0.0154
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0743 0.1061 -0.7002 0.4838 -0.2824 0.1337
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
<div id="meta-regression-output" class="section level3">
<h3>Meta-regression output</h3>
<pre><code>##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0473 (SE = 0.0529)
## tau (square root of estimated tau^2 value): 0.2176
## I^2 (residual heterogeneity / unaccounted variability): 64.46%
## H^2 (unaccounted variability / sampling variability): 2.81
## R^2 (amount of heterogeneity accounted for): 1.50%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 11.2386, p-val = 0.0240
##
## Test of Moderators (coefficient(s) 2:3):
## QM(df = 2) = 1.9685, p-val = 0.3737
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1623 0.2002 0.8104 0.4177 -0.2302 0.5547
## VaccinePolio -0.2722 0.3753 -0.7251 0.4684 -1.0078 0.4635
## VaccineRotavirus -0.3377 0.2416 -1.3979 0.1621 -0.8112 0.1358
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
<div id="intervention-specific-outputs" class="section level3">
<h3>Intervention-specific outputs</h3>
<div id="summary-of-cholera-studies" class="section level4">
<h4>Summary of cholera studies</h4>
<pre><code>##
## Random-Effects Model (k = 2; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -0.1356 0.2711 4.2711 0.2711 16.2711
##
## tau^2 (estimated amount of total heterogeneity): 0.0433 (SE = 0.1086)
## tau (square root of estimated tau^2 value): 0.2080
## I^2 (total heterogeneity / total variability): 56.34%
## H^2 (total variability / sampling variability): 2.29
##
## Test for Heterogeneity:
## Q(df = 1) = 2.2903, p-val = 0.1302
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1632 0.1950 0.8370 0.4026 -0.2190 0.5454
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
<div id="summary-of-rotavirus-studies" class="section level4">
<h4>Summary of rotavirus studies</h4>
<pre><code>##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -0.4202 0.8405 4.8405 3.0377 16.8405
##
## tau^2 (estimated amount of total heterogeneity): 0.0485 (SE = 0.0607)
## tau (square root of estimated tau^2 value): 0.2202
## I^2 (total heterogeneity / total variability): 66.62%
## H^2 (total variability / sampling variability): 3.00
##
## Test for Heterogeneity:
## Q(df = 3) = 8.9483, p-val = 0.0300
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1759 0.1363 -1.2907 0.1968 -0.4430 0.0912
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
<div id="summary-of-pv3-studies" class="section level4">
<h4>Summary of PV3 studies</h4>
<pre><code>## [1] "Insufficient studies (n<2)"</code></pre>
</div>
</div>
<div id="meta-regression-secondary-moderators" class="section level3">
<h3>Meta-regression: secondary moderators</h3>
<div id="age-group" class="section level4">
<h4>Age group</h4>
<pre><code>##
## infant multiple
## 6 1</code></pre>
<pre><code>##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0605 (SE = 0.0560)
## tau (square root of estimated tau^2 value): 0.2461
## I^2 (residual heterogeneity / unaccounted variability): 69.93%
## H^2 (unaccounted variability / sampling variability): 3.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 15.6182, p-val = 0.0080
##
## Test of Moderators (coefficient(s) 2):
## QM(df = 1) = 0.0110, p-val = 0.9164
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt -0.0722 0.1216 -0.5940 0.5525 -0.3106
## factor(Age_group)multiple -0.0377 0.3589 -0.1049 0.9164 -0.7410
## ci.ub
## intrcpt 0.1661
## factor(Age_group)multiple 0.6657
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
<div id="income-setting" class="section level4">
<h4>Income setting</h4>
<pre><code>##
## low_lowermiddle uppermiddle_high
## 6 1</code></pre>
<pre><code>##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0612 (SE = 0.0608)
## tau (square root of estimated tau^2 value): 0.2473
## I^2 (residual heterogeneity / unaccounted variability): 64.30%
## H^2 (unaccounted variability / sampling variability): 2.80
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 14.3643, p-val = 0.0135
