RSMA - Pneumo V4
Lozada, Julian et al (2022)
2023-01-06
# option for html output
knitr::opts_chunk$set(echo = TRUE)
## A meta-analysis combines the results of several scientiïc studies addressing the same question.
## Though, individual study measurements that are reported may have some degree of error.
## It is simply analysis of analyses that correspond to a shared topic.
## There are two different approaches in Meta-analysis viz. meta-analysis using aggregate data (AD)
## or individual participant data (IPD).Using an AD approach, summary data for the same outcome e.g.
## prevalence rate of a disease,proportions from each study,are pooled for the statistical analysis
## (Suvarnapathaki,2021). Review Systematic and Metanalysys (RSMA) - Analysis Stadistics
I. Descriptive statistics (data structure)
rm(list=ls(all=TRUE)) #Deletes all memory content
# Call of the packages
library(readxl)
data_neumo1 <- read_excel("C:/Users/USER/Desktop/RSMA/data_neumo1.xlsx")
View(data_neumo1)
#install.packages(c("metafor", "meta"))
#install.packages("tidyverse")
library(metafor)
library(meta)
library(metadat)
library(tidyverse)head(data_neumo1,114)## # A tibble: 114 × 20
## Sequence code author_1 year_pub author contint age prev sample posit
## <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 RSMA01 RS001 Abdullahi 2008 Abdul… 1 4 0.0467 107 5
## 2 RSMA02 RS001 Abdullahi 2008 Abdul… 1 2 0.0564 195 11
## 3 RSMA03 RS001 Abdullahi 2008 Abdul… 1 1 0.0640 406 26
## 4 RSMA04 RS002 Adetifa 2012 Adeti… 1 4 0.0685 482 33
## 5 RSMA05 RS002 Adetifa 2012 Adeti… 1 2 0.108 530 57
## 6 RSMA06 RS002 Adetifa 2012 Adeti… 1 1 0.260 361 94
## 7 RSMA07 RS004 Adler 2019 Adler… 5 1 0.0654 795 52
## 8 RSMA08 RS010 Almeida 2014 Almei… 5 4 0.0229 3361 77
## 9 RSMA09 RS015 Ansaldi 2013 Ansal… 5 4 0.02 283 56
## 10 RSMA10 RS017 Becker-Dreps 2015 Becke… 2 4 0.0190 210 4
## # … with 104 more rows, and 10 more variables: type_prev <dbl>, stage <dbl>,
## # comorb <dbl>, type_morb <dbl>, meth_iden <dbl>, type_samp <dbl>,
## # setting <dbl>, type_setting <dbl>, type_study <dbl>, quality <dbl>glimpse(data_neumo1)## Rows: 127
## Columns: 20
## $ Sequence <chr> "RSMA01", "RSMA02", "RSMA03", "RSMA04", "RSMA05", "RSMA06…
## $ code <chr> "RS001", "RS001", "RS001", "RS002", "RS002", "RS002", "RS…
## $ author_1 <chr> "Abdullahi", "Abdullahi", "Abdullahi", "Adetifa", "Adetif…
## $ year_pub <dbl> 2008, 2008, 2008, 2012, 2012, 2012, 2019, 2014, 2013, 201…
## $ author <chr> "Abdullahi_2008", "Abdullahi_2008", "Abdullahi_2008", "Ad…
## $ contint <dbl> 1, 1, 1, 1, 1, 1, 5, 5, 5, 2, 1, 2, 2, 6, 3, 3, 5, 5, 4, …
## $ age <dbl> 4, 2, 1, 4, 2, 1, 1, 4, 4, 4, 1, 4, 4, 1, 4, 4, 2, 4, 1, …
## $ prev <dbl> 0.046728972, 0.056410256, 0.064039409, 0.068464730, 0.107…
## $ sample <dbl> 107, 195, 406, 482, 530, 361, 795, 3361, 283, 210, 600, 1…
## $ posit <dbl> 5, 11, 26, 33, 57, 94, 52, 77, 56, 4, 108, 14, 41, 0, 17,…
## $ type_prev <dbl> 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, …
## $ stage <dbl> 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, …
## $ comorb <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, …
## $ type_morb <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ meth_iden <dbl> 1, 1, 1, 1, 1, 1, 1, 3, 2, 1, 1, 1, 2, 1, 3, 3, 1, 1, 1, …
## $ type_samp <dbl> 1, 1, 1, 1, 1, 1, 2, 3, 1, 1, 2, 3, 3, 1, 1, 1, 1, 1, 4, …
## $ setting <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 4, 1, 1, 1, 1, 2, …
## $ type_setting <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 10, 1, 1, 1, 1, 7,…
## $ type_study <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 4, 2, 2, 4, 4, 3, …
## $ quality <dbl> 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 4, …names(data_neumo1) <- c("Sequence", "code", "author", "year_pub", "contint", "age", "prev","sample",
"posit", "type_prev", "stage", "comorb","type_morb","meth_iden","type_samp",
"setting","type_setting","type_study","quality")## Warning: The `value` argument of `names<-` must have the same length as `x` as of tibble
## 3.0.0.
## ℹ `names` must have length 20, not 19.## Warning: The `value` argument of `names<-` can't be empty as of tibble 3.0.0.
## ℹ Column 20 must be named.glimpse(data_neumo1)## Rows: 127
## Columns: 20
## $ Sequence <chr> "RSMA01", "RSMA02", "RSMA03", "RSMA04", "RSMA05", "RSMA06…
## $ code <chr> "RS001", "RS001", "RS001", "RS002", "RS002", "RS002", "RS…
## $ author <chr> "Abdullahi", "Abdullahi", "Abdullahi", "Adetifa", "Adetif…
## $ year_pub <dbl> 2008, 2008, 2008, 2012, 2012, 2012, 2019, 2014, 2013, 201…
## $ contint <chr> "Abdullahi_2008", "Abdullahi_2008", "Abdullahi_2008", "Ad…
## $ age <dbl> 1, 1, 1, 1, 1, 1, 5, 5, 5, 2, 1, 2, 2, 6, 3, 3, 5, 5, 4, …
## $ prev <dbl> 4, 2, 1, 4, 2, 1, 1, 4, 4, 4, 1, 4, 4, 1, 4, 4, 2, 4, 1, …
## $ sample <dbl> 0.046728972, 0.056410256, 0.064039409, 0.068464730, 0.107…
## $ posit <dbl> 107, 195, 406, 482, 530, 361, 795, 3361, 283, 210, 600, 1…
## $ type_prev <dbl> 5, 11, 26, 33, 57, 94, 52, 77, 56, 4, 108, 14, 41, 0, 17,…
## $ stage <dbl> 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, …
## $ comorb <dbl> 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, …
## $ type_morb <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, …
## $ meth_iden <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ type_samp <dbl> 1, 1, 1, 1, 1, 1, 1, 3, 2, 1, 1, 1, 2, 1, 3, 3, 1, 1, 1, …
## $ setting <dbl> 1, 1, 1, 1, 1, 1, 2, 3, 1, 1, 2, 3, 3, 1, 1, 1, 1, 1, 4, …
## $ type_setting <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 4, 1, 1, 1, 1, 2, …
## $ type_study <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 10, 1, 1, 1, 1, 7,…
## $ quality <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 4, 2, 2, 4, 4, 3, …
## $ NA <dbl> 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 4, …str(data_neumo1)## tibble [127 × 20] (S3: tbl_df/tbl/data.frame)
