Description Usage Arguments Details Value References See Also Examples
This function performs various calculations needed for an umbrella review.
1 2 3 4 5 6 7 8 9 
x 
a wellformatted dataset. 
method.var 
the estimator used to quantify the betweenstudy variance in the randomeffects metaanalysis. Default is the Restricted Likelihood Maximum ("REML") estimator. Alternatively, DerSimonian and Laird 
mult.level 
a logical variable indicating the presence of multiple effect sizes per study in at least one factor of the umbrella review. Default is 
r 
a correlation coefficient indicating the strength of the association between multiple outcomes (or timepoints) within the same study. The 
true_effect 
the method to estimate the true effect in the test for excess of significance. It must be 
seed 
an integer value used as an argument by the set.seed() function. Only used for the Ioannidis' test for excess of significance with ratios (i.e., “OR”, “RR”, “IRR” or their logarithm) as effect size measures. 
verbose 
a logical variable indicating whether text outputs and messages should be generated. We recommend turning this option to FALSE only after having carefully read all the generated messages. 
This function automatically performs calculations allowing to stratify evidence according to various criteria. For each factor included in a wellformatted dataset, this function automatically:
performs fixed and randomeffects metaanalyses.
provides an estimation of the betweenstudy variance and heterogeneity using three indicators (tau^2, Qstatistic and I^2 statistic).
estimates the 95% prediction interval (if the number of studies is equal or larger to 3).
provides an identification of the statistical significance of the largest study included in the metaanalysis.
provides an assessment of publication bias using the Egger's test (if the number of studies is equal or larger to 3).
provides an assessment of excess significance bias using the Ioannidis' test.
performs a jackknife leaveoneout metaanalysis (if the number of studies is equal or larger to 2).
calculates the proportion of participants included in studies at low risk of bias (if study quality is indicated in the dataset).
A specificity of this function is that it does not include arguments to specify the name of the columns of the dataset used as input.
Instead, the function requires users to build a dataset that meets fixed rules.
Details on how building this wellformatted dataset
are given in the metaumbrellapackage
section of this manual and a vignette is specifically dedicated to this topic.
Moreover, examples of wellformatted datasets
are available as data distributed along with the package (see df.OR, df.OR.multi, df.SMD, df.RR, df.HR, df.IRR).
When estimating the test for excess of significance, the umbrella()
function must assume a best approximation of the true effect.
The true_effect
argument can be used to select the method that will be applied to estimate this approximation of the true effect.
If "largest"
is selected, the best approximation of the true effect size is assumed to be equal to the effect size of the largest study included in the metaanalysis.
If "pooled"
is selected, the best approximation of the true effect size is assumed to be equal to the pooled effect size of the metaanalysis.
If a numeric
value is entered, the best approximation of the true effect size is assumed to be equal to this numeric value. Note that when entering a numeric value for a ratio, the value should be given in its natural scale (and not in its logarithm).
The umbrella()
function returns an object of class “umbrella”, which is a list containing information required for stratifying the evidence.
This list contains, for each factor included in the umbrella review:
measure  the measure of the effect used to perform the calculations. 
x  the data used to conduct the metaanalysis. Note that these data may be 
slightly different from the raw data introduced.  
x_multi  the original data when there is a multivariate structure. 
Note that these data may be slightly different from the raw data introduced.  
x_shared  a dataframe allowing to visualize adjustments made when a shared_nexp 
or shared_controls correction is requested  
(see metaumbrellapackage for more information). 

n  the overall number of studies, cases and controls. 
method.var  the estimator used for fitting the random effects metaanalyses 
random  pooled effect size, pvalue and 95% confidence interval and prediction 
interval of the randomeffects metaanalysis.  
fixed  pooled effect size, pvalue and 95% confidence interval and prediction 
interval of the fixedeffect metaanalysis.  
largest  95% confidence interval of the largest study. 
heterogeneity  tau^2, I^2 and Q test. 
egger  estimate and pvalue of the Egger's test for publication bias. 
esb  results of the Ioannidis' test for excess of significance bias. See 
esb.test() for more information. 

riskofbias  percentage of participants in studies at low risk of bias. 
amstar  AMSTAR score obtained by the metaanalysis. 
evidence  evidence class according to some criteria. 
The functions print
and summary
may be used to print the details or a summary of the results.
FusarPoli, P., Radua, J. (2018). Ten simple rules for conducting umbrella reviews.
EvidenceBased Mental Health, 21, 95–100.
Radua, J., RamellaCravaro, V., Ioannidis, J.P.A., Reichenberg, A., Phiphopthatsanee, N., Amir, T., Yenn Thoo, H., Oliver, D., Davies, C., Morgan, C., McGuire, P., Murray, R.M., FusarPoli, P. (2018)
What causes psychosis? An umbrella review of risk and protective factors.
World Psychiatry, 17, 49–66.
metaumbrellapackage
for the formatting of wellformatted datasets
add.evidence()
for stratifying the evidence in an umbrella review
forest()
for drawing a forest plot of the factors included in an umbrella review
subset.umbrella()
for retrieving a subset of the factors included in an umbrella review
union.umbrella()
for combining the factors included in two umbrella reviews
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ### Perform an umbrella review with randomeffects metaanalyses
### with a HartungKnappSidikJonkman estimator
umb < umbrella(df.IRR, method.var = "hksj")
### obtain the results of the calculations in a dataframe
summary(umb)
### manually inspect the results of the umbrella review calculations for the 'Smoking' factor
### included in the dataset.
umb$Smoking
### Perform a metaanalysis with multilevel data, assuming a correlation of 0.8
### between all outcomes of the same study
umb.multi < umbrella(df.OR.multi, mult.level = TRUE, r = 0.8)
### obtain a stratification of the evidence according to the Ioannidis classification
add.evidence(umb.multi, criteria = "Ioannidis")

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