RSMA - Pneumo V3
Lozada, Julian et al (2022)
2022-12-30
# option for html output
knitr::opts_chunk$set(echo = TRUE)
## A meta-analysis combines the results of several scientic 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 author2 year_…¹ contint age prev sample posit
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 RSMA01 RS001 Abdullahi_2… Abdull… 2008 1 4 0.0467 107 5
## 2 RSMA02 RS001 Abdullahi_2… Abdull… 2008 1 2 0.0564 195 11
## 3 RSMA03 RS001 Abdullahi_2… Abdull… 2008 1 1 0.0640 406 26
## 4 RSMA04 RS002 Adetifa_2012 Adetifa 2012 1 4 0.0685 482 33
## 5 RSMA05 RS002 Adetifa_2012 Adetifa 2012 1 2 0.108 530 57
## 6 RSMA06 RS002 Adetifa_2012 Adetifa 2012 1 1 0.260 361 94
## 7 RSMA07 RS004 Adler_2019 Adler 2019 5 1 0.0654 795 52
## 8 RSMA08 RS010 Almeida_2014 Almeida 2014 5 4 0.0229 3361 77
## 9 RSMA09 RS015 Ansaldi_2013 Ansaldi 2013 5 4 0.02 283 56
## 10 RSMA10 RS017 Becker-Drep… Becker… 2015 2 4 0.0190 210 4
## # … with 104 more rows, 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>, and
## # abbreviated variable name ¹year_pubglimpse(data_neumo1)## Rows: 114
## Columns: 20
## $ Sequence <chr> "RSMA01", "RSMA02", "RSMA03", "RSMA04", "RSMA05", "RSMA06…
## $ code <chr> "RS001", "RS001", "RS001", "RS002", "RS002", "RS002", "RS…
## $ author <chr> "Abdullahi_2008", "Abdullahi_2008", "Abdullahi_2008", "Ad…
## $ author2 <chr> "Abdullahi", "Abdullahi", "Abdullahi", "Adetifa", "Adetif…
## $ year_pub <dbl> 2008, 2008, 2008, 2012, 2012, 2012, 2019, 2014, 2013, 201…
## $ 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: 114
## Columns: 20
## $ Sequence <chr> "RSMA01", "RSMA02", "RSMA03", "RSMA04", "RSMA05", "RSMA06…
## $ code <chr> "RS001", "RS001", "RS001", "RS002", "RS002", "RS002", "RS…
## $ author <chr> "Abdullahi_2008", "Abdullahi_2008", "Abdullahi_2008", "Ad…
## $ year_pub <chr> "Abdullahi", "Abdullahi", "Abdullahi", "Adetifa", "Adetif…
## $ contint <dbl> 2008, 2008, 2008, 2012, 2012, 2012, 2019, 2014, 2013, 201…
## $ 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 [114 × 20] (S3: tbl_df/tbl/data.frame)
## $ Sequence : chr [1:114] "RSMA01" "RSMA02" "RSMA03" "RSMA04" ...
## $ code : chr [1:114] "RS001" "RS001" "RS001" "RS002" ...
## $ author : chr [1:114] "Abdullahi_2008" "Abdullahi_2008" "Abdullahi_2008" "Adetifa_2012" ...
## $ year_pub : chr [1:114] "Abdullahi" "Abdullahi" "Abdullahi" "Adetifa" ...
## $ contint : num [1:114] 2008 2008 2008 2012 2012 ...
## $ age : num [1:114] 1 1 1 1 1 1 5 5 5 2 ...
## $ prev : num [1:114] 4 2 1 4 2 1 1 4 4 4 ...
## $ sample : num [1:114] 0.0467 0.0564 0.064 0.0685 0.1075 ...
## $ posit : num [1:114] 107 195 406 482 530 ...