## $ Sequence : chr [1:127] "RSMA01" "RSMA02" "RSMA03" "RSMA04" ...
## $ code : chr [1:127] "RS001" "RS001" "RS001" "RS002" ...
## $ author : chr [1:127] "Abdullahi" "Abdullahi" "Abdullahi" "Adetifa" ...
## $ year_pub : num [1:127] 2008 2008 2008 2012 2012 ...
## $ contint : chr [1:127] "Abdullahi_2008" "Abdullahi_2008" "Abdullahi_2008" "Adetifa_2012" ...
## $ age : num [1:127] 1 1 1 1 1 1 5 5 5 2 ...
## $ prev : num [1:127] 4 2 1 4 2 1 1 4 4 4 ...
## $ sample : num [1:127] 0.0467 0.0564 0.064 0.0685 0.1075 ...
## $ posit : num [1:127] 107 195 406 482 530 ...
## $ type_prev : num [1:127] 5 11 26 33 57 94 52 77 56 4 ...
## $ stage : num [1:127] 2 2 2 1 1 1 1 1 2 1 ...
## $ comorb : num [1:127] 1 1 1 2 2 2 2 2 2 2 ...
## $ type_morb : num [1:127] 2 2 2 2 2 2 2 2 2 2 ...
## $ meth_iden : num [1:127] 1 1 1 1 1 1 1 1 1 1 ...
## $ type_samp : num [1:127] 1 1 1 1 1 1 1 3 2 1 ...
## $ setting : num [1:127] 1 1 1 1 1 1 2 3 1 1 ...
## $ type_setting: num [1:127] 2 2 2 2 2 2 1 2 1 2 ...
## $ type_study : num [1:127] 1 1 1 1 1 1 1 1 1 2 ...
## $ quality : num [1:127] 4 4 4 4 4 4 4 4 4 4 ...
## $ NA : num [1:127] 3 3 3 4 4 4 4 4 4 4 ...summary(data_neumo1)## Sequence code author year_pub
## Length:127 Length:127 Length:127 Min. :1997
## Class :character Class :character Class :character 1st Qu.:2010
## Mode :character Mode :character Mode :character Median :2014
## Mean :2014
## 3rd Qu.:2018
## Max. :2021
## contint age prev sample
## Length:127 Min. :1.000 Min. :1.000 Min. :0.00000
## Class :character 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.04644
## Mode :character Median :2.000 Median :1.000 Median :0.08482
## Mean :3.047 Mean :2.031 Mean :0.11435
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:0.14096
## Max. :6.000 Max. :4.000 Max. :0.61538
## posit type_prev stage comorb
## Min. : 8.0 Min. : 0.00 Min. :1.000 Min. :1.00
## 1st Qu.: 212.5 1st Qu.: 11.50 1st Qu.:1.000 1st Qu.:2.00
## Median : 399.0 Median : 33.00 Median :1.000 Median :2.00
## Mean : 715.7 Mean : 90.98 Mean :1.417 Mean :1.78
## 3rd Qu.: 667.5 3rd Qu.: 82.50 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :8336.0 Max. :1868.00 Max. :3.000 Max. :3.00
## type_morb meth_iden type_samp setting type_setting
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :1.000 Median :1.00 Median :2.000
## Mean :1.819 Mean :1.638 Mean :1.268 Mean :1.78 Mean :1.717
## 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:3.00 3rd Qu.:2.000
## Max. :2.000 Max. :7.000 Max. :3.000 Max. :5.00 Max. :4.000
## type_study quality NA
## Min. : 1.000 Min. :1.000 Min. :2.000
## 1st Qu.: 1.000 1st Qu.:2.000 1st Qu.:3.000
## Median : 1.000 Median :4.000 Median :3.000
## Mean : 3.094 Mean :3.394 Mean :3.307
## 3rd Qu.: 6.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :10.000 Max. :4.000 Max. :4.000convert <- which(data_neumo1$prev =="age")
data_neumo1$prev[convert] <- "age"
summary(data_neumo1$prev)## Length Class Mode
## 127 character characterpar(mfrow = c(2,4))
barplot(Age <- table(data_neumo1$prev))
barplot(Age <- table(data_neumo1$sample))
barplot(Age <- table(data_neumo1$type_samp))
barplot(Age <- table(data_neumo1$comorb))
barplot(Age <- table(data_neumo1$type_morb))
barplot(Age <- table(data_neumo1$meth_iden))
par(mfrow = c(2,4))
barplot(prev <- table(data_neumo1$age))
barplot(prev <- table(data_neumo1$sample))
barplot(prev <- table(data_neumo1$type_samp))
barplot(prev <- table(data_neumo1$comorb))
barplot(prev <- table(data_neumo1$type_morb))
barplot(prev <- table(data_neumo1$meth_iden))
par(mfrow = c(1,3))
hist(data_neumo1$age)
Prev_adults <- subset(data_neumo1, prev=="1" & age=="2")
dim(Prev_adults)## [1] 20 20print(Prev_adults)## # A tibble: 20 × 20
## Sequence code author year_…¹ contint age prev sample posit type_…² stage
## <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 RSMA35 RS032 Feola 2016 Feola_… 2 1 0.0376 399 15 1
## 2 RSMA38 RS036 Gounder 2014 Gounde… 2 1 0.142 8336 1183 2
## 3 RSMA41 RS038 Grant 2016 Grant_… 2 1 0.118 2847 336 2
## 4 RSMA44 RS040 Hammitt 2006 Hammit… 2 1 0.01 115 15 2
## 5 RSMA45 RS040 Hammitt 2006 Hammit… 2 1 0.01 115 15 2
## 6 RSMA61 RS062 Millar 2008 Millar… 2 1 0.139 1729 241 3
## 7 RSMA67 RS068 Onwubi… 2008 Onwubi… 2 1 0.0343 175 6 1
## 8 RSMA80 RS078 Rodrig… 1997 Rodrig… 2 1 0.168 321 54 2
## 9 RSMA81 RS078 Rodrig… 1997 Rodrig… 2 1 0.0533 150 8 2
## 10 RSMA82 RS079 Rosen 2007 Rosen … 2 1 0.0462 65 3 1
## 11 RSMA83 RS079 Rosen 2007 Rosen_… 2 1 0.0476 63 3 1
## 12 RSMA84 RS081 Saravo… 2007 Saravo… 2 1 0.015 200 3 1
## 13 RSMA85 RS081 Saravo… 2007 Saravo… 2 1 0.03 200 6 1
## 14 RSMA86 RS081 Saravo… 2007 Saravo… 2 1 0.11 200 22 1
## 15 RSMA87 RS083 Scott 2012 Scott_… 2 1 0.109 2681 291 2
## 16 RSMA95 RS086 Sutcli… 2019 Sutcli… 2 1 0.145 509 74 2
## 17 RSMA96 RS086 Sutcli… 2019 Sutcli… 2 1 0.475 509 242 2
## 18 RSMA125 RS142 Watt 2004 Watt_2… 2 1 0.111 1994 222 1
## 19 RSMA126 RS142 Watt 2004 Watt_2… 2 1 0.0577 1994 115 1
## 20 RSMA127 RS142 Watt 2004 Watt_2… 2 1 0.152 1994 304 1
## # … with 9 more variables: comorb <dbl>, type_morb <dbl>, meth_iden <dbl>,
## # type_samp <dbl>, setting <dbl>, type_setting <dbl>, type_study <dbl>,
## # quality <dbl>, `` <dbl>, and abbreviated variable names ¹year_pub,
## # ²type_prev
II. Effect size & Heterogeneity Analysis (Viechtbauer, 2010)
rm(list=ls(all=TRUE)) #Deletes all memory content
# Call of the packages
library(readxl)
data_neumo1 <- read_excel("C:/Users/USER/Desktop/RSMA/data_neumo1.xlsx")
View(data_neumo1)
#install.packages(c("metafor", "meta"))
#install.packages("tidyverse")
library(metafor)
library(meta)
library(metadat)
library(tidyverse)
# Effect size (weighted average of the individual study)
# Calculation of effect size without transformation - raw proportion (xi/ni)
ies=escalc(xi=posit, ni=sample, data=data_neumo1, measure="PR")
pes=rma(yi, vi, data=ies)
print(pes)##
## Random-Effects Model (k = 127; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0112 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.1058
## I^2 (total heterogeneity / total variability): 99.62%
## H^2 (total variability / sampling variability): 262.19
##
## Test for Heterogeneity:
## Q(df = 126) = 12371.1556, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1160 0.0096 12.1308 <.0001 0.0972 0.1347 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Calculation of effect size - second option - logit transformed proportion (log xi/(ni-xi))
ies=escalc(xi=posit, ni=sample, data=data_neumo1, measure="PLO")
pes=rma(yi, vi, data=ies)
print(pes)##
## Random-Effects Model (k = 127; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.9769 (SE = 0.1334)
## tau (square root of estimated tau^2 value): 0.9884
## I^2 (total heterogeneity / total variability): 98.51%
## H^2 (total variability / sampling variability): 67.15
##
## Test for Heterogeneity:
## Q(df = 126) = 7558.4945, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -2.3238 0.0919 -25.2883 <.0001 -2.5039 -2.1437 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Calculation of effect size - logit transformed proportion (log xi/(ni-xi))
ies.logit=escalc(xi=posit, ni=sample, data=data_neumo1, measure="PLO")
pes.logit=rma(yi, vi, data=ies.logit)
pes=predict(pes.logit, transf=transf.ilogit)
print(pes)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.0892 0.0756 0.1049 0.0138 0.4065# Calculation of effect size - arcsine transformed (asin (sqrt(xi/ni))
ies.logit=escalc(xi=posit, ni=sample, data=data_neumo1, measure="PAS")
pes.logit=rma(yi, vi, data=ies.logit)
pes=predict(pes.logit, transf=transf.ilogit)
print(pes)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.5791 0.5722 0.5861 0.5007 0.6537# Calculation of effect size with transformation logit - third option
pes.logit=rma(yi, vi, data=ies, method="DL")
pes=predict(pes.logit, transf=transf.ilogit)
transf=transf.ilogit
print(pes)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.0898 0.0768 0.1048 0.0155 0.3826# Calculation of effect size without transformation
#(adjustments to proportions close to 0)
ies.logit=escalc(xi=posit, ni=sample, measure="PLO", data=data_neumo1)
pes.logit=rma(yi, vi, data=ies.logit, method="DL", level=95)
pes=predict(pes.logit, transf=transf.ilogit)
print(pes, digits=6)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.089825 0.076809 0.104796 0.015473 0.382610# Heterogeneity calculation (variation between studies)
print(pes.logit, digits=4)##
## Random-Effects Model (k = 127; tau^2 estimator: DL)
##
## tau^2 (estimated amount of total heterogeneity): 0.8712 (SE = 0.2452)
## tau (square root of estimated tau^2 value): 0.9334
## I^2 (total heterogeneity / total variability): 98.33%
## H^2 (total variability / sampling variability): 59.99
##
## Test for Heterogeneity:
## Q(df = 126) = 7558.4945, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -2.3158 0.0871 -26.5838 <.0001 -2.4865 -2.1450 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1confint(pes.logit, digits=2)##
## estimate ci.lb ci.ub
## tau^2 0.87 0.86 1.58
## tau 0.93 0.93 1.26
## I^2(%) 98.33 98.31 99.08
## H^2 59.99 59.10 108.26III. Forest Plot (Effect size and grouping of the interest variable)
# II. Forest Plot (pooled estimate of the parameter)
rm(list=ls(all=TRUE)) #Deletes all memory content
# Call of the packages
library(readxl)
data_neumo1 <- read_excel("C:/Users/USER/Desktop/RSMA/data_neumo1.xlsx")
View(data_neumo1)
#install.packages(c("metafor", "meta"))
#install.packages("tidyverse")
library(metafor)
library(meta)
library(metadat)
library(tidyverse)
# Forest PLot without transformation
pes.summary=metaprop(posit,sample,author,data=data_neumo1, sm="PRAW")
