metanalisis
Ismael Calandri
29/12/2021
Part 0 data correction
knitr::opts_chunk$set(echo = T)
library(estmeansd)
# BoxCox estimacion del grupo expuesto Raman,2020
bc.mean.sd(q1.val=25,med.val=27,q3.val=29,n=59)## $est.mean
## [1] 27.11934
##
## $est.sd
## [1] 3.154434# BoxCoxestimacion del grupo no expuesto Raman,2020
bc.mean.sd(q1.val=27,med.val=28,q3.val=29,n=30)## $est.mean
## [1] 28.02238
##
## $est.sd
## [1] 1.556732Remember the fact that we estimate the data of Raman (we have to clarify that beacuse the measures are different)
Part 1 Metanalisis
Metanalysis tabular results
knitr::opts_chunk$set(echo = F)
m.raw <- metacont(Ne,
Me,
Se,
Nc,
Mc,
Sc,
data=meta,
studlab=paste(Author),
comb.fixed = FALSE,
comb.random = TRUE,
prediction=TRUE,
sm="MD")
m.raw## MD 95%-CI %W(random)
## Amalakanti et al. 2021 -0.1000 [-0.8862; 0.6862] 26.4
## Del Brutto, et al 2021 -2.1000 [-3.7740; -0.4260] 11.3
## Raman et al 2021* -0.8600 [-1.7986; 0.0786] 22.7
## Triana et al. 2020 -1.6900 [-2.8251; -0.5549] 18.7
## Crivelli et al. (2021) -0.8000 [-1.8276; 0.2276] 20.8
##
## Number of studies combined: k = 5
##
## MD 95%-CI z p-value
## Random effects model -0.9421 [-1.5983; -0.2859] -2.81 0.0049
## Prediction interval [-2.8924; 1.0082]
##
## Quantifying heterogeneity:
## tau^2 = 0.2634 [0.0000; 4.6208]; tau = 0.5133 [0.0000; 2.1496]
## I^2 = 48.3% [0.0%; 81.0%]; H = 1.39 [1.00; 2.30]
##
## Test of heterogeneity:
## Q d.f. p-value
## 7.73 4 0.1019
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tauForest plot
Part 2 Bias analysis
Influential assesment
Outlier detection
## No outliers detected (random-effects model).Great news: no outliers!!
Influential analysis
## [===========================================================================] DONE
In red: extreme influential studies according Viechtbauer and Cheung rule
Please interpretate the subplots as follow:
dffits: The DIFFITS value of a study indicates in standard deviations how much the predicted pooled effect changes after excluding this study. cook.d: The Cook’s distance resembles the Mahalanobis distance you may know from outlier detection in conventional multivariate statistics. It is the distance between the value once the study is included compared to when it is excluded. cov.r: The covariance ratio is the determinant of the variance-covariance matrix of the parameter estimates when the study is removed, divided by the determinant of the variance-covariance matrix of the parameter estimates when the full dataset is considered. Importantly, values of cov.r < 1 indicate that removing the study will lead to a more precise effect size estimation (i.e., less heterogeneity).
**Influential analysis summary**: Amalakanti is the most influentila study (this is relevant because amalakanti findings are opposite to the results -there is no difference-, despite of that study and its influency, there is a significant difference.
Publication bias assesment
In this section we analyze the possibility that negative studies have not been submitted because they are of less interest to the journals to be published (publication bias).
Funnel plot
The funnel plot method assumes that the effects should cluster symmetrically around the estimated mean of the effect.
Egger´s test
This test provides a numerical measure of effects asimmetry
## Warning in eggers.test(x = m.raw): Your meta-analysis contains k = 5 studies.
## Egger's test may lack the statistical power to detect bias when the number of
## studies is small (i.e., k<10).## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## -4.959 -7.67 - -2.25 -3.581 0.03724701
##
## Eggers' test indicates the presence of funnel plot asymmetry.Metaregresion
Metaregresion by age
##
## Mixed-Effects Model (k = 5; tau^2 estimator: DL)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.2665)
## 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): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 3) = 1.9008, p-val = 0.5933
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.8315, p-val = 0.0157
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 2.2592 1.2955 1.7438 0.0812 -0.2800 4.7983 .
## cases_age -0.0642 0.0266 -2.4148 0.0157 -0.1163 -0.0121 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1