## $ type_prev : num [1:114] 5 11 26 33 57 94 52 77 56 4 ...
## $ stage : num [1:114] 2 2 2 1 1 1 1 1 2 1 ...
## $ comorb : num [1:114] 1 1 1 2 2 2 2 2 2 2 ...
## $ type_morb : num [1:114] 2 2 2 2 2 2 2 2 2 2 ...
## $ meth_iden : num [1:114] 1 1 1 1 1 1 1 1 1 1 ...
## $ type_samp : num [1:114] 1 1 1 1 1 1 1 3 2 1 ...
## $ setting : num [1:114] 1 1 1 1 1 1 2 3 1 1 ...
## $ type_setting: num [1:114] 2 2 2 2 2 2 1 2 1 2 ...
## $ type_study : num [1:114] 1 1 1 1 1 1 1 1 1 2 ...
## $ quality : num [1:114] 4 4 4 4 4 4 4 4 4 4 ...
## $ NA : num [1:114] 3 3 3 4 4 4 4 4 4 4 ...summary(data_neumo1)## Sequence code author year_pub
## Length:114 Length:114 Length:114 Length:114
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## contint age prev sample
## Min. :1997 Min. :1.000 Min. :1.000 Min. :0.00000
## 1st Qu.:2010 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.04428
## Median :2014 Median :2.000 Median :1.500 Median :0.08091
## Mean :2014 Mean :3.053 Mean :2.149 Mean :0.10634
## 3rd Qu.:2018 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:0.11851
## Max. :2021 Max. :6.000 Max. :4.000 Max. :0.47544
## posit type_prev stage comorb
## Min. : 8.0 Min. : 0.00 Min. :1.000 Min. :1.000
## 1st Qu.: 200.0 1st Qu.: 11.00 1st Qu.:1.000 1st Qu.:2.000
## Median : 383.0 Median : 29.00 Median :1.000 Median :2.000
## Mean : 704.5 Mean : 85.39 Mean :1.421 Mean :1.798
## 3rd Qu.: 642.8 3rd Qu.: 69.75 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :8336.0 Max. :1868.00 Max. :3.000 Max. :3.000
## type_morb meth_iden type_samp setting
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :1.000 Median :1.000
## Mean :1.807 Mean :1.684 Mean :1.281 Mean :1.737
## 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:3.000
## Max. :2.000 Max. :7.000 Max. :3.000 Max. :5.000
## type_setting type_study quality NA
## Min. :1.000 Min. : 1.000 Min. :1.000 Min. :2.000
## 1st Qu.:1.000 1st Qu.: 1.000 1st Qu.:2.000 1st Qu.:3.000
## Median :2.000 Median : 1.000 Median :4.000 Median :3.000
## Mean :1.711 Mean : 2.877 Mean :3.342 Mean :3.342
## 3rd Qu.:2.000 3rd Qu.: 4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :4.000 Max. :10.000 Max. :4.000 Max. :4.000convert <- which(data_neumo1$prev =="age")
data_neumo1$prev[convert] <- "age"
summary(data_neumo1$prev)## Length Class Mode
## 114 character characterpar(mfrow = c(2,4))
barplot(Age <- table(data_neumo1$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] 17 20print(Prev_adults)## # A tibble: 17 × 20