forest(pes.summary)
# Forest PLot with transformation loggit
pes.summary=metaprop(posit,sample,author, data=data_neumo1, sm="PLO")
forest(pes.summary)
# Forest PLot with transformation loggit - subgroup (age, sample, method and type sample)
ms2s <- update(pes.summary, byvar= age, print.byvar=TRUE)
forest(ms2s)
ms2s <- update(pes.summary, byvar= stage, print.byvar=TRUE)
forest(ms2s)
ms2s <- update(pes.summary, byvar= contint, print.byvar=TRUE)
forest(ms2s)
ms2s <- update(pes.summary, byvar= meth_iden, print.byvar=TRUE)
forest(ms2s)
ms2s <- update(pes.summary, byvar= type_samp, print.byvar=TRUE)
forest(ms2s)
ms2s <- update(pes.summary, byvar= type_prev, print.byvar=TRUE)
forest(ms2s) 
ms2s <- update(pes.summary, byvar= comorb, print.byvar=TRUE)
forest(ms2s)
ms2s <- update(pes.summary, byvar= setting, print.byvar=TRUE)
forest(ms2s)
ms2s <- update(pes.summary, byvar= type_study, print.byvar=TRUE)
forest(ms2s)
# Forest Plot d- fit
pes.summary=metaprop(posit,sample,author, data=data_neumo1, sm="PR",
method.tau="DL", method.ci="NAsm")
forest(pes.summary,
xlim=c(0,4),
pscale=20,
rightcols=FALSE,
leftcols=c("author", "posit", "sample", "prev", "ci"),
leftlabs=c("author", "posit", "sample", "prev", "95% C.I."),
xlab="Prevalence of CC", smlab="",
weight.study="random", squaresize=0.5, col.square="red",
col.square.lines="green",
col.diamond="maroon", col.diamond.lines="maroon",
pooled.totals=FALSE,
comb.fixed=FALSE,
fs.hetstat=10,
print.tau2=TRUE,
print.Q=TRUE,
print.pval.Q=TRUE,
print.I2=TRUE,
digits=2)
# Forest Plot d- fit
pes.summary=metaprop(posit,sample,author, data=data_neumo1, sm="PLO",
method.tau="DL", method.ci="NAsm")
forest(pes.summary,
xlim=c(0,4),
pscale=20,
rightcols=FALSE,
leftcols=c("author", "posit", "sample", "prev","ci"),
leftlabs=c("author", "posit", "sample", "prev", "95% C.I."),
xlab="Prevalence of CC", smlab="",
weight.study="random", squaresize=0.2, col.square="green",
col.square.lines="red",
col.diamond="maroon", col.diamond.lines="yellow",
pooled.totals=TRUE,
comb.fixed=TRUE,
fs.hetstat=10,
print.tau2=TRUE,
print.Q=TRUE,
print.pval.Q=TRUE,
print.I2=TRUE,
digits=2)
IV. Sensitivity Analysis (model adjustments)
# IV. Sensitivity Analysis (variation between studies)
rm(list=ls(all=TRUE)) #Deletes all memory content
# Call of the packages
library(readxl)
data_neumo1 <- read_excel("C:/Users/USER/Desktop/RSMA/data_neumo1.xlsx")
View(data_neumo1)
#install.packages(c("metafor", "meta"))
#install.packages("tidyverse")
library(metafor)
library(meta)
library(metadat)
library(tidyverse)
# transformation data
ies.logit=escalc(xi=posit, ni=sample, data=data_neumo1, measure="PLO")
pes.logit=rma(yi, vi, data=ies.logit)
pes=predict(pes.logit, transf=transf.ilogit)
print(pes)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.0892 0.0756 0.1049 0.0138 0.4065# Accuracy of studies
precision=sqrt(ies.logit$vi)
1/sqrt(ies.logit$vi)## [1] 2.1831984 3.2217212 4.9330493 5.5444264 7.1323076 8.3380781
## [7] 6.9712798 8.6738654 6.7021435 1.9808608 9.4106323 3.4698703
## [13] 4.9183331 0.7058246 4.0776926 3.8229211 2.2259178 2.8031163
## [19] 6.0777968 8.5127864 11.6055385 5.4784187 2.0000000 0.9701425
## [25] 3.1495465 4.2426407 2.3299295 2.1612618 0.6871843 3.1901208
## [31] 0.7068421 6.3228802 6.0801994 4.9901088 3.7994855 5.9803600
## [37] 8.7302309 31.8608701 9.6303222 4.9790470 17.2146922 2.8618176
## [43] 3.4292272 3.6115756 3.6115756 7.8408211 7.9983086 4.1699392
## [49] 5.2669743 2.4354792 13.4239708 9.0404077 10.1768744 6.5206332
## [55] 6.7631010 3.6030290 17.9881944 10.3692527 11.0705058 9.5767338
## [61] 14.4016579 7.3476497 5.0584278 7.3048641 11.2570533 5.6655701
## [67] 2.4071323 2.7610740 2.9195084 5.4195190 5.2541777 6.8668098
## [73] 4.1623311 9.6778661 33.8658477 0.9984114 0.7065471 3.8094528
## [79] 4.9646752 6.7019317 2.7519690 1.6916082 1.6903085 1.7190113
## [85] 2.4124676 4.4249294 16.1063465 14.8776905 5.5646994 2.6261287
## [91] 8.7316908 8.6075207 6.5553201 6.4462434 7.9524619 11.2668996
## [97] 20.6392747 6.4738279 7.8121604 3.3792536 7.4311355 4.8068764
## [103] 3.1080322 6.5186266 5.1164296 2.2186934 5.6690816 0.7067566
## [109] 4.5869034 7.7907062 8.0617879 3.3752637 4.5496071 3.4248100
## [115] 3.0410722 4.2209113 8.5127864 6.0777968 7.5213980 4.7076160
## [121] 0.9981114 4.0960174 14.4648114 13.0609876 14.0457770 10.4099761
## [127] 16.0515719sortvar=precision
pes.summary=metaprop(posit, sample, author, data=data_neumo1, sm="PLO")
forest(pes.summary)
# Outliers
stud.res=rstudent(pes.logit)
abs.z=abs(stud.res$z)
stud.res[order(-abs.z)]##
## resid se z
## 123 2.8129 0.9591 2.9330
## 31 -4.8883 1.7208 -2.8407
## 108 -4.6074 1.7218 -2.6758
## 76 -3.4420 1.4004 -2.4580
## 77 -4.1363 1.7236 -2.3999
## 121 -3.2677 1.4017 -2.3313
## 96 2.2419 0.9765 2.2958
## 17 -2.3909 1.0729 -2.2285
## 21 2.0643 0.9803 2.1057
## 50 -2.1563 1.0602 -2.0339
## 97 1.9473 0.9800 1.9870
## 13 1.9744 1.0005 1.9733
## 14 -3.3030 1.7269 -1.9127
## 75 1.8743 0.9807 1.9111
## 56 1.8555 1.0213 1.8168
## 99 1.7214 0.9919 1.7355
## 106 -1.8433 1.0817 -1.7041
## 62 -1.6686 0.9914 -1.6831
## 101 1.6496 0.9940 1.6596
## 84 -1.8738 1.1440 -1.6379
## 18 -1.7000 1.0466 -1.6243
## 43 -1.5799 1.0272 -1.5380
## 55 1.5216 0.9980 1.5247
## 124 1.4927 0.9901 1.5076
## 10 -1.6297 1.1085 -1.4702
## 15 -1.4880 1.0160 -1.4646
## 8 -1.4425 0.9925 -1.4533
## 116 1.4267 1.0169 1.4031
## 22 1.3296 1.0065 1.3210
## 53 1.2897 0.9949 1.2963
## 16 -1.3233 1.0224 -1.2942
## 6 1.2905 0.9974 1.2939
## 52 1.2728 0.9965 1.2773
## 85 -1.1610 1.0744 -1.0806
## 78 -1.0773 1.0256 -1.0505
## 32 1.0442 1.0056 1.0384
## 119 1.0334 1.0020 1.0314
## 103 -1.0307 1.0430 -0.9882
## 67 -1.0218 1.0759 -0.9498
## 120 -0.9588 1.0150 -0.9446
## 9 0.9323 1.0052 0.9275
## 54 0.9316 1.0059 0.9261
## 72 -0.9238 1.0034 -0.9207
## 20 0.9128 1.0012 0.9117
## 117 0.9128 1.0012 0.9117
## 73 -0.9289 1.0215 -0.9093
## 98 0.9077 1.0062 0.9021
## 35 -0.9262 1.0272 -0.9016
## 23 0.9443 1.1131 0.8484
## 11 0.8148 1.0007 0.8142
## 49 -0.7750 1.0122 -0.7657
## 109 -0.7692 1.0179 -0.7556
## 94 0.7372 1.0076 0.7316
## 80 0.7322 1.0068 0.7272
## 64 0.7178 1.0051 0.7141
## 68 -0.6893 1.0588 -0.6510
## 93 0.6522 1.0078 0.6471
## 1 -0.6963 1.0952 -0.6358
## 100 -0.6600 1.0382 -0.6357
## 127 0.6142 0.9984 0.6152
## 82 -0.7089 1.1571 -0.6126
## 104 0.5960 1.0082 0.5911
## 112 -0.6118 1.0386 -0.5891
## 83 -0.6759 1.1575 -0.5839
## 79 -0.5837 1.0158 -0.5746
## 70 -0.5727 1.0127 -0.5655
## 95 0.5579 1.0046 0.5553
## 69 -0.5643 1.0530 -0.5359
## 38 0.5295 0.9974 0.5309
## 81 -0.5564 1.0599 -0.5249
## 107 -0.5288 1.0115 -0.5228
## 61 0.5084 0.9994 0.5087
## 66 -0.5062 1.0117 -0.5003
## 12 0.5132 1.0376 0.4946
## 2 -0.4966 1.0433 -0.4760
## 126 -0.4733 1.0010 -0.4728
## 90 0.4955 1.0669 0.4645
## 111 -0.4657 1.0041 -0.4638
## 36 -0.4520 1.0104 -0.4474
## 113 -0.4462 1.0204 -0.4373
## 44 0.4308 1.0348 0.4163
## 45 0.4308 1.0348 0.4163
## 3 -0.3606 1.0173 -0.3545
## 7 -0.3379 1.0073 -0.3354
## 58 0.3223 1.0023 0.3215
## 41 0.3159 0.9994 0.3161
## 24 -0.4503 1.4325 -0.3144
## 28 -0.3312 1.0986 -0.3015
## 29 -0.5106 1.7623 -0.2897
## 122 -0.2970 1.0265 -0.2893
## 4 -0.2885 1.0134 -0.2846
## 37 0.2740 1.0043 0.2728
## 125 0.2495 1.0004 0.2494
## 88 0.2467 1.0001 0.2467
## 59 -0.2408 1.0016 -0.2404
## 34 0.2427 1.0176 0.2385
## 60 0.2314 1.0033 0.2307
## 86 0.2357 1.0229 0.2304
## 39 -0.2253 1.0029 -0.2246
## 87 0.2207 0.9998 0.2208
## 51 0.2141 1.0007 0.2140
## 5 0.2103 1.0076 0.2087
## 110 0.2090 1.0061 0.2078
## 65 -0.1890 1.0016 -0.1887
## 30 -0.1918 1.0453 -0.1835
## 115 -0.1866 1.0500 -0.1777
## 102 -0.1784 1.0190 -0.1751
## 91 0.1620 1.0045 0.1612
## 26 0.1284 1.0252 0.1252
## 71 -0.1217 1.0156 -0.1198
## 118 0.1175 1.0114 0.1162
## 19 0.1175 1.0114 0.1162
## 33 0.1095 1.0113 0.1082
## 25 0.1081 1.0468 0.1033
## 114 0.1028 1.0395 0.0989
## 105 0.0980 1.0168 0.0963
## 40 -0.0941 1.0177 -0.0925
## 92 0.0883 1.0047 0.0878
## 46 -0.0862 1.0059 -0.0857
## 27 0.0737 1.0856 0.0679
## 57 0.0549 0.9996 0.0550
## 48 -0.0545 1.0261 -0.0531
## 47 -0.0380 1.0057 -0.0377
## 63 -0.0373 1.0172 -0.0367
## 74 0.0192 1.0033 0.0192
## 42 0.0110 1.0569 0.0104
## 89 0.0016 1.0139 0.0016# Influence of outliers
L1O=leave1out(pes.logit, transf=transf.ilogit); print(L1O)##