##
## Test of Moderators (coefficient(s) 2):
## QM(df = 1) = 0.3207, p-val = 0.5712
##
## Model Results:
##
## estimate se zval pval
## intrcpt -0.1070 0.1267 -0.8446 0.3983
## factor(Income_group)uppermiddle_high 0.1695 0.2993 0.5663 0.5712
## ci.lb ci.ub
## intrcpt -0.3554 0.1413
## factor(Income_group)uppermiddle_high -0.4172 0.7562
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
<div id="background-immunogenicity-seroconversion-rate-in-the-control-group" class="section level4">
<h4>Background immunogenicity (seroconversion rate in the control group)</h4>
<pre><code>##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0369 (SE = 0.0420)
## tau (square root of estimated tau^2 value): 0.1921
## I^2 (residual heterogeneity / unaccounted variability): 56.56%
## H^2 (unaccounted variability / sampling variability): 2.30
## R^2 (amount of heterogeneity accounted for): 23.23%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 11.1609, p-val = 0.0483
##
## Test of Moderators (coefficient(s) 2):
## QM(df = 1) = 1.7566, p-val = 0.1850
##
## Model Results:
##
## estimate se zval pval
## intrcpt -0.7068 0.4892 -1.4448 0.1485
## asin(sqrt(Baseline_seroconversion)) 0.8301 0.6263 1.3254 0.1850
## ci.lb ci.ub
## intrcpt -1.6657 0.2521
## asin(sqrt(Baseline_seroconversion)) -0.3974 2.0576
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
</div>
</div>
<div id="opv-specific-analysis" class="section level2">
<h2>OPV-specific analysis</h2>
<div id="forest-plot-1" class="section level3">
<h3>Forest plot</h3>
<p><img src="Breastfeeding_withheld_files/figure-html/unnamed-chunk-15-1.png" width="1152" /></p>
</div>
<div id="funnel-plot-1" class="section level3">
<h3>Funnel plot</h3>
<p><img src="Breastfeeding_withheld_files/figure-html/unnamed-chunk-16-1.png" width="576" /></p>
</div>
<div id="eggers-test-1" class="section level3">
<h3>Egger’s test</h3>
<p>Infants replicated across vaccines, therefore separate tests performed</p>
<pre><code>## [1] "OPV1:"</code></pre>
<pre><code>## [1] "Insufficient studies (n<3)"</code></pre>
<pre><code>## [1] "OPV2:"</code></pre>
<pre><code>## [1] "Insufficient studies (n<3)"</code></pre>
<pre><code>## [1] "OPV3:"</code></pre>
<pre><code>## [1] "Insufficient studies (n<3)"</code></pre>
</div>
<div id="meta-analysis-output-1" class="section level3">
<h3>Meta-analysis output</h3>
<pre><code>##
## Multivariate Meta-Analysis Model (k = 3; method: REML)
##
## logLik Deviance AIC BIC AICc
## 1.3423 -2.6846 -0.6846 -1.9915 3.3154
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 1 yes Reference
##
## Test for Heterogeneity:
## Q(df = 2) = 0.0502, p-val = 0.9752
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0638 0.0733 -0.8702 0.3842 -0.2074 0.0798
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
<div id="meta-regression-output-1" class="section level3">
<h3>Meta-regression output</h3>
<pre><code>##
## Multivariate Meta-Analysis Model (k = 3; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 1 yes Reference
##
## Test for Residual Heterogeneity:
## QE(df = 0) = 0.0000, p-val = 1.0000
##
## Test of Moderators (coefficient(s) 2:3):
## QM(df = 2) = 0.0502, p-val = 0.9752
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0382 0.2766 -0.1379 0.8903 -0.5804 0.5041
## Measure_of_SCOPV2 N-AB -0.0222 0.2881 -0.0770 0.9386 -0.5869 0.5425
## Measure_of_SCOPV3 N-AB -0.0717 0.3605 -0.1990 0.8423 -0.7784 0.6349
##
## intrcpt
## Measure_of_SCOPV2 N-AB
## Measure_of_SCOPV3 N-AB
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
<div id="serotype-specific-outputs" class="section level3">
<h3>Serotype-specific outputs</h3>
<div id="summary-of-pv1-studies" class="section level4">
<h4>Summary of PV1 studies</h4>
<pre><code>## [1] "Insufficient studies (n<2)"</code></pre>
</div>
<div id="summary-of-pv2-studies" class="section level4">
<h4>Summary of PV2 studies</h4>
<pre><code>## [1] "Insufficient studies (n<2)"</code></pre>
</div>
<div id="summary-of-pv3-studies-1" class="section level4">
<h4>Summary of PV3 studies</h4>
<pre><code>## [1] "Insufficient studies (n<2)"</code></pre>
</div>
</div>
</div>
</div>
</div>
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