## Sequence code author year_…¹ contint age prev sample posit type_…² stage
## <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 RSMA35 RS032 Feola_… Feola 2016 2 1 0.0376 399 15 1
## 2 RSMA38 RS036 Gounde… Gounder 2014 2 1 0.142 8336 1183 2
## 3 RSMA41 RS038 Grant_… Grant 2016 2 1 0.118 2847 336 2
## 4 RSMA44 RS040 Hammit… Hammitt 2006 2 1 0.01 115 15 2
## 5 RSMA45 RS040 Hammit… Hammitt 2006 2 1 0.01 115 15 2
## 6 RSMA61 RS062 Millar… Millar 2008 2 1 0.139 1729 241 3
## 7 RSMA67 RS068 Onwubi… Onwubi… 2008 2 1 0.0343 175 6 1
## 8 RSMA80 RS078 Rodrig… Rodrig… 1997 2 1 0.168 321 54 2
## 9 RSMA81 RS078 Rodrig… Rodrig… 1997 2 1 0.0533 150 8 2
## 10 RSMA82 RS079 Rosen … Rosen 2007 2 1 0.0462 65 3 1
## 11 RSMA83 RS079 Rosen_… Rosen 2007 2 1 0.0476 63 3 1
## 12 RSMA84 RS081 Saravo… Saravo… 2007 2 1 0.015 200 3 1
## 13 RSMA85 RS081 Saravo… Saravo… 2007 2 1 0.03 200 6 1
## 14 RSMA86 RS081 Saravo… Saravo… 2007 2 1 0.11 200 22 1
## 15 RSMA87 RS083 Scott_… Scott 2012 2 1 0.109 2681 291 2
## 16 RSMA95 RS086 Sutcli… Sutcli… 2019 2 1 0.145 509 74 2
## 17 RSMA96 RS086 Sutcli… Sutcli… 2019 2 1 0.475 509 242 2
## # … 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 = 114; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0089 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0943
## I^2 (total heterogeneity / total variability): 99.54%
## H^2 (total variability / sampling variability): 216.40
##
## Test for Heterogeneity:
## Q(df = 113) = 10041.1588, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1078 0.0090 11.9182 <.0001 0.0900 0.1255 ***
##
## ---
## 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 = 114; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.8940 (SE = 0.1300)
## tau (square root of estimated tau^2 value): 0.9455
## I^2 (total heterogeneity / total variability): 98.29%
## H^2 (total variability / sampling variability): 58.32
##
## Test for Heterogeneity:
## Q(df = 113) = 6139.8997, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -2.3800 0.0933 -25.5193 <.0001 -2.5627 -2.1972 ***
##
## ---
## 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
## 0.0847 0.0716 0.1000 0.0142 0.3734# 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
## 0.5765 0.5696 0.5834 0.5030 0.6468# 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
## 0.0851 0.0723 0.0999 0.0152 0.3592# 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
## 0.085092 0.072301 0.099903 0.015196 0.359216# Heterogeneity calculation (variation between studies)
print(pes.logit, digits=4)##