## estimate zval pval ci.lb ci.ub Q Qp tau2 I2
## 1 0.0895 -25.0961 0.0000 0.0758 0.1055 7549.4440 0.0000 0.9812 98.5279
## 2 0.0894 -25.0709 0.0000 0.0757 0.1054 7544.0468 0.0000 0.9836 98.5305
## 3 0.0894 -25.0578 0.0000 0.0756 0.1053 7531.8905 0.0000 0.9852 98.5304
## 4 0.0893 -25.0561 0.0000 0.0756 0.1052 7529.3150 0.0000 0.9858 98.5302
## 5 0.0890 -25.0862 0.0000 0.0753 0.1048 7546.7988 0.0000 0.9871 98.5284
## 6 0.0883 -25.3565 0.0000 0.0748 0.1040 7533.7739 0.0000 0.9719 98.5024
## 7 0.0894 -25.0533 0.0000 0.0756 0.1053 7507.5023 0.0000 0.9855 98.5266
## 8 0.0903 -25.1991 0.0000 0.0765 0.1061 7219.0514 0.0000 0.9634 98.4885
## 9 0.0885 -25.2352 0.0000 0.0749 0.1043 7555.9303 0.0000 0.9797 98.5186
## 10 0.0901 -25.2095 0.0000 0.0764 0.1060 7537.6623 0.0000 0.9655 98.5045
## 11 0.0886 -25.2026 0.0000 0.0750 0.1043 7557.1724 0.0000 0.9816 98.5136
## 12 0.0888 -25.1415 0.0000 0.0752 0.1046 7558.1152 0.0000 0.9849 98.5321
## 13 0.0880 -25.6553 0.0000 0.0747 0.1034 7519.1281 0.0000 0.9514 98.4790
## 14 0.0900 -25.2704 0.0000 0.0763 0.1058 7550.6071 0.0000 0.9664 98.5066
## 15 0.0902 -25.2034 0.0000 0.0765 0.1061 7480.7094 0.0000 0.9637 98.4994
## 16 0.0901 -25.1647 0.0000 0.0764 0.1060 7500.0619 0.0000 0.9685 98.5072
## 17 0.0908 -25.4256 0.0000 0.0771 0.1065 7512.1630 0.0000 0.9410 98.4660
## 18 0.0903 -25.2448 0.0000 0.0766 0.1062 7514.2583 0.0000 0.9598 98.4950
## 19 0.0890 -25.0773 0.0000 0.0753 0.1049 7546.4393 0.0000 0.9872 98.5310
## 20 0.0885 -25.2299 0.0000 0.0750 0.1043 7554.9956 0.0000 0.9800 98.5141
## 21 0.0879 -25.7381 0.0000 0.0746 0.1033 7304.4411 0.0000 0.9454 98.4491
## 22 0.0883 -25.3662 0.0000 0.0748 0.1039 7546.4282 0.0000 0.9713 98.5087
## 23 0.0886 -25.2323 0.0000 0.0750 0.1044 7558.2411 0.0000 0.9804 98.5268
## 24 0.0893 -25.1678 0.0000 0.0756 0.1051 7557.2827 0.0000 0.9811 98.5285
## 25 0.0890 -25.0906 0.0000 0.0754 0.1049 7555.1618 0.0000 0.9865 98.5348
## 26 0.0890 -25.0841 0.0000 0.0753 0.1049 7552.8524 0.0000 0.9869 98.5340
## 27 0.0891 -25.1018 0.0000 0.0754 0.1049 7556.4509 0.0000 0.9858 98.5346
## 28 0.0893 -25.0894 0.0000 0.0756 0.1052 7553.6767 0.0000 0.9843 98.5326
## 29 0.0893 -25.2068 0.0000 0.0756 0.1051 7557.8198 0.0000 0.9797 98.5265
## 30 0.0892 -25.0718 0.0000 0.0755 0.1051 7550.6611 0.0000 0.9859 98.5339
## 31 0.0904 -25.3960 0.0000 0.0768 0.1062 7543.0553 0.0000 0.9515 98.4834
## 32 0.0885 -25.2688 0.0000 0.0749 0.1042 7553.5921 0.0000 0.9776 98.5163
## 33 0.0890 -25.0765 0.0000 0.0754 0.1049 7546.0882 0.0000 0.9872 98.5310
## 34 0.0889 -25.0941 0.0000 0.0753 0.1048 7553.5280 0.0000 0.9868 98.5326
## 35 0.0898 -25.0990 0.0000 0.0760 0.1057 7521.2617 0.0000 0.9774 98.5206
## 36 0.0894 -25.0565 0.0000 0.0757 0.1054 7512.2629 0.0000 0.9844 98.5272
## 37 0.0889 -25.0933 0.0000 0.0753 0.1048 7545.2542 0.0000 0.9869 98.5237
## 38 0.0887 -25.1350 0.0000 0.0751 0.1046 7528.5917 0.0000 0.9852 98.3577
## 39 0.0893 -25.0523 0.0000 0.0756 0.1052 7480.8927 0.0000 0.9864 98.5201
## 40 0.0892 -25.0639 0.0000 0.0755 0.1051 7543.3706 0.0000 0.9868 98.5326
## 41 0.0889 -25.0976 0.0000 0.0752 0.1048 7515.7530 0.0000 0.9868 98.4847
## 42 0.0891 -25.0867 0.0000 0.0754 0.1050 7554.7516 0.0000 0.9863 98.5348
## 43 0.0903 -25.2224 0.0000 0.0766 0.1062 7498.7664 0.0000 0.9618 98.4974
## 44 0.0888 -25.1269 0.0000 0.0752 0.1047 7557.6169 0.0000 0.9855 98.5329
## 45 0.0888 -25.1269 0.0000 0.0752 0.1047 7557.6169 0.0000 0.9855 98.5329
## 46 0.0892 -25.0590 0.0000 0.0755 0.1051 7521.5890 0.0000 0.9870 98.5265
## 47 0.0891 -25.0618 0.0000 0.0754 0.1050 7524.6928 0.0000 0.9872 98.5263
## 48 0.0891 -25.0698 0.0000 0.0755 0.1050 7548.9372 0.0000 0.9867 98.5338
## 49 0.0897 -25.0783 0.0000 0.0759 0.1056 7499.6079 0.0000 0.9799 98.5220
## 50 0.0906 -25.3639 0.0000 0.0770 0.1064 7511.1540 0.0000 0.9471 98.4756
## 51 0.0890 -25.0839 0.0000 0.0753 0.1048 7517.1032 0.0000 0.9872 98.5058
## 52 0.0883 -25.3503 0.0000 0.0748 0.1040 7531.0944 0.0000 0.9723 98.5009
## 53 0.0883 -25.3575 0.0000 0.0748 0.1040 7521.6324 0.0000 0.9718 98.4962
## 54 0.0885 -25.2349 0.0000 0.0750 0.1043 7556.0832 0.0000 0.9797 98.5190
## 55 0.0882 -25.4483 0.0000 0.0748 0.1038 7527.3063 0.0000 0.9657 98.4973
## 56 0.0881 -25.5697 0.0000 0.0747 0.1036 7541.1186 0.0000 0.9576 98.4908
## 57 0.0891 -25.0663 0.0000 0.0754 0.1050 7424.2900 0.0000 0.9874 98.4808
## 58 0.0889 -25.0996 0.0000 0.0752 0.1047 7543.8279 0.0000 0.9867 98.5179
## 59 0.0893 -25.0514 0.0000 0.0756 0.1052 7452.1056 0.0000 0.9864 98.5147
## 60 0.0889 -25.0872 0.0000 0.0753 0.1048 7539.1262 0.0000 0.9871 98.5213
## 61 0.0888 -25.1312 0.0000 0.0751 0.1046 7551.4242 0.0000 0.9854 98.4983
## 62 0.0904 -25.2647 0.0000 0.0768 0.1063 7261.3633 0.0000 0.9560 98.4808
## 63 0.0891 -25.0670 0.0000 0.0754 0.1050 7545.0580 0.0000 0.9869 98.5327
## 64 0.0886 -25.1783 0.0000 0.0750 0.1044 7558.4600 0.0000 0.9830 98.5220
## 65 0.0892 -25.0526 0.0000 0.0755 0.1052 7460.3006 0.0000 0.9867 98.5145
## 66 0.0895 -25.0590 0.0000 0.0757 0.1054 7513.0018 0.0000 0.9838 98.5269
## 67 0.0898 -25.1185 0.0000 0.0761 0.1057 7541.7338 0.0000 0.9765 98.5207
## 68 0.0896 -25.0851 0.0000 0.0759 0.1055 7544.1688 0.0000 0.9813 98.5276
## 69 0.0895 -25.0767 0.0000 0.0758 0.1054 7545.2434 0.0000 0.9828 98.5297
## 70 0.0895 -25.0626 0.0000 0.0758 0.1054 7512.1372 0.0000 0.9830 98.5262
## 71 0.0892 -25.0617 0.0000 0.0755 0.1051 7540.4457 0.0000 0.9867 98.5320
## 72 0.0898 -25.0941 0.0000 0.0761 0.1057 7436.8432 0.0000 0.9771 98.5143
## 73 0.0898 -25.0982 0.0000 0.0761 0.1057 7513.6512 0.0000 0.9773 98.5199
## 74 0.0891 -25.0649 0.0000 0.0754 0.1050 7516.3106 0.0000 0.9873 98.5213
## 75 0.0880 -25.6349 0.0000 0.0747 0.1034 5744.4993 0.0000 0.9526 98.2803
## 76 0.0905 -25.3906 0.0000 0.0769 0.1063 7541.6421 0.0000 0.9495 98.4803
## 77 0.0902 -25.3287 0.0000 0.0766 0.1060 7546.9471 0.0000 0.9592 98.4954
## 78 0.0899 -25.1199 0.0000 0.0762 0.1058 7513.7578 0.0000 0.9744 98.5161
## 79 0.0895 -25.0644 0.0000 0.0758 0.1055 7518.9309 0.0000 0.9828 98.5268
## 80 0.0886 -25.1819 0.0000 0.0750 0.1044 7558.4235 0.0000 0.9828 98.5232
## 81 0.0895 -25.0788 0.0000 0.0758 0.1054 7546.8687 0.0000 0.9829 98.5299
## 82 0.0895 -25.1129 0.0000 0.0758 0.1054 7552.9592 0.0000 0.9810 98.5279
## 83 0.0895 -25.1119 0.0000 0.0758 0.1054 7553.2253 0.0000 0.9813 98.5284
## 84 0.0902 -25.2456 0.0000 0.0766 0.1061 7539.3234 0.0000 0.9620 98.4994
## 85 0.0899 -25.1358 0.0000 0.0762 0.1058 7538.8165 0.0000 0.9739 98.5170
## 86 0.0890 -25.0952 0.0000 0.0753 0.1048 7554.4709 0.0000 0.9867 98.5334
## 87 0.0890 -25.0844 0.0000 0.0753 0.1048 7500.0212 0.0000 0.9872 98.4918
## 88 0.0889 -25.0880 0.0000 0.0753 0.1048 7514.1452 0.0000 0.9871 98.4984
## 89 0.0891 -25.0683 0.0000 0.0754 0.1050 7543.9120 0.0000 0.9871 98.5320
## 90 0.0888 -25.1453 0.0000 0.0752 0.1046 7558.2330 0.0000 0.9847 98.5328
## 91 0.0890 -25.0792 0.0000 0.0753 0.1049 7537.2117 0.0000 0.9872 98.5242
## 92 0.0890 -25.0716 0.0000 0.0754 0.1049 7531.6748 0.0000 0.9873 98.5247
## 93 0.0887 -25.1633 0.0000 0.0751 0.1045 7558.4264 0.0000 0.9838 98.5250
## 94 0.0886 -25.1832 0.0000 0.0750 0.1044 7558.4114 0.0000 0.9827 98.5237
## 95 0.0887 -25.1424 0.0000 0.0751 0.1046 7557.3617 0.0000 0.9849 98.5230
## 96 0.0878 -25.8485 0.0000 0.0746 0.1031 7253.2388 0.0000 0.9376 98.4378
## 97 0.0880 -25.6746 0.0000 0.0747 0.1034 6863.3156 0.0000 0.9498 98.4029
## 98 0.0885 -25.2280 0.0000 0.0750 0.1043 7556.5682 0.0000 0.9801 98.5197
## 99 0.0881 -25.5438 0.0000 0.0747 0.1036 7494.2899 0.0000 0.9591 98.4843
## 100 0.0896 -25.0768 0.0000 0.0758 0.1055 7537.9328 0.0000 0.9817 98.5275
## 101 0.0881 -25.5082 0.0000 0.0747 0.1037 7508.2598 0.0000 0.9616 98.4892
## 102 0.0892 -25.0609 0.0000 0.0755 0.1051 7541.2205 0.0000 0.9864 98.5324
## 103 0.0898 -25.1150 0.0000 0.0761 0.1057 7530.2681 0.0000 0.9757 98.5189
## 104 0.0887 -25.1511 0.0000 0.0751 0.1045 7558.1051 0.0000 0.9844 98.5260
## 105 0.0890 -25.0777 0.0000 0.0754 0.1049 7549.3708 0.0000 0.9871 98.5328
## 106 0.0904 -25.2639 0.0000 0.0767 0.1062 7527.3484 0.0000 0.9585 98.4936
## 107 0.0895 -25.0599 0.0000 0.0758 0.1054 7511.2122 0.0000 0.9835 98.5265
## 108 0.0904 -25.3692 0.0000 0.0767 0.1061 7544.5747 0.0000 0.9545 98.4881
## 109 0.0897 -25.0794 0.0000 0.0759 0.1056 7514.2162 0.0000 0.9801 98.5234
## 110 0.0890 -25.0855 0.0000 0.0753 0.1048 7544.4498 0.0000 0.9871 98.5268
## 111 0.0894 -25.0544 0.0000 0.0757 0.1054 7472.1867 0.0000 0.9843 98.5219
## 112 0.0895 -25.0741 0.0000 0.0758 0.1055 7539.4157 0.0000 0.9823 98.5285
## 113 0.0894 -25.0605 0.0000 0.0757 0.1053 7532.0508 0.0000 0.9844 98.5298
## 114 0.0890 -25.0873 0.0000 0.0754 0.1049 7554.4804 0.0000 0.9866 98.5347
## 115 0.0892 -25.0738 0.0000 0.0755 0.1051 7551.4603 0.0000 0.9858 98.5340
## 116 0.0883 -25.3964 0.0000 0.0748 0.1039 7548.9901 0.0000 0.9694 98.5080
## 117 0.0885 -25.2299 0.0000 0.0750 0.1043 7554.9956 0.0000 0.9800 98.5141
## 118 0.0890 -25.0773 0.0000 0.0753 0.1049 7546.4393 0.0000 0.9872 98.5310
## 119 0.0885 -25.2661 0.0000 0.0749 0.1042 7551.9657 0.0000 0.9777 98.5136
## 120 0.0898 -25.1011 0.0000 0.0761 0.1057 7498.9758 0.0000 0.9766 98.5180
## 121 0.0904 -25.3631 0.0000 0.0768 0.1062 7543.0432 0.0000 0.9526 98.4851
## 122 0.0893 -25.0618 0.0000 0.0756 0.1052 7542.3150 0.0000 0.9856 98.5322
## 123 0.0876 -26.2801 0.0000 0.0746 0.1026 6606.4480 0.0000 0.9071 98.3704
## 124 0.0882 -25.4427 0.0000 0.0748 0.1038 7448.6474 0.0000 0.9660 98.4753
## 125 0.0889 -25.0885 0.0000 0.0753 0.1048 7519.5656 0.0000 0.9871 98.5026
## 126 0.0895 -25.0533 0.0000 0.0757 0.1054 7411.9864 0.0000 0.9843 98.5141
## 127 0.0887 -25.1532 0.0000 0.0751 0.1045 7556.8995 0.0000 0.9843 98.4877
## H2
## 1 67.9315
## 2 68.0509
## 3 68.0445
## 4 68.0352
## 5 67.9551
## 6 66.7755
## 7 67.8697
## 8 66.1593
## 9 67.5017
## 10 66.8683
## 11 67.2786
## 12 68.1260
## 13 65.7482
## 14 66.9597
## 15 66.6420
## 16 66.9876
## 17 65.1871
## 18 66.4473
## 19 68.0755
## 20 67.2992
## 21 64.4798
## 22 67.0548
## 23 67.8816
## 24 67.9582
## 25 68.2503
## 26 68.2110
## 27 68.2412
## 28 68.1470
## 29 67.8650
## 30 68.2067
## 31 65.9368
## 32 67.4013
## 33 68.0758
## 34 68.1472
## 35 67.5961
## 36 67.8973
## 37 67.7382
## 38 60.8918
## 39 67.5732
## 40 68.1485
## 41 65.9918
## 42 68.2499
## 43 66.5533
## 44 68.1610
## 45 68.1610
## 46 67.8671
## 47 67.8563
## 48 68.2058
## 49 67.6598
## 50 65.5983
## 51 66.9250
## 52 66.7051
## 53 66.4987
## 54 67.5225
## 55 66.5483
## 56 66.2588
## 57 65.8249
## 58 67.4725
## 59 67.3285
## 60 67.6252
## 61 66.5904
## 62 65.8237
## 63 68.1534
## 64 67.6575
## 65 67.3162
## 66 67.8852
## 67 67.6017
## 68 67.9168
## 69 68.0123
## 70 67.8514
## 71 68.1214
## 72 67.3085
## 73 67.5633
## 74 67.6259
## 75 58.1480
## 76 65.8031
## 77 66.4640
## 78 67.3883
## 79 67.8790
## 80 67.7128
## 81 68.0229
## 82 67.9310
## 83 67.9516
## 84 66.6402
## 85 67.4291
## 86 68.1856
## 87 66.3035
## 88 66.5943
## 89 68.1188
## 90 68.1549
## 91 67.7611
## 92 67.7839
## 93 67.7973
## 94 67.7355
## 95 67.7047
## 96 64.0134
## 97 62.6122
## 98 67.5556
## 99 65.9774
## 100 67.9136
## 101 66.1918
## 102 68.1369
## 103 67.5192
## 104 67.8441
## 105 68.1570
## 106 66.3838
## 107 67.8667
## 108 66.1423
## 109 67.7230
## 110 67.8775
## 111 67.6544
## 112 67.9559
## 113 68.0169
## 114 68.2450
## 115 68.2107
## 116 67.0230
## 117 67.2992
## 118 68.0755
## 119 67.2767
## 120 67.4769
## 121 66.0099
## 122 68.1310
## 123 61.3634
## 124 65.5856
## 125 66.7824
## 126 67.3012
## 127 66.1258# Weight of influence of outliers
inf=influence(pes.logit)
