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Increased_vaccine_inoculum.html
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<h1 class="title toc-ignore">Increased vaccine inoculum</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="Increased_vaccine_inoculum_files/figure-html/unnamed-chunk-3-1.png" width="1344" /></p>
</div>
<div id="funnel-plot" class="section level3">
<h3>Funnel plot</h3>
<p><img src="Increased_vaccine_inoculum_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 = 2.9359, p = 0.0033</code></pre>
</div>
<div id="meta-analysis-output" class="section level3">
<h3>Meta-analysis output</h3>
<pre><code>##
## Random-Effects Model (k = 11; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2089 -4.4178 -0.4178 0.1874 1.2965
##
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0021
## I^2 (total heterogeneity / total variability): 0.04%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 10) = 16.9197, p-val = 0.0762
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0446 0.0293 1.5222 0.1280 -0.0128 0.1020
##
## ---
## 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 = 11; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0038)
## tau (square root of estimated tau^2 value): 0.0010
## I^2 (residual heterogeneity / unaccounted variability): 0.01%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 78.23%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 14.8352, p-val = 0.0956
##
## Test of Moderators (coefficient(s) 2):
## QM(df = 1) = 2.0840, p-val = 0.1488
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1168 0.0580 2.0146 0.0439 0.0032 0.2304 *
## VaccinePolio -0.0970 0.0672 -1.4436 0.1488 -0.2286 0.0347
##
## ---
## 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 = 5; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2349 -4.4697 -0.4697 -1.6971 11.5303
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0114)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 4) = 3.0247, p-val = 0.5537
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1168 0.0580 2.0147 0.0439 0.0032 0.2304 *
##
## ---
## 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>## [1] "Insufficient studies (n<2)"</code></pre>
</div>
<div id="summary-of-pv3-studies" class="section level4">
<h4>Summary of PV3 studies</h4>
<pre><code>##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -0.2638 0.5277 4.5277 3.7465 10.5277
##
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0040)
## tau (square root of estimated tau^2 value): 0.0009
## I^2 (total heterogeneity / total variability): 0.01%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 11.8105, p-val = 0.0375
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0198 0.0339 0.5841 0.5591 -0.0467 0.0863
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</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>## [1] "Residual heterogeneity not significant (p>0.05)"</code></pre>
</div>
<div id="income-setting" class="section level4">
<h4>Income setting</h4>
<pre><code>## [1] "Residual heterogeneity not significant (p>0.05)"</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>## [1] "Residual heterogeneity not significant (p>0.05)"</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="Increased_vaccine_inoculum_files/figure-html/unnamed-chunk-15-1.png" width="1248" /></p>
</div>
<div id="funnel-plot-1" class="section level3">
<h3>Funnel plot</h3>
<p><img src="Increased_vaccine_inoculum_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>##
## Regression Test for Funnel Plot Asymmetry
##
## model: mixed-effects meta-regression model
## predictor: standard error
##
## test for funnel plot asymmetry: z = -0.3263, p = 0.7442</code></pre>
<pre><code>## [1] "OPV2:"</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 = 0.4036, p = 0.6865</code></pre>
<pre><code>## [1] "OPV3:"</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 = 2.2139, p = 0.0268</code></pre>
</div>
<div id="meta-analysis-output-1" class="section level3">
<h3>Meta-analysis output</h3>
<pre><code>##
## Multivariate Meta-Analysis Model (k = 18; method: REML)
##
## logLik Deviance AIC BIC AICc
## 9.3297 -18.6595 -14.6595 -12.9931 -13.8023
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0020 0.0444 8 no Reference
##
## Test for Heterogeneity:
## Q(df = 17) = 26.2255, p-val = 0.0704
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0593 0.0257 2.3044 0.0212 0.0089 0.1097 *
##
## ---
## 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 = 18; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0021 0.0457 8 no Reference
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 23.4345, p-val = 0.0753
##
## Test of Moderators (coefficient(s) 2:3):
## QM(df = 2) = 2.3801, p-val = 0.3042
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0841 0.0306 2.7457 0.0060 0.0241 0.1442
## Measure_of_SCOPV2 N-AB -0.0665 0.0612 -1.0867 0.2772 -0.1864 0.0534
## Measure_of_SCOPV3 N-AB -0.0620 0.0446 -1.3904 0.1644 -0.1493 0.0254
##
## 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>##
## Random-Effects Model (k = 8; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.2572 -10.5144 -6.5144 -6.6226 -3.5144
##
## tau^2 (estimated amount of total heterogeneity): 0.0035 (SE = 0.0046)
## tau (square root of estimated tau^2 value): 0.0588
## I^2 (total heterogeneity / total variability): 43.40%
## H^2 (total variability / sampling variability): 1.77
##
## Test for Heterogeneity:
## Q(df = 7) = 10.9013, p-val = 0.1430
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0808 0.0352 2.2995 0.0215 0.0119 0.1498 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
<div id="summary-of-pv2-studies" class="section level4">
<h4>Summary of PV2 studies</h4>
<pre><code>##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0074 -6.0148 -2.0148 -3.8175 9.9852
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0091)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 3) = 0.7227, p-val = 0.8679
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0093 0.0511 0.1819 0.8557 -0.0909 0.1095
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</div>
<div id="summary-of-pv3-studies-1" class="section level4">
<h4>Summary of PV3 studies</h4>
<pre><code>##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -0.2638 0.5277 4.5277 3.7465 10.5277
##
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0040)
## tau (square root of estimated tau^2 value): 0.0009
## I^2 (total heterogeneity / total variability): 0.01%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 11.8105, p-val = 0.0375
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0198 0.0339 0.5841 0.5591 -0.0467 0.0863
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
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