## Random-Effects Model (k = 114; tau^2 estimator: DL)
##
## tau^2 (estimated amount of total heterogeneity): 0.8318 (SE = 0.2624)
## tau (square root of estimated tau^2 value): 0.9121
## I^2 (total heterogeneity / total variability): 98.16%
## H^2 (total variability / sampling variability): 54.34
##
## Test for Heterogeneity:
## Q(df = 113) = 6139.8997, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -2.3751 0.0902 -26.3312 <.0001 -2.5519 -2.1983 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1confint(pes.logit, digits=2)##
## estimate ci.lb ci.ub
## tau^2 0.83 0.77 1.49
## tau 0.91 0.88 1.22
## I^2(%) 98.16 98.03 98.97
## H^2 54.34 50.64 96.68III. 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
## 0.0847 0.0716 0.1000 0.0142 0.3734# 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 3.3792536 7.4311355 4.8068764 3.1080322 6.5186266
## [103] 5.1164296 2.2186934 5.6690816 0.7067566 4.5869034 7.7907062
## [109] 8.0617879 3.3752637 4.5496071 3.4248100 3.0410722 4.2209113sortvar=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
## 31 -4.8329 1.6968 -2.8483
## 106 -4.5519 1.6978 -2.6810
## 96 2.3001 0.9282 2.4780
## 76 -3.3868 1.3705 -2.4712
## 77 -4.0808 1.6995 -2.4011
## 21 2.1225 0.9329 2.2752
## 17 -2.3364 1.0327 -2.2623
## 97 2.0055 0.9329 2.1496
## 13 2.0324 0.9549 2.1285
## 75 1.9324 0.9339 2.0691
## 50 -2.1017 1.0197 -2.0610
## 56 1.9134 0.9773 1.9579
## 14 -3.2473 1.7029 -1.9069
## 99 1.7076 0.9488 1.7998
## 104 -1.7883 1.0424 -1.7154
## 62 -1.6139 0.9481 -1.7023
## 55 1.5795 0.9534 1.6568
## 84 -1.8186 1.1072 -1.6425
## 18 -1.6450 1.0060 -1.6352
## 43 -1.5249 0.9858 -1.5468
## 114 1.4844 0.9735 1.5248
## 15 -1.4329 0.9742 -1.4710
## 10 -1.5744 1.0705 -1.4708
## 8 -1.3874 0.9496 -1.4611
## 22 1.3874 0.9628 1.4409
## 53 1.3475 0.9507 1.4174
## 6 1.3483 0.9532 1.4144
## 52 1.3306 0.9524 1.3971
## 16 -1.2680 0.9811 -1.2924
## 32 1.1017 0.9624 1.1448
## 85 -1.1054 1.0353 -1.0677
## 78 -1.0217 0.9845 -1.0378
## 9 0.9898 0.9623 1.0286
## 54 0.9891 0.9629 1.0271
## 20 0.9703 0.9580 1.0128
## 101 -0.9750 1.0027 -0.9724
## 23 1.0015 1.0748 0.9318
## 67 -0.9661 1.0369 -0.9317
## 11 0.8722 0.9577 0.9107
## 72 -0.8681 0.9615 -0.9029
## 73 -0.8732 0.9804 -0.8906
## 35 -0.8704 0.9864 -0.8825
## 94 0.7945 0.9651 0.8232
## 80 0.7895 0.9642 0.8188
## 64 0.7751 0.9625 0.8053
## 49 -0.7191 0.9708 -0.7408
## 93 0.7095 0.9654 0.7349
## 107 -0.7133 0.9767 -0.7303
## 102 0.6532 0.9660 0.6762
## 95 0.6151 0.9622 0.6392
## 68 -0.6332 1.0192 -0.6213
## 38 0.5867 0.9547 0.6146
## 1 -0.6403 1.0570 -0.6057
## 98 -0.6039 0.9979 -0.6052
## 61 0.5656 0.9568 0.5911
## 82 -0.6528 1.1210 -0.5824
## 12 0.5703 0.9967 0.5722
## 110 -0.5557 0.9983 -0.5567
## 83 -0.6198 1.1214 -0.5527
## 79 -0.5276 0.9746 -0.5413
## 90 0.5526 1.0272 0.5379
## 70 -0.5166 0.9714 -0.5318
## 69 -0.5081 1.0132 -0.5015
## 44 0.4878 0.9939 0.4908
## 45 0.4878 0.9939 