print(inf); plot(inf)##
## rstudent dffits cook.d cov.r tau2.del QE.del hat weight dfbs inf
## 1 -0.6358 -0.0505 0.0026 1.0113 0.9812 7549.4440 0.0071 0.7116 -0.0505
## 2 -0.4760 -0.0372 0.0014 1.0144 0.9836 7544.0468 0.0079 0.7868 -0.0372
## 3 -0.3545 -0.0260 0.0007 1.0164 0.9852 7531.8905 0.0083 0.8295 -0.0260
## 4 -0.2846 -0.0193 0.0004 1.0171 0.9858 7529.3150 0.0084 0.8366 -0.0193
## 5 0.2087 0.0271 0.0007 1.0184 0.9871 7546.7988 0.0085 0.8474 0.0271
## 6 1.2939 0.1161 0.0134 1.0038 0.9719 7533.7739 0.0085 0.8519 0.1160
## 7 -0.3354 -0.0243 0.0006 1.0169 0.9855 7507.5023 0.0085 0.8466 -0.0243
## 8 -1.4533 -0.1455 0.0209 0.9956 0.9634 7219.0514 0.0085 0.8528 -0.1454
## 9 0.9275 0.0878 0.0077 1.0112 0.9797 7555.9303 0.0085 0.8452 0.0878
## 10 -1.4702 -0.1309 0.0170 0.9959 0.9655 7537.6623 0.0069 0.6856 -0.1310
## 11 0.8142 0.0792 0.0063 1.0131 0.9816 7557.1724 0.0085 0.8545 0.0792
## 12 0.4946 0.0505 0.0026 1.0158 0.9849 7558.1152 0.0080 0.7967 0.0505
## 13 1.9733 0.1602 0.0250 0.9838 0.9514 7519.1281 0.0083 0.8293 0.1602
## 14 -1.9127 -0.1094 0.0119 0.9928 0.9664 7550.6071 0.0028 0.2830 -0.1098
## 15 -1.4646 -0.1431 0.0202 0.9955 0.9637 7480.7094 0.0081 0.8143 -0.1431
## 16 -1.2942 -0.1234 0.0151 1.0001 0.9685 7500.0619 0.0081 0.8078 -0.1234
## 17 -2.2285 -0.2175 0.0459 0.9725 0.9410 7512.1630 0.0072 0.7164 -0.2179
## 18 -1.6243 -0.1560 0.0240 0.9912 0.9598 7514.2583 0.0076 0.7648 -0.1561
## 19 0.1162 0.0186 0.0003 1.0184 0.9872 7546.4393 0.0084 0.8411 0.0186
## 20 0.9117 0.0869 0.0076 1.0116 0.9800 7554.9956 0.0085 0.8524 0.0869
## 21 2.1057 0.1709 0.0283 0.9782 0.9454 7304.4411 0.0086 0.8579 0.1707
## 22 1.3210 0.1170 0.0136 1.0031 0.9713 7546.4282 0.0084 0.8359 0.1169
## 23 0.8484 0.0734 0.0054 1.0103 0.9804 7558.2411 0.0069 0.6883 0.0733
## 24 -0.3144 -0.0170 0.0003 1.0082 0.9811 7557.2827 0.0041 0.4141 -0.0170
## 25 0.1033 0.0166 0.0003 1.0172 0.9865 7555.1618 0.0078 0.7836 0.0166
## 26 0.1252 0.0190 0.0004 1.0179 0.9869 7552.8524 0.0082 0.8179 0.0191
## 27 0.0679 0.0127 0.0002 1.0159 0.9858 7556.4509 0.0073 0.7273 0.0127
## 28 -0.3015 -0.0198 0.0004 1.0143 0.9843 7553.6767 0.0071 0.7090 -0.0197
## 29 -0.2897 -0.0130 0.0002 1.0054 0.9797 7557.8198 0.0027 0.2729 -0.0130
## 30 -0.1835 -0.0094 0.0001 1.0166 0.9859 7550.6611 0.0079 0.7854 -0.0094
## 31 -2.8407 -0.1692 0.0284 0.9784 0.9515 7543.0553 0.0028 0.2835 -0.1706
## 32 1.0384 0.0963 0.0093 1.0092 0.9776 7553.5921 0.0084 0.8428 0.0963
## 33 0.1082 0.0179 0.0003 1.0184 0.9872 7546.0882 0.0084 0.8411 0.0179
## 34 0.2385 0.0294 0.0009 1.0179 0.9868 7553.5280 0.0083 0.8303 0.0294
## 35 -0.9016 -0.0809 0.0066 1.0087 0.9774 7521.2617 0.0081 0.8072 -0.0809
## 36 -0.4474 -0.0354 0.0013 1.0157 0.9844 7512.2629 0.0084 0.8404 -0.0354
## 37 0.2728 0.0330 0.0011 1.0183 0.9869 7545.2542 0.0085 0.8530 0.0330
## 38 0.5309 0.0559 0.0032 1.0167 0.9852 7528.5917 0.0086 0.8636 0.0559
## 39 -0.2246 -0.0135 0.0002 1.0178 0.9864 7480.8927 0.0085 0.8550 -0.0135
## 40 -0.0925 -0.0009 0.0000 1.0179 0.9868 7543.3706 0.0083 0.8301 -0.0009
## 41 0.3161 0.0371 0.0014 1.0182 0.9868 7515.7530 0.0086 0.8614 0.0371
## 42 0.0104 0.0081 0.0001 1.0168 0.9863 7554.7516 0.0077 0.7684 0.0081
## 43 -1.5380 -0.1496 0.0221 0.9935 0.9618 7498.7664 0.0080 0.7952 -0.1496
## 44 0.4163 0.0441 0.0020 1.0164 0.9855 7557.6169 0.0080 0.8015 0.0441
## 45 0.4163 0.0441 0.0020 1.0164 0.9855 7557.6169 0.0080 0.8015 0.0441
## 46 -0.0857 -0.0001 0.0000 1.0184 0.9870 7521.5890 0.0085 0.8503 -0.0001
## 47 -0.0377 0.0044 0.0000 1.0185 0.9872 7524.6928 0.0085 0.8508 0.0044
## 48 -0.0531 0.0027 0.0000 1.0177 0.9867 7548.9372 0.0082 0.8164 0.0028
## 49 -0.7657 -0.0678 0.0046 1.0114 0.9799 7499.6079 0.0083 0.8337 -0.0678
## 50 -2.0339 -0.1987 0.0385 0.9787 0.9471 7511.1540 0.0074 0.7372 -0.1989
## 51 0.2140 0.0278 0.0008 1.0186 0.9872 7517.1032 0.0086 0.8595 0.0278
## 52 1.2773 0.1149 0.0132 1.0042 0.9723 7531.0944 0.0085 0.8537 0.1149
## 53 1.2963 0.1165 0.0135 1.0037 0.9718 7521.6324 0.0086 0.8560 0.1165
## 54 0.9261 0.0876 0.0077 1.0112 0.9797 7556.0832 0.0084 0.8441 0.0876
## 55 1.5247 0.1321 0.0172 0.9977 0.9657 7527.3063 0.0085 0.8455 0.1320
## 56 1.8168 0.1480 0.0215 0.9895 0.9576 7541.1186 0.0080 0.8012 0.1480
## 57 0.0550 0.0132 0.0002 1.0189 0.9874 7424.2900 0.0086 0.8617 0.0132
## 58 0.3215 0.0374 0.0014 1.0181 0.9867 7543.8279 0.0086 0.8563 0.0374
## 59 -0.2404 -0.0151 0.0002 1.0178 0.9864 7452.1056 0.0086 0.8573 -0.0151
## 60 0.2307 0.0292 0.0009 1.0185 0.9871 7539.1262 0.0085 0.8549 0.0293
## 61 0.5087 0.0539 0.0029 1.0169 0.9854 7551.4242 0.0086 0.8602 0.0539
## 62 -1.6831 -0.1725 0.0291 0.9884 0.9560 7261.3633 0.0085 0.8483 -0.1724
## 63 -0.0367 0.0044 0.0000 1.0181 0.9869 7545.0580 0.0083 0.8312 0.0044
## 64 0.7141 0.0707 0.0050 1.0144 0.9830 7558.4600 0.0085 0.8482 0.0707
## 65 -0.1887 -0.0100 0.0001 1.0181 0.9867 7460.3006 0.0086 0.8575 -0.0100
## 66 -0.5003 -0.0407 0.0017 1.0151 0.9838 7513.0018 0.0084 0.8377 -0.0407
## 67 -0.9498 -0.0820 0.0067 1.0070 0.9765 7541.7338 0.0073 0.7346 -0.0820
## 68 -0.6510 -0.0536 0.0029 1.0119 0.9813 7544.1688 0.0076 0.7621 -0.0536
## 69 -0.5359 -0.0427 0.0018 1.0135 0.9828 7545.2434 0.0077 0.7717 -0.0427
## 70 -0.5655 -0.0472 0.0022 1.0143 0.9830 7512.1372 0.0084 0.8353 -0.0472
## 71 -0.1198 -0.0034 0.0000 1.0179 0.9867 7540.4457 0.0083 0.8335 -0.0034
## 72 -0.9207 -0.0849 0.0072 1.0087 0.9771 7436.8432 0.0085 0.8461 -0.0849
## 73 -0.9093 -0.0822 0.0068 1.0086 0.9773 7513.6512 0.0082 0.8162 -0.0822
## 74 0.0192 0.0098 0.0001 1.0187 0.9873 7516.3106 0.0086 0.8551 0.0098
## 75 1.9111 0.1593 0.0247 0.9853 0.9526 5744.4993 0.0086 0.8637 0.1592
## 76 -2.4580 -0.1806 0.0322 0.9779 0.9495 7541.6421 0.0043 0.4265 -0.1817
## 77 -2.3999 -0.1405 0.0196 0.9858 0.9592 7546.9471 0.0028 0.2834 -0.1413
## 78 -1.0505 -0.0967 0.0093 1.0057 0.9744 7513.7578 0.0081 0.8075 -0.0967
## 79 -0.5746 -0.0480 0.0023 1.0141 0.9828 7518.9309 0.0083 0.8300 -0.0480
## 80 0.7272 0.0717 0.0052 1.0142 0.9828 7558.4235 0.0085 0.8452 0.0717
## 81 -0.5249 -0.0414 0.0017 1.0134 0.9829 7546.8687 0.0076 0.7615 -0.0414
## 82 -0.6126 -0.0459 0.0021 1.0103 0.9810 7552.9592 0.0064 0.6367 -0.0459
## 83 -0.5839 -0.0434 0.0019 1.0106 0.9813 7553.2253 0.0064 0.6364 -0.0434
## 84 -1.6379 -0.1430 0.0202 0.9921 0.9620 7539.3234 0.0064 0.6420 -0.1432
## 85 -1.0806 -0.0953 0.0091 1.0046 0.9739 7538.8165 0.0074 0.7351 -0.0953
## 86 0.2304 0.0285 0.0008 1.0177 0.9867 7554.4709 0.0082 0.8215 0.0285
## 87 0.2208 0.0285 0.0008 1.0186 0.9872 7500.0212 0.0086 0.8610 0.0285
## 88 0.2467 0.0308 0.0010 1.0185 0.9871 7514.1452 0.0086 0.8604 0.0308
## 89 0.0016 0.0080 0.0001 1.0183 0.9871 7543.9120 0.0084 0.8368 0.0080
## 90 0.4645 0.0465 0.0022 1.0151 0.9847 7558.2330 0.0075 0.7527 0.0465
## 91 0.1612 0.0229 0.0005 1.0186 0.9872 7537.2117 0.0085 0.8530 0.0229
## 92 0.0878 0.0162 0.0003 1.0187 0.9873 7531.6748 0.0085 0.8526 0.0162
## 93 0.6471 0.0650 0.0043 1.0152 0.9838 7558.4264 0.0084 0.8443 0.0650
## 94 0.7316 0.0720 0.0052 1.0141 0.9827 7558.4114 0.0084 0.8436 0.0720
## 95 0.5553 0.0575 0.0033 1.0163 0.9849 7557.3617 0.0085 0.8507 0.0576
## 96 2.2958 0.1821 0.0318 0.9707 0.9376 7253.2388 0.0086 0.8575 0.1819
## 97 1.9870 0.1640 0.0261 0.9826 0.9498 6863.3156 0.0086 0.8624 0.1638
## 98 0.9021 0.0857 0.0074 1.0116 0.9801 7556.5682 0.0084 0.8438 0.0857
## 99 1.7355 0.1467 0.0211 0.9914 0.9591 7494.2899 0.0085 0.8502 0.1467
## 100 -0.6357 -0.0531 0.0028 1.0127 0.9817 7537.9328 0.0079 0.7933 -0.0531
## 101 1.6596 0.1416 0.0197 0.9938 0.9616 7508.2598 0.0085 0.8487 0.1415
## 102 -0.1751 -0.0087 0.0001 1.0175 0.9864 7541.2205 0.0083 0.8278 -0.0087
## 103 -0.9882 -0.0886 0.0078 1.0068 0.9757 7530.2681 0.0078 0.7816 -0.0886
## 104 0.5911 0.0603 0.0037 1.0158 0.9844 7558.1051 0.0084 0.8441 0.0603
## 105 0.0963 0.0166 0.0003 1.0182 0.9871 7549.3708 0.0083 0.8319 0.0166
## 106 -1.7041 -0.1589 0.0249 0.9895 0.9585 7527.3484 0.0072 0.7156 -0.1591
## 107 -0.5228 -0.0429 0.0019 1.0148 0.9835 7511.2122 0.0084 0.8377 -0.0429
## 108 -2.6758 -0.1584 0.0249 0.9813 0.9545 7544.5747 0.0028 0.2835 -0.1595
## 109 -0.7556 -0.0664 0.0044 1.0114 0.9801 7514.2162 0.0082 0.8243 -0.0664
## 110 0.2078 0.0271 0.0007 1.0184 0.9871 7544.4498 0.0085 0.8501 0.0271
## 111 -0.4638 -0.0372 0.0014 1.0158 0.9843 7472.1867 0.0085 0.8510 -0.0372
## 112 -0.5891 -0.0485 0.0024 1.0133 0.9823 7539.4157 0.0079 0.7932 -0.0485
## 113 -0.4373 -0.0341 0.0012 1.0155 0.9844 7532.0508 0.0082 0.8237 -0.0341
## 114 0.0989 0.0163 0.0003 1.0174 0.9866 7554.4804 0.0080 0.7950 0.0163
## 115 -0.1777 -0.0089 0.0001 1.0165 0.9858 7551.4603 0.0078 0.7783 -0.0089
## 116 1.4031 0.1215 0.0147 1.0010 0.9694 7548.9901 0.0082 0.8175 0.1215
## 117 0.9117 0.0869 0.0076 1.0116 0.9800 7554.9956 0.0085 0.8524 0.0869
## 118 0.1162 0.0186 0.0003 1.0184 0.9872 7546.4393 0.0084 0.8411 0.0186
## 119 1.0314 0.0961 0.0092 1.0094 0.9777 7551.9657 0.0085 0.8491 0.0961
## 120 -0.9446 -0.0865 0.0075 1.0080 0.9766 7498.9758 0.0083 0.8263 -0.0865
## 121 -2.3313 -0.1701 0.0286 0.9809 0.9526 7543.0432 0.0043 0.4263 -0.1711
## 122 -0.2893 -0.0195 0.0004 1.0166 0.9856 7542.3150 0.0081 0.8147 -0.0195
## 123 2.9330 0.2157 0.0432 0.9412 0.9071 6606.4480 0.0086 0.8602 0.2152
## 124 1.5076 0.1319 0.0172 0.9982 0.9660 7448.6474 0.0086 0.8593 0.1319
## 125 0.2494 0.0310 0.0010 1.0185 0.9871 7519.5656 0.0086 0.8600 0.0311
## 126 -0.4728 -0.0382 0.0015 1.0158 0.9843 7411.9864 0.0086 0.8563 -0.0382
## 127 0.6152 0.0630 0.0040 1.0158 0.9843 7556.8995 0.0086 0.8610 0.0630
# Forest Plot - visualization of outliers
#l1o=leave1out(pes)
