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Narrow_dose_interval.html
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<h1 class="title toc-ignore">Narrow dose interval</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="Narrow_dose_interval_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="Narrow_dose_interval_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 = -0.0591, p = 0.9529</code></pre>
</div>
<div id="meta-analysis-output" class="section level3">
<h3>Meta-analysis output</h3>
<pre><code>##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6473 -7.2945 -3.2945 -5.9082 8.7055
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0023)
## 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 = 2) = 0.1603, p-val = 0.9230
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0193 0.0216 -0.8895 0.3737 -0.0617 0.0232
##
## ---
## 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 = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0025)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): NA%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.1281, p-val = 0.7204
##
## Test of Moderators (coefficient(s) 2):
## QM(df = 1) = 0.0321, p-val = 0.8578
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0200 0.0221 -0.9074 0.3642 -0.0633 0.0232
## VaccineRotavirus 0.0200 0.1118 0.1792 0.8578 -0.1991 0.2392
##
## ---
## 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>## [1] "Insufficient studies (n<2)"</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 = 2; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1885 -4.3771 -0.3771 -4.3771 11.6229
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0025)
## 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 = 1) = 0.1281, p-val = 0.7204
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0200 0.0221 -0.9074 0.3642 -0.0633 0.0232
##
## ---
## 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="Narrow_dose_interval_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="Narrow_dose_interval_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 = -0.3828, p = 0.7019</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 = 7; method: REML)
##
## logLik Deviance AIC BIC AICc
## 10.0844 -20.1688 -16.1688 -16.5853 -12.1688
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 3 no Reference
##
## Test for Heterogeneity:
## Q(df = 6) = 9.3175, p-val = 0.1565
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0141 0.0094 -1.4960 0.1347 -0.0326 0.0044
##
## ---
## 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 = 7; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 3 no Reference
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 9.1123, p-val = 0.0584
##
## Test of Moderators (coefficient(s) 2:3):
## QM(df = 2) = 0.2051, p-val = 0.9025
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0138 0.0108 -1.2727 0.2031 -0.0350 0.0074
## Measure_of_SCOPV2 N-AB 0.0138 0.0403 0.3414 0.7328 -0.0653 0.0928
## Measure_of_SCOPV3 N-AB -0.0063 0.0246 -0.2552 0.7985 -0.0545 0.0419
##
## 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 = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.2597 -8.5193 -4.5193 -6.3221 7.4807
##
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0353
## I^2 (total heterogeneity / total variability): 66.83%
## H^2 (total variability / sampling variability): 3.01
##
## Test for Heterogeneity:
## Q(df = 3) = 8.9842, p-val = 0.0295
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0190 0.0236 -0.8051 0.4208 -0.0652 0.0272
##
## ---
## 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>## [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>##
## Random-Effects Model (k = 2; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1885 -4.3771 -0.3771 -4.3771 11.6229
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0025)
## 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 = 1) = 0.1281, p-val = 0.7204
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0200 0.0221 -0.9074 0.3642 -0.0633 0.0232
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
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