0.4908
## 81 -0.5002 1.0204 -0.4902
## 105 -0.4726 0.9701 -0.4872
## 66 -0.4500 0.9703 -0.4638
## 2 -0.4404 1.0032 -0.4390
## 109 -0.4094 0.9624 -0.4254
## 36 -0.3958 0.9690 -0.4084
## 111 -0.3899 0.9794 -0.3981
## 58 0.3793 0.9601 0.3951
## 41 0.3730 0.9571 0.3897
## 37 0.3309 0.9622 0.3439
## 88 0.3037 0.9579 0.3171
## 3 -0.3043 0.9761 -0.3117
## 34 0.2996 0.9762 0.3069
## 60 0.2884 0.9612 0.3000
## 86 0.2926 0.9817 0.2981
## 7 -0.2815 0.9657 -0.2915
## 87 0.2777 0.9576 0.2900
## 51 0.2710 0.9585 0.2828
## 24 -0.3941 1.4034 -0.2808
## 5 0.2672 0.9658 0.2767
## 108 0.2659 0.9642 0.2758
## 29 -0.4544 1.7387 -0.2613
## 28 -0.2749 1.0605 -0.2592
## 4 -0.2320 0.9720 -0.2387
## 91 0.2188 0.9625 0.2274
## 59 -0.1843 0.9597 -0.1920
## 26 0.1852 0.9841 0.1882
## 19 0.1744 0.9697 0.1798
## 39 -0.1688 0.9611 -0.1756
## 33 0.1663 0.9697 0.1715
## 25 0.1649 1.0067 0.1638
## 112 0.1596 0.9990 0.1597
## 103 0.1548 0.9754 0.1587
## 92 0.1451 0.9628 0.1507
## 65 -0.1325 0.9597 -0.1380
## 30 -0.1353 1.0052 -0.1346
## 113 -0.1301 1.0101 -0.1288
## 100 -0.1219 0.9778 -0.1246
## 27 0.1304 1.0469 0.1245
## 57 0.1117 0.9575 0.1167
## 74 0.0760 0.9614 0.0790
## 71 -0.0651 0.9744 -0.0668
## 42 0.0676 1.0171 0.0665
## 89 0.0583 0.9725 0.0600
## 40 -0.0375 0.9765 -0.0384
## 46 -0.0296 0.9642 -0.0307
## 63 0.0194 0.9759 0.0199
## 47 0.0187 0.9639 0.0194
## 48 0.0022 0.9852 0.0022# Influence of outliers
L1O=leave1out(pes.logit, transf=transf.ilogit); print(L1O)##
## estimate zval pval ci.lb ci.ub Q Qp tau2 I2
## 1 0.0851 -25.3063 0.0000 0.0718 0.1005 6131.4779 0.0000 0.8987 98.3078
## 2 0.0850 -25.2763 0.0000 0.0717 0.1004 6126.6199 0.0000 0.9012 98.3111
## 3 0.0849 -25.2608 0.0000 0.0716 0.1004 6115.7120 0.0000 0.9029 98.3108
## 4 0.0848 -25.2590 0.0000 0.0716 0.1003 6113.5583 0.0000 0.9035 98.3104
## 5 0.0845 -25.2987 0.0000 0.0712 0.0999 6130.4595 0.0000 0.9042 98.3069
## 6 0.0838 -25.6397 0.0000 0.0708 0.0989 6110.9155 0.0000 0.8856 98.2673
## 7 0.0849 -25.2553 0.0000 0.0716 0.1003 6093.6187 0.0000 0.9032 98.3056
## 8 0.0858 -25.4302 0.0000 0.0726 0.1013 5815.4783 0.0000 0.8798 98.2548
## 9 0.0840 -25.4864 0.0000 0.0709 0.0992 6136.1755 0.0000 0.8949 98.2910
## 10 0.0857 -25.4394 0.0000 0.0725 0.1011 6119.9387 0.0000 0.8824 98.2773
## 11 0.0840 -25.4461 0.0000 0.0709 0.0993 6137.2999 0.0000 0.8972 98.2846
## 12 0.0843 -25.3667 0.0000 0.0711 0.0996 6139.7003 0.0000 0.9015 98.3112
## 13 0.0834 -26.0122 0.0000 0.0706 0.0983 6097.4426 0.0000 0.8619 98.2320
## 14 0.0855 -25.5039 0.0000 0.0723 0.1009 6132.2044 0.0000 0.8838 98.2809
## 15 0.0858 -25.4345 0.0000 0.0726 0.1012 6065.5634 0.0000 0.8802 98.2699
## 16 