#yi=l1o$ estimate; vi=l1o$se^2
#forest(yi, vi,
#slab=paste(data_neumo1$author, sep=","),
#refline=pes$b,
#xlab=c(ies.logit)
#print(pes.logit)
#l1o=leave1out(pes.logit)
#yi=l1o$estimate; vi=l1o$se^2
#forest(yi, vi, transf=transf.ilogit,
#slab=paste(data_neumo1$author,sep=","),
#refline=pes$pred,
#xlab="Summary proportions leaving out each study")V. Metaregresion
rm(list=ls(all=TRUE)) #Deletes all memory content
library(readxl)
data_neumo1 <- read_excel("C:/Users/USER/Desktop/RSMA/data_neumo1.xlsx")
View(data_neumo1)
# Call of the packages
#install.packages(c("metafor", "meta"))
#install.packages("PerformanceAnalytics")
library(metafor)
library(meta)
library(tidyverse)
library(metadat)# Metaregression data structure
glimpse(data_neumo1)## Rows: 127
## Columns: 20
## $ Sequence <chr> "RSMA01", "RSMA02", "RSMA03", "RSMA04", "RSMA05", "RSMA06…
## $ code <chr> "RS001", "RS001", "RS001", "RS002", "RS002", "RS002", "RS…
## $ author_1 <chr> "Abdullahi", "Abdullahi", "Abdullahi", "Adetifa", "Adetif…
## $ year_pub <dbl> 2008, 2008, 2008, 2012, 2012, 2012, 2019, 2014, 2013, 201…
## $ author <chr> "Abdullahi_2008", "Abdullahi_2008", "Abdullahi_2008", "Ad…
## $ contint <dbl> 1, 1, 1, 1, 1, 1, 5, 5, 5, 2, 1, 2, 2, 6, 3, 3, 5, 5, 4, …
## $ age <dbl> 4, 2, 1, 4, 2, 1, 1, 4, 4, 4, 1, 4, 4, 1, 4, 4, 2, 4, 1, …
## $ prev <dbl> 0.046728972, 0.056410256, 0.064039409, 0.068464730, 0.107…
## $ sample <dbl> 107, 195, 406, 482, 530, 361, 795, 3361, 283, 210, 600, 1…
## $ posit <dbl> 5, 11, 26, 33, 57, 94, 52, 77, 56, 4, 108, 14, 41, 0, 17,…
## $ type_prev <dbl> 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, …
## $ stage <dbl> 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, …
## $ comorb <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, …
## $ type_morb <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ meth_iden <dbl> 1, 1, 1, 1, 1, 1, 1, 3, 2, 1, 1, 1, 2, 1, 3, 3, 1, 1, 1, …
## $ type_samp <dbl> 1, 1, 1, 1, 1, 1, 2, 3, 1, 1, 2, 3, 3, 1, 1, 1, 1, 1, 4, …
## $ setting <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 4, 1, 1, 1, 1, 2, …
## $ type_setting <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 10, 1, 1, 1, 1, 7,…
## $ type_study <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 4, 2, 2, 4, 4, 3, …
## $ quality <dbl> 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 4, …# Multicollinearity Analysis
data_neumo1[,c("prev","year_pub","age","sample","stage","type_prev","comorb","type_morb", "meth_iden",
"type_samp","quality")] %>% cor()## prev year_pub age sample stage
## prev 1.000000000 0.13868200 -0.17967540 0.07411439 0.02176720
## year_pub 0.138682005 1.00000000 0.22627526 0.03067928 0.39752442
## age -0.179675397 0.22627526 1.00000000 -0.13093882 0.11567383
## sample 0.074114394 0.03067928 -0.13093882 1.00000000 0.04390912
## stage 0.021767199 0.39752442 0.11567383 0.04390912 1.00000000
## type_prev 0.236530610 -0.18428735 -0.17725722 0.12161419 -0.04104637
## comorb -0.086607462 -0.10618728 0.07478766 0.15715092 0.04058524
## type_morb 0.081356109 0.16986434 -0.01084178 -0.15431325 0.02179090
## meth_iden -0.018333113 0.03447639 0.24005604 -0.01641210 0.09677704
## type_samp -0.009052112 0.19581058 0.10742613 -0.07424087 0.02474667
## quality -0.029441628 0.08528716 0.18837118 -0.11182899 0.12662086
## type_prev comorb type_morb meth_iden type_samp
## prev 0.23653061 -0.08660746 0.08135611 -0.01833311 -0.009052112
## year_pub -0.18428735 -0.10618728 0.16986434 0.03447639 0.195810580
## age -0.17725722 0.07478766 -0.01084178 0.24005604 0.107426133
## sample 0.12161419 0.15715092 -0.15431325 -0.01641210 -0.074240869
## stage -0.04104637 0.04058524 0.02179090 0.09677704 0.024746673
## type_prev 1.00000000 0.13652971 -0.12022355 -0.02858849 -0.086073483
## comorb 0.13652971 1.00000000 -0.94544037 0.10623960 0.212889711
## type_morb -0.12022355 -0.94544037 1.00000000 -0.08748310 -0.212806023
## meth_iden -0.02858849 0.10623960 -0.08748310 1.00000000 0.401702746
## type_samp -0.08607348 0.21288971 -0.21280602 0.40170275 1.000000000
## quality -0.19181043 -0.12561763 0.10478573 -0.01195179 0.021202178
## quality
## prev -0.02944163
## year_pub 0.08528716
## age 0.18837118
## sample -0.11182899
## stage 0.12662086
## type_prev -0.19181043
## comorb -0.12561763
## type_morb 0.10478573
## meth_iden -0.01195179
## type_samp 0.02120218
## quality 1.00000000# Correlation graph (authors: Peterson y Carl 2020)
library(PerformanceAnalytics)
data_neumo1[,c("prev","year_pub","age","sample","stage","type_prev","comorb","type_morb", "meth_iden",
"type_samp","quality")] %>% chart.Correlation()
warnings()
data_neumo1[,c("prev","age","comorb","type_morb", "meth_iden","type_samp")] %>% chart.Correlation()
warnings()
# Adjusted multiple metaregression model
m.qual <- rma(yi = prev,
sei = age,
data = data_neumo1,
method = "ML",
mods = ~ comorb,
test = "knha")
m.qual##
## Mixed-Effects Model (k = 127; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1675)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 125) = 0.9331, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 125) = 0.0867, p-val = 0.7689
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1386 0.0456 3.0384 125 0.0029 0.0483 0.2290 **
## comorb -0.0073 0.0247 -0.2945 125 0.7689 -0.0562 0.0416
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.qual <- rma(yi = prev,
sei = age,
data = data_neumo1,
method = "ML",
mods = ~ comorb,
test = "knha")
m.qual##
## Mixed-Effects Model (k = 127; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1675)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 125) = 0.9331, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 125) = 0.0867, p-val = 0.7689
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1386 0.0456 3.0384 125 0.0029 0.0483 0.2290 **
## comorb -0.0073 0.0247 -0.2945 125 0.7689 -0.0562 0.0416
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.qual <- rma(yi = prev,
sei = age,
data = data_neumo1,
method = "ML",
mods = ~ meth_iden,
test = "knha")
m.qual##
## Mixed-Effects Model (k = 127; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1675)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 125) = 0.9292, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 125) = 0.6038, p-val = 0.4386
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1066 0.0262 4.0635 125 <.0001 0.0547 0.1585 ***
## meth_iden 0.0163 0.0210 0.7771 125 0.4386 -0.0253 0.0580
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.qual <- rma(yi = prev,
sei = age,
data = data_neumo1,
method = "ML",
mods = ~ type_samp,
test = "knha")
m.qual##
## Mixed-Effects Model (k = 127; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1675)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 125) = 0.9204, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 125) = 1.8065, p-val = 0.1814
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1058 0.0176 6.0109 125 <.0001 0.0710 0.1407 ***
## type_samp 0.0116 0.0087 1.3441 125 0.1814 -0.0055 0.0288
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.qual <- rma(yi = prev,
sei = age,
data = data_neumo1,
method = "ML",
mods = ~ quality,
test = "knha")
m.qual##
## Mixed-Effects Model (k = 127; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1675)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 125) = 0.9110, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 125) = 3.1128, p-val = 0.0801
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0029 0.0702 0.0414 125 0.9671 -0.1360 0.1418
## quality 0.0379 0.0215 1.7643 125 0.0801 -0.0046 0.0805 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# fitted model with several predictors
m.qual.rep <- rma(yi = prev,
sei = age,
data = data_neumo1,
method = "ML",
mods = ~ stage + comorb + meth_iden + type_samp + quality,
test = "knha")
m.qual.rep##
## Mixed-Effects Model (k = 127; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1675)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 121) = 0.8961, p-val = 1.0000
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 121) = 1.0156, p-val = 0.4116
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.0060 0.1000 -0.0604 121 0.9520 -0.2040 0.1919
## stage -0.0106 0.0190 -0.5571 121 0.5785 -0.0482 0.0270
## comorb -0.0028 0.0257 -0.1087 121 0.9136 -0.0536 0.0480
## meth_iden 0.0177 0.0220 0.8045 121 0.4227 -0.0259 0.0613
## type_samp 0.0078 0.0094 0.8354 121 0.4051 -0.0107 0.0263
## quality 0.0375 0.0231 1.6250 121 0.1068 -0.0082 0.0832
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.qual.rep <- rma(yi = prev,
sei = type_samp,
data = data_neumo1,
method = "ML",
mods = ~ age + stage + comorb + meth_iden + quality,
test = "knha")
m.qual.rep##
## Mixed-Effects Model (k = 127; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1520)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 121) = 1.0027, p-val = 1.0000
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 121) = 0.2883, p-val = 0.9187
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1509 0.0884 1.7078 121 0.0902 -0.0240 0.3259 .
## age -0.0068 0.0082 -0.8286 121 0.4090 -0.0231 0.0095
## stage 0.0029 0.0201 0.1421 121 0.8872 -0.0370 0.0427
## comorb -0.0124 0.0232 -0.5351 121 0.5936 -0.0582 0.0334
## meth_iden -0.0103 0.0203 -0.5061 121 0.6137 -0.0505 0.0299
## quality 0.0009 0.0213 0.0399 121 0.9682 -0.0414 0.0431
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.qual.rep <- rma(yi = prev,
sei = meth_iden,
data = data_neumo1,
method = "ML",
mods = ~ age +stage + comorb + type_samp + quality,
test = "knha")
m.qual.rep##
## Mixed-Effects Model (k = 127; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1381)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 121) = 1.1589, p-val = 1.0000
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 121) = 1.0237, p-val = 0.4068
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1024 0.0878 1.1662 121 0.2458 -0.0714 0.2763
## age -0.0160 0.0077 -2.0738 121 0.0402 -0.0313 -0.0007 *
## stage 0.0063 0.0209 0.3027 121 0.7626 -0.0350 0.0477
## comorb -0.0099 0.0244 -0.4045 121 0.6866 -0.0583 0.0385
## type_samp -0.0023 0.0091 -0.2516 121 0.8018 -0.0204 0.0158
## quality 0.0156 0.0204 0.7651 121 0.4457 -0.0247 0.0559
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.qual.rep <- rma(yi = prev,
sei = comorb,
data = data_neumo1,
method = "ML",
mods = ~ age +stage + meth_iden + type_samp + quality,
test = "knha")
m.qual.rep##
## Mixed-Effects Model (k = 127; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.2604)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 121) = 0.5992, p-val = 1.0000
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 121) = 0.7054, p-val = 0.6204
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0925 0.0815 1.1347 121 0.2588 -0.0689 0.2538
## age -0.0148 0.0085 -1.7548 121 0.0818 -0.0316 0.0019 .
## stage 0.0150 0.0226 0.6642 121 0.5078 -0.0297 0.0597
## meth_iden 0.0133 0.0196 0.6773 121 0.4995 -0.0255 0.0520
## type_samp 0.0029 0.0098 0.2957 121 0.7680 -0.0165 0.0223
## quality 0.0033 0.0211 0.1543 121 0.8777 -0.0385 0.0450
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Comparison by ANNOVA of the two adjusted models
rm(list=ls(all=TRUE)) #Deletes all memory content
library(readxl)
data_neumo1 <- read_excel("C:/Users/USER/Desktop/RSMA/data_neumo1.xlsx")
View(data_neumo1)
# Call of the packages
#install.packages(c("metafor", "meta"))
#install.packages("PerformanceAnalytics")
library(metafor)
library(meta)
library(tidyverse)
library(metadat)
data_neumo1[,c("prev","year_pub","age","sample","stage","type_prev","comorb","type_morb", "meth_iden",
"type_samp","quality")] %>% cor()## prev year_pub age sample stage
## prev 1.000000000 0.13868200 -0.17967540 0.07411439 0.02176720
## year_pub 0.138682005 1.00000000 0.22627526 0.03067928 0.39752442
## age -0.179675397 0.22627526 1.00000000 -0.13093882 0.11567383
## sample 0.074114394 0.03067928 -0.13093882 1.00000000 0.04390912
## stage 0.021767199 0.39752442 0.11567383 0.04390912 1.00000000
## type_prev 0.236530610 -0.18428735 -0.17725722 0.12161419 -0.04104637
## comorb -0.086607462 -0.10618728 0.07478766 0.15715092 0.04058524
## type_morb 0.081356109 0.16986434 -0.01084178 -0.15431325 0.02179090
## meth_iden -0.018333113 0.03447639 0.24005604 -0.01641210 0.09677704
## type_samp -0.009052112 0.19581058 0.10742613 -0.07424087 0.02474667
## quality -0.029441628 0.08528716 0.18837118 -0.11182899 0.12662086
## type_prev comorb type_morb meth_iden type_samp
## prev 0.23653061 -0.08660746 0.08135611 -0.01833311 -0.009052112
## year_pub -0.18428735 -0.10618728 0.16986434 0.03447639 0.195810580
## age -0.17725722 0.07478766 -0.01084178 0.24005604 0.107426133
## sample 0.12161419 0.15715092 -0.15431325 -0.01641210 -0.074240869
## stage -0.04104637 0.04058524 0.02179090 0.09677704 0.024746673
## type_prev 1.00000000 0.13652971 -0.12022355 -0.02858849 -0.086073483
## comorb 0.13652971 1.00000000 -0.94544037 0.10623960 0.212889711
## type_morb -0.12022355 -0.94544037 1.00000000 -0.08748310 -0.212806023
## meth_iden -0.02858849 0.10623960 -0.08748310 1.00000000 0.401702746
## type_samp -0.08607348 0.21288971 -0.21280602 0.40170275 1.000000000
## quality -0.19181043 -0.12561763 0.10478573 -0.01195179 0.021202178
## quality
## prev -0.02944163
## year_pub 0.08528716
## age 0.18837118
## sample -0.11182899
## stage 0.12662086
## type_prev -0.19181043
## comorb -0.12561763
## type_morb 0.10478573
## meth_iden -0.01195179
## type_samp 0.02120218
## quality 1.00000000# Correlation graph (authors: Peterson y Carl 2020)
library(PerformanceAnalytics)
data_neumo1[,c("prev","year_pub","age","sample","stage","type_prev","comorb","type_morb", "meth_iden",
"type_samp","quality")] %>% chart.Correlation()
warnings()
data_neumo1[,c("prev","age","comorb","type_morb", "meth_iden","type_samp")] %>% chart.Correlation()
warnings()
m.qual <- rma(yi = prev,
sei = age,
data = data_neumo1,
method = "ML",
mods = ~ comorb,
test = "knha")
m.qual##
## Mixed-Effects Model (k = 127; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1675)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 125) = 0.9331, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 125) = 0.0867, p-val = 0.7689
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1386 0.0456 3.0384 125 0.0029 0.0483 0.2290 **
## comorb -0.0073 0.0247 -0.2945 125 0.7689 -0.0562 0.0416
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.qual.rep <- rma(yi = prev,
sei = age,
data = data_neumo1,
method = "ML",
mods = ~ year_pub + stage + comorb + type_prev + meth_iden + type_samp + quality,
test = "knha")
m.qual.rep##
## Mixed-Effects Model (k = 127; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1675)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 119) = 0.7929, p-val = 1.0000
##
## Test of Moderators (coefficients 2:8):
## F(df1 = 7, df2 = 119) = 3.0190, p-val = 0.0059
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -12.4049 3.7083 -3.3452 119 0.0011 -19.7477 -5.0621 **
## year_pub 0.0061 0.0018 3.3313 119 0.0012 0.0025 0.0098 **
## stage -0.0345 0.0195 -1.7693 119 0.0794 -0.0731 0.0041 .
## comorb -0.0026 0.0246 -0.1064 119 0.9155 -0.0514 0.0461
## type_prev 0.0407 0.0171 2.3795 119 0.0189 0.0068 0.0746 *
## meth_iden 0.0380 0.0220 1.7259 119 0.0870 -0.0056 0.0815 .
## type_samp 0.0027 0.0092 0.2904 119 0.7720 -0.0155 0.0208
## quality 0.0449 0.0220 2.0462 119 0.0429 0.0015 0.0884 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Comparison by ANNOVA of the two adjusted models
anova(m.qual, m.qual.rep)##
## df AIC BIC AICc logLik LRT pval QE tau^2
## Full 9 383.2216 408.8193 384.7601 -182.6108 0.7929 0.0000
## Reduced 3 371.3618 379.8944 371.5569 -182.6809 0.1402 0.9999 0.9331 0.0000
## R^2
## Full
## Reduced 0.0000%##### Analysis of model interactions
# Add factor labels to 'comorbility'# 1 = SI # 2 = NO
levels(data_neumo1$comorb) = c("SI", "NO")
# Fit the meta-regression model
m.qual.rep.int <- rma(yi = prev,
sei = age,
data = data_neumo1,
method = "REML",
mods = ~ age * comorb,
test = "knha")
m.qual.rep.int##
## Mixed-Effects Model (k = 127; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1700)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 123) = 0.9282, p-val = 1.0000
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 123) = 0.2450, p-val = 0.8647
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0992 0.1147 0.8655 123 0.3885 -0.1277 0.3262
## age 0.0345 0.0931 0.3707 123 0.7115 -0.1497 0.2187
## comorb 0.0211 0.0612 0.3441 123 0.7313 -0.1001 0.1423
## age:comorb -0.0247 0.0494 -0.4998 123 0.6181 -0.1225 0.0731
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1### Test de permutación
permutest(m.qual)## Running 1000 iterations for an approximate permutation test.##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 125) = 0.0867, p-val = 0.8080
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt 0.1386 0.0456 3.0384 125 0.3490 0.0483 0.2290
## comorb -0.0073 0.0247 -0.2945 125 0.8080 -0.0562 0.0416
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) p-values based on permutation testingpermutest(m.qual.rep)## Running 1000 iterations for an approximate permutation test.##
## Test of Moderators (coefficients 2:8):¹
## F(df1 = 7, df2 = 119) = 3.0190, p-val = 0.0730
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt -12.4049 3.7083 -3.3452 119 0.0080 -19.7477 -5.0621 **
## year_pub 0.0061 0.0018 3.3313 119 0.0080 0.0025 0.0098 **
## stage -0.0345 0.0195 -1.7693 119 0.1550 -0.0731 0.0041
## comorb -0.0026 0.0246 -0.1064 119 0.9320 -0.0514 0.0461
## type_prev 0.0407 0.0171 2.3795 119 0.0550 0.0068 0.0746 .
## meth_iden 0.0380 0.0220 1.7259 119 0.1610 -0.0056 0.0815
## type_samp 0.0027 0.0092 0.2904 119 0.8220 -0.0155 0.0208
## quality 0.0449 0.0220 2.0462 119 0.1170 0.0015 0.0884
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) p-values based on permutation testing# Add factor labels to 'method_identification'# 1 = Culture, # 2 = PCR, # 3 = Culture & PCR, # 4 = Others
levels(data_neumo1$meth_iden) = c("1", "2", "3","4")
# Fit the meta-regression model
m.qual.rep.int <- rma(yi = prev,
sei = age,
data = data_neumo1,
method = "REML",
mods = ~ age * meth_iden,
test = "knha")
m.qual.rep.int##
## Mixed-Effects Model (k = 127; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1699)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 123) = 0.9218, p-val = 1.0000
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 123) = 0.5295, p-val = 0.6629
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0898 0.0572 1.5693 123 0.1191 -0.0235 0.2031
## age 0.0083 0.0400 0.2063 123 0.8369 -0.0710 0.0875
## meth_iden 0.0417 0.0423 0.9845 123 0.3268 -0.0421 0.1255
## age:meth_iden -0.0160 0.0258 -0.6185 123 0.5374 -0.0671 0.0351
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1### Test de permutación
permutest(m.qual)## Running 1000 iterations for an approximate permutation test.##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 125) = 0.0867, p-val = 0.8390
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt 0.1386 0.0456 3.0384 125 0.3530 0.0483 0.2290
## comorb -0.0073 0.0247 -0.2945 125 0.8390 -0.0562 0.0416
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) p-values based on permutation testingpermutest(m.qual.rep)## Running 1000 iterations for an approximate permutation test.##
## Test of Moderators (coefficients 2:8):¹
## F(df1 = 7, df2 = 119) = 3.0190, p-val = 0.0750
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt -12.4049 3.7083 -3.3452 119 0.0100 -19.7477 -5.0621 **
## year_pub 0.0061 0.0018 3.3313 119 0.0110 0.0025 0.0098 *
## stage -0.0345 0.0195 -1.7693 119 0.1600 -0.0731 0.0041
## comorb -0.0026 0.0246 -0.1064 119 0.9410 -0.0514 0.0461
## type_prev 0.0407 0.0171 2.3795 119 0.0570 0.0068 0.0746 .
## meth_iden 0.0380 0.0220 1.7259 119 0.1660 -0.0056 0.0815
## type_samp 0.0027 0.0092 0.2904 119 0.8020 -0.0155 0.0208
## quality 0.0449 0.0220 2.0462 119 0.1230 0.0015 0.0884
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) p-values based on permutation testing# Add factor labels to 'type_samp'# 1 = FNF, # 2 = FOF, # 3 = FNF+FOF, # 4 = FNF + Others, # 5 =FNF + Saliva, # 6 = Saliva, # 7 = FNF + Esputo
levels(data_neumo1$type_samp) = c("1", "2", "3","4","5","6","7")
# Fit the meta-regression model
m.qual.rep.int <- rma(yi = prev,
sei = age,
data = data_neumo1,
method = "REML",
mods = ~ age * type_samp,
test = "knha")
m.qual.rep.int##
## Mixed-Effects Model (k = 127; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1699)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 123) = 0.9097, p-val = 1.0000
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 123) = 1.0817, p-val = 0.3595
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0897 0.0407 2.2012 123 0.0296 0.0090 0.1703 *
## age 0.0122 0.0309 0.3929 123 0.6951 -0.0491 0.0734
## type_samp 0.0281 0.0188 1.4937 123 0.1378 -0.0091 0.0653
## age:type_samp -0.0129 0.0134 -0.9673 123 0.3353 -0.0394 0.0135
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1### Test de permutación
permutest(m.qual)## Running 1000 iterations for an approximate permutation test.##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 125) = 0.0867, p-val = 0.8230
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt 0.1386 0.0456 3.0384 125 0.3540 0.0483 0.2290
## comorb -0.0073 0.0247 -0.2945 125 0.8230 -0.0562 0.0416
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) p-values based on permutation testingpermutest(m.qual.rep)## Running 1000 iterations for an approximate permutation test.##
## Test of Moderators (coefficients 2:8):¹
## F(df1 = 7, df2 = 119) = 3.0190, p-val = 0.0820
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt -12.4049 3.7083 -3.3452 119 0.0080 -19.7477 -5.0621 **
## year_pub 0.0061 0.0018 3.3313 119 0.0080 0.0025 0.0098 **
## stage -0.0345 0.0195 -1.7693 119 0.1370 -0.0731 0.0041
## comorb -0.0026 0.0246 -0.1064 119 0.9370 -0.0514 0.0461
## type_prev 0.0407 0.0171 2.3795 119 0.0480 0.0068 0.0746 *
## meth_iden 0.0380 0.0220 1.7259 119 0.1810 -0.0056 0.0815
## type_samp 0.0027 0.0092 0.2904 119 0.8150 -0.0155 0.0208
## quality 0.0449 0.0220 2.0462 119 0.1040 0.0015 0.0884
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) p-values based on permutation testing# Fit the meta-regression model to 'age' # 1 = All, # 2 = young, # 3 = middle, # 4 = older
levels(data_neumo1$age) = c("1", "2", "3","4")
# Fit the meta-regression model
m.qual.rep.int <- rma(yi = prev,
sei = meth_iden,
data = data_neumo1,
method = "REML",
mods = ~ meth_iden * type_samp,
test = "knha")
m.qual.rep.int##
## Mixed-Effects Model (k = 127; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1395)
## 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): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 123) = 1.2053, p-val = 1.0000
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 123) = 0.0892, p-val = 0.9658
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1096 0.0778 1.4085 123 0.1615 -0.0444 0.2637
## meth_iden 0.0092 0.0730 0.1255 123 0.9003 -0.1354 0.1537
## type_samp -0.0059 0.0268 -0.2187 123 0.8273 -0.0589 0.0472
## meth_iden:type_samp 0.0018 0.0225 0.0812 123 0.9354 -0.0428 0.0464
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1### Permutation test
permutest(m.qual)## Running 1000 iterations for an approximate permutation test.##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 125) = 0.0867, p-val = 0.8100
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt 0.1386 0.0456 3.0384 125 0.3380 0.0483 0.2290
## comorb -0.0073 0.0247 -0.2945 125 0.8100 -0.0562 0.0416
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) p-values based on permutation testingpermutest(m.qual.rep)## Running 1000 iterations for an approximate permutation test.##
## Test of Moderators (coefficients 2:8):¹
## F(df1 = 7, df2 = 119) = 3.0190, p-val = 0.0700
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt -12.4049 3.7083 -3.3452 119 0.0130 -19.7477 -5.0621 *
## year_pub 0.0061 0.0018 3.3313 119 0.0120 0.0025 0.0098 *
## stage -0.0345 0.0195 -1.7693 119 0.1660 -0.0731 0.0041
## comorb -0.0026 0.0246 -0.1064 119 0.9270 -0.0514 0.0461
## type_prev 0.0407 0.0171 2.3795 119 0.0640 0.0068 0.0746 .
## meth_iden 0.0380 0.0220 1.7259 119 0.1520 -0.0056 0.0815
## type_samp 0.0027 0.0092 0.2904 119 0.8000 -0.0155 0.0208
## quality 0.0449 0.0220 2.0462 119 0.1000 0.0015 0.0884 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) p-values based on permutation testing## Multiple model inference
if (!require("devtools")) {
install.packages("devtools")
}
devtools::install_github("MathiasHarrer/dmetar")
#install.packages("metafor")
#install.packages("ggplot2")
#install.packages("MuMIn")
library(metafor)
library(ggplot2)
library(MuMIn)
library(dmetar)
multimodel.inference(TE = "prev",
seTE = "age",
data = data_neumo1,
predictors = c("year_pub", "stage", "comorb","type_prev", "meth_iden",
"type_samp","quality"),
interaction = FALSE)##
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##
## Multimodel Inference: Final Results
## --------------------------
##
## - Number of fitted models: 128
## - Full formula: ~ year_pub + stage + comorb + type_prev + meth_iden + type_samp + quality
## - Coefficient significance test: knha
## - Interactions modeled: no
## - Evaluation criterion: AICc
##
##
## Best 5 Models
## --------------------------
##
##
## Global model call: metafor::rma(yi = TE, sei = seTE, mods = form, data = glm.data,
## method = method, test = test)
## ---
## Model selection table
## (Int) cmr mth_idn qlt typ_prv typ_smp yer_pub df logLik AICc
## 56 + -0.010880 0.01446 0.03836 0.03590 0.009527 7 -175.325 365.6
## 55 + 0.01502 0.04026 0.03497 0.008681 6 -176.461 365.6
## 40 + -0.004548 0.01697 0.03608 0.007582 6 -176.464 365.6
## 119 + 0.03178 0.04155 0.03952 0.002746 0.004910 7 -175.369 365.7
## 104 + -0.002050 0.03282 0.03732 0.001740 0.004537 7 -175.378 365.7
## delta weight
## 56 0.00 0.205
## 55 0.03 0.202
## 40 0.04 0.202
## 119 0.09 0.196
## 104 0.11 0.195
## Models ranked by AICc(x)
##
##
## Multimodel Inference Coefficients
## --------------------------
##
##
## Estimate Std. Error z value Pr(>|z|)
## intrcpt -4.940273378 5.704076350 0.8660952 0.3864379
## comorb -0.003835789 0.018520967 0.2071052 0.8359277
## meth_iden 0.014046477 0.021543958 0.6519915 0.5144067
## quality 0.021115887 0.025468889 0.8290855 0.4070560
## type_prev 0.018496291 0.022107752 0.8366428 0.4027934
## type_samp 0.004414151 0.008140967 0.5422146 0.5876707
## year_pub 0.002468829 0.002834876 0.8708773 0.3838211
## stage -0.009085084 0.018109939 0.5016629 0.6159047
##
##
## Predictor Importance
## --------------------------
##
##
## model importance
## 1 meth_iden 0.5801311
## 2 quality 0.5359219
## 3 type_samp 0.5344075
## 4 type_prev 0.5094191
## 5 comorb 0.5065850
## 6 year_pub 0.4909211
## 7 stage 0.4867771
VI. Analysis of publication bias
rm(list=ls(all=TRUE)) #Deletes all memory content
library(readxl)
data_neumo1 <- read_excel("C:/Users/USER/Desktop/RSMA/data_neumo1.xlsx")
View(data_neumo1)
# Call of the packages
#install.packages("metafor")
#install.packages("meta")
library(metafor)
library(meta)
library(ggplot2)
library(MuMIn)
library(dmetar)
# Effect size with transformation logit
ies.logit=escalc(xi=posit, ni=sample, data=data_neumo1, measure="PLO")
pes.logit=rma(yi, vi, data=ies.logit)
pes=predict(pes.logit, transf=transf.ilogit)
print(pes) ##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.0892 0.0756 0.1049 0.0138 0.4065# Publication bias analysis
# funnel.meta(pes.logit) # revisar código
funnel(pes.logit, yaxis="sei")
# rank (pes.logit) #revisar el codigo
regtest(pes.logit, model="rma", predictor="sei") ## Bias estimator - Egger test.##
## Regression Test for Funnel Plot Asymmetry
##
## Model: mixed-effects meta-regression model
## Predictor: standard error
##
## Test for Funnel Plot Asymmetry: z = -8.6257, p < .0001
## Limit Estimate (as sei -> 0): b = -1.5369 (CI: -1.7639, -1.3100)R session information
In the reproducible research framework, an important step is to save all the versions of the packages used to perform analysis. They must be provided when submitting a paper.
sessionInfo()R version 4.2.2 (2022-10-31 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 19044)
Matrix products: default
locale: [1] LC_COLLATE=English_United Kingdom.utf8 [2]
LC_CTYPE=English_United Kingdom.utf8
[3] LC_MONETARY=English_United Kingdom.utf8 [4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.utf8
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages: [1] dmetar_0.0.9000 MuMIn_1.47.1
[3] devtools_2.4.5 usethis_2.1.6
[5] PerformanceAnalytics_2.0.4 xts_0.12.2
[7] zoo_1.8-11 forcats_0.5.2
[9] stringr_1.4.1 dplyr_1.0.10
[11] purrr_0.3.5 readr_2.1.3
[13] tidyr_1.2.1 tibble_3.1.8
[15] ggplot2_3.4.0 tidyverse_1.3.2
[17] meta_6.0-0 metafor_3.8-1
[19] metadat_1.2-0 Matrix_1.5-1
[21] readxl_1.4.1
loaded via a namespace (and not attached): [1] googledrive_2.0.0
minqa_1.2.5 colorspace_2.0-3
[4] class_7.3-20 modeltools_0.2-23 ellipsis_0.3.2
[7] mclust_6.0.0 fs_1.5.2 rstudioapi_0.14
[10] farver_2.1.1 remotes_2.4.2 flexmix_2.3-18
[13] ggrepel_0.9.2 fansi_1.0.3 lubridate_1.9.0
[16] mathjaxr_1.6-0 xml2_1.3.3 splines_4.2.2
[19] robustbase_0.95-0 cachem_1.0.6 knitr_1.41
[22] pkgload_1.3.2 jsonlite_1.8.3 nloptr_2.0.3
[25] broom_1.0.1 kernlab_0.9-31 cluster_2.1.4
[28] dbplyr_2.2.1 shiny_1.7.3 compiler_4.2.2
[31] httr_1.4.4 backports_1.4.1 assertthat_0.2.1
[34] fastmap_1.1.0 gargle_1.2.1 cli_3.4.1
[37] later_1.3.0 htmltools_0.5.3 prettyunits_1.1.1
[40] tools_4.2.2 gtable_0.3.1 glue_1.6.2
[43] Rcpp_1.0.9 cellranger_1.1.0 jquerylib_0.1.4
[46] vctrs_0.5.1 nlme_3.1-160 fpc_2.2-9
[49] xfun_0.35 ps_1.7.2 lme4_1.1-31
[52] rvest_1.0.3 timechange_0.1.1 mime_0.12
[55] miniUI_0.1.1.1 CompQuadForm_1.4.3 lifecycle_1.0.3
[58] googlesheets4_1.0.1 DEoptimR_1.0-11 MASS_7.3-58.1
[61] scales_1.2.1 hms_1.1.2 promises_1.2.0.1
[64] parallel_4.2.2 curl_4.3.3 yaml_2.3.6
[67] gridExtra_2.3 memoise_2.0.1 pbapply_1.6-0
[70] sass_0.4.4 stringi_1.7.8 highr_0.9
[73] poibin_1.5 boot_1.3-28 pkgbuild_1.4.0
[76] prabclus_2.3-2 rlang_1.0.6 pkgconfig_2.0.3
[79] evaluate_0.18 lattice_0.20-45 labeling_0.4.2
[82] htmlwidgets_1.5.4 tidyselect_1.2.0 processx_3.8.0
[85] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[88] profvis_0.3.7 DBI_1.1.3 pillar_1.8.1
[91] haven_2.5.1 withr_2.5.0 nnet_7.3-18
[94] abind_1.4-5 modelr_0.1.10 crayon_1.5.2
[97] utf8_1.2.2 tzdb_0.3.0 rmarkdown_2.18
[100] urlchecker_1.0.1 grid_4.2.2 netmeta_2.6-0
[103] callr_3.7.3 diptest_0.76-0 reprex_2.0.2
[106] digest_0.6.30 xtable_1.8-4 httpuv_1.6.6
[109] stats4_4.2.2 munsell_0.5.0 bslib_0.4.1
[112] magic_1.6-1 sessioninfo_1.2.2 quadprog_1.5-8