0.0857 -25.3876 0.0000 0.0724 0.1011 6084.2701 0.0000 0.8854 98.2804
## 17 0.0864 -25.6978 0.0000 0.0732 0.1017 6095.0306 0.0000 0.8563 98.2255
## 18 0.0859 -25.4833 0.0000 0.0727 0.1013 6097.4579 0.0000 0.8762 98.2643
## 19 0.0845 -25.2872 0.0000 0.0713 0.1000 6129.8120 0.0000 0.9044 98.3107
## 20 0.0840 -25.4803 0.0000 0.0709 0.0993 6134.6604 0.0000 0.8952 98.2849
## 21 0.0834 -26.1239 0.0000 0.0706 0.0982 5866.6536 0.0000 0.8545 98.1887
## 22 0.0838 -25.6500 0.0000 0.0708 0.0989 6125.8946 0.0000 0.8850 98.2760
## 23 0.0841 -25.4736 0.0000 0.0710 0.0994 6139.5385 0.0000 0.8964 98.3037
## 24 0.0848 -25.3905 0.0000 0.0716 0.1002 6138.7897 0.0000 0.8983 98.3079
## 25 0.0845 -25.3028 0.0000 0.0713 0.1000 6137.1040 0.0000 0.9037 98.3158
## 26 0.0845 -25.2953 0.0000 0.0713 0.0999 6135.1971 0.0000 0.9041 98.3146
## 27 0.0846 -25.3157 0.0000 0.0714 0.1000 6138.1680 0.0000 0.9030 98.3156
## 28 0.0848 -25.2994 0.0000 0.0716 0.1003 6135.5332 0.0000 0.9018 98.3136
## 29 0.0848 -25.4336 0.0000 0.0716 0.1002 6139.2789 0.0000 0.8968 98.3053
## 30 0.0848 -25.2786 0.0000 0.0715 0.1002 6132.9122 0.0000 0.9034 98.3152
## 31 0.0860 -25.6417 0.0000 0.0728 0.1013 6124.7300 0.0000 0.8691 98.2523
## 32 0.0839 -25.5285 0.0000 0.0708 0.0991 6133.5285 0.0000 0.8924 98.2876
## 33 0.0845 -25.2861 0.0000 0.0713 0.1000 6129.4913 0.0000 0.9045 98.3107
## 34 0.0844 -25.3083 0.0000 0.0712 0.0998 6135.9582 0.0000 0.9038 98.3125
## 35 0.0853 -25.3088 0.0000 0.0720 0.1008 6104.8864 0.0000 0.8949 98.2984
## 36 0.0850 -25.2588 0.0000 0.0717 0.1004 6097.5290 0.0000 0.9021 98.3066
## 37 0.0844 -25.3079 0.0000 0.0712 0.0998 6129.5739 0.0000 0.9039 98.3002
## 38 0.0842 -25.3611 0.0000 0.0711 0.0996 6125.0128 0.0000 0.9016 98.0731
## 39 0.0848 -25.2546 0.0000 0.0715 0.1002 6070.2736 0.0000 0.9041 98.2966
## 40 0.0847 -25.2696 0.0000 0.0714 0.1001 6126.6019 0.0000 0.9043 98.3133
## 41 0.0844 -25.3136 0.0000 0.0712 0.0998 6107.4113 0.0000 0.9037 98.2466
## 42 0.0846 -25.2975 0.0000 0.0714 0.1000 6136.6771 0.0000 0.9036 98.3160
## 43 0.0859 -25.4572 0.0000 0.0726 0.1013 6082.7180 0.0000 0.8782 98.2673
## 44 0.0843 -25.3487 0.0000 0.0711 0.0997 6139.3211 0.0000 0.9023 98.3124
## 45 0.0843 -25.3487 0.0000 0.0711 0.0997 6139.3211 0.0000 0.9023 98.3124
## 46 0.0847 -25.2637 0.0000 0.0714 0.1001 6107.4724 0.0000 0.9046 98.3050
## 47 0.0846 -25.2674 0.0000 0.0714 0.1001 6110.4621 0.0000 0.9047 98.3046
## 48 0.0847 -25.2769 0.0000 0.0714 0.1001 6131.5570 0.0000 0.9042 98.3149
## 49 0.0852 -25.2840 0.0000 0.0719 0.1007 6084.8674 0.0000 0.8975 98.3001
## 50 0.0862 -25.6253 0.0000 0.0730 0.1016 6094.1751 0.0000 0.8627 98.2382
## 51 0.0845 -25.2961 0.0000 0.0712 0.0999 6106.4687 0.0000 0.9043 98.2759
## 52 0.0838 -25.6321 0.0000 0.0708 0.0989 6107.6173 0.0000 0.8860 98.2652
## 53 0.0838 -25.6414 0.0000 0.0708 0.0989 6096.6428 0.0000 0.8854 98.2586
## 54 0.0840 -25.4860 0.0000 0.0709 0.0992 6136.3939 0.0000 0.8949 98.2917
## 55 0.0836 -25.7545 0.0000 0.0707 0.0987 6104.8789 0.0000 0.8784 98.2592
## 56 0.0835 -25.8994 0.0000 0.0707 0.0985 6121.0223 0.0000 0.8695 98.2499
## 57 0.0846 -25.2734 0.0000 0.0713 0.1000 6024.8936 0.0000 0.9048 98.2421
## 58 0.0844 -25.3160 0.0000 0.0712 0.0998 6128.8492 0.0000 0.9036 98.2922
## 59 0.0848 -25.2534 0.0000 0.0715 0.1003 6044.2123 0.0000 0.9040 98.2893
## 60 0.0844 -25.3001 0.0000 0.0712 0.0999 6124.4170 0.0000 0.9042 98.2970
## 61 0.0842 -25.3562 0.0000 0.0711 0.0996 6136.0889 0.0000 0.9019 98.2647
## 62 0.0860 -25.5105 0.0000 0.0728 0.1014 5854.7467 0.0000 0.8718 98.2442
## 63 0.0846 -25.2736 0.0000 0.0714 0.1001 6128.2079 0.0000 0.9044 98.3133
## 64 0.0841 -25.4151 0.0000 0.0710 0.0994 6139.6044 0.0000 0.8989 98.2964
## 65 0.0848 -25.2552 0.0000 0.0715 0.1002 6052.1519 0.0000 0.9043 98.2888
## 66 0.0850 -25.2615 0.0000 0.0717 0.1005 6098.0397 0.0000 0.9015 98.3064
## 67 0.0853 -25.3322 0.0000 0.0721 0.1008 6124.0851 0.0000 0.8938 98.2986
## 68 0.0851 -25.2929 0.0000 0.0718 0.1006 6126.5737 0.0000 0.8989 98.3075
## 69 0.0850 -25.2832 0.0000 0.0717 0.1005 6127.6635 0.0000 0.9004 98.3101
## 70 0.0851 -25.2657 0.0000 0.0718 0.1005 6097.0543 0.0000 0.9006 98.3055
## 71 0.0847 -25.2667 0.0000 0.0714 0.1002 6123.9576 0.0000 0.9043 98.3126
## 72 0.0854 -25.3028 0.0000 0.0721 0.1008 6025.4433 0.0000 0.8945 98.2898
## 73 0.0853 -25.3078 0.0000 0.0720 0.1008 6097.7231 0.0000 0.8947 98.2974
## 74 0.0846 -25.2715 0.0000 0.0713 0.1000 6103.5913 0.0000 0.9048 98.2976
## 75 0.0834 -25.9940 0.0000 0.0706 0.0983 4123.3113 0.0000 0.8628 97.9583
## 76 0.0861 -25.6409 0.0000 0.0729 0.1014 6123.4444 0.0000 0.8667 98.2474
## 77 0.0858 -25.5680 0.0000 0.0726 0.1011 6128.5852 0.0000 0.8767 98.2672
## 78 0.0854 -25.3338 0.0000 0.0722 0.1009 6097.6044 0.0000 0.8916 98.2923
## 79 0.0851 -25.2678 0.0000 0.0718 0.1005 6103.3109 0.0000 0.9005 98.3063
## 80 0.0841 -25.4195 0.0000 0.0710 0.0994 6139.5464 0.0000 0.8987 98.2981
## 81 0.0850 -25.2858 0.0000 0.0717 0.1005 6129.1700 0.0000 0.9004 98.3104
## 82 0.0850 -25.3266 0.0000 0.0718 0.1005 6134.7455 0.0000 0.8984 98.3077
## 83 0.0850 -25.3254 0.0000 0.0718 0.1005 6135.0019 0.0000 0.8987 98.3082
## 84 0.0858 -25.4810 0.0000 0.0726 0.1012 6121.4548 0.0000 0.8789 98.2708
## 85 0.0854 -25.3527 0.0000 0.0722 0.1009 6121.2500 0.0000 0.8912 98.2936
## 86 0.0844 -25.3095 0.0000 0.0712 0.0999 6136.6950 0.0000 0.9038 98.3136
## 87 0.0844 -25.2967 0.0000 0.0712 0.0999 6092.7519 0.0000 0.9043 98.2567
## 88 0.0844 -25.3013 0.0000 0.0712 0.0998 6104.6502 0.0000 0.9042 98.2656
## 89 0.0846 -25.2754 0.0000 0.0714 0.1001 6127.3125 0.0000 0.9045 98.3122
## 90 0.0843 -25.3700 0.0000 0.0711 0.0997 6139.7528 0.0000 0.9014 98.3123
## 91 0.0845 -25.2899 0.0000 0.0713 0.0999 6122.3553 0.0000 0.9045 98.3012
## 92 0.0845 -25.2801 0.0000 0.0713 0.1000 6117.2375 0.0000 0.9047 98.3021
## 93 0.0842 -25.3960 0.0000 0.0710 0.0995 6139.8962 0.0000 0.8999 98.3009
## 94 0.0841 -25.4210 0.0000 0.0710 0.0994 6139.5353 0.0000 0.8986 98.2987
## 95 0.0842 -25.3700 0.0000 0.0710 0.0996 6139.4434 0.0000 0.9012 98.2984
## 96 0.0833 -26.2647 0.0000 0.0706 0.0980 5814.2283 0.0000 0.8453 98.1715
## 97 0.0834 -26.0442 0.0000 0.0706 0.0983 5383.0439 0.0000 0.8596 98.1253
## 98 0.0851 -25.2828 0.0000 0.0718 0.1006 6120.8024 0.0000 0.8993 98.3074
## 99 0.0836 -25.8305 0.0000 0.0707 0.0986 6084.3226 0.0000 0.8735 98.2473
## 100 0.0847 -25.2654 0.0000 0.0715 0.1002 6124.5153 0.0000 0.9040 98.3131
## 101 0.0854 -25.3280 0.0000 0.0721 0.1008 6113.2571 0.0000 0.8931 98.2962
## 102 0.0842 -25.3807 0.0000 0.0710 0.0995 6139.8064 0.0000 0.9007 98.3024
## 103 0.0845 -25.2874 0.0000 0.0713 0.1000 6132.2195 0.0000 0.9044 98.3131
## 104 0.0859 -25.5046 0.0000 0.0727 0.1013 6109.9472 0.0000 0.8750 98.2627
## 105 0.0850 -25.2625 0.0000 0.0717 0.1005 6096.3240 0.0000 0.9012 98.3059
## 106 0.0859 -25.6124 0.0000 0.0727 0.1012 6126.2358 0.0000 0.8721 98.2581
## 107 0.0852 -25.2854 0.0000 0.0719 0.1007 6098.5359 0.0000 0.8976 98.3019
## 108 0.0845 -25.2978 0.0000 0.0712 0.0999 6128.5545 0.0000 0.9042 98.3046
## 109 0.0850 -25.2560 0.0000 0.0717 0.1005 6060.6732 0.0000 0.9020 98.2995
## 110 0.0851 -25.2797 0.0000 0.0718 0.1005 6122.2290 0.0000 0.8999 98.3086
## 111 0.0849 -25.2637 0.0000 0.0717 0.1004 6115.6821 0.0000 0.9020 98.3101
## 112 0.0845 -25.2988 0.0000 0.0713 0.1000 6136.5266 0.0000 0.9039 98.3157
## 113 0.0847 -25.2811 0.0000 0.0715 0.1002 6133.6295 0.0000 0.9033 98.3153
## 114 0.0837 -25.6857 0.0000 0.0707 0.0988 6129.0787 0.0000 0.8829 98.2750
## H2
## 1 59.0950
## 2 59.2118
## 3 59.1995
## 4 59.1854
## 5 59.0621
## 6 57.7128
## 7 59.0186
## 8 57.2989
## 9 58.5143
## 10 58.0491
## 11 58.2970
## 12 59.2143
## 13 56.5626
## 14 58.1708
## 15 57.8017
## 16 58.1542
## 17 56.3544
## 18 57.6135
## 19 59.1951
## 20 58.3060
## 21 55.2089
## 22 58.0037
## 23 58.9512
## 24 59.0994
## 25 59.3766
## 26 59.3341
## 27 59.3699
## 28 59.2965
## 29 59.0063
## 30 59.3530
## 31 57.2175
## 32 58.3969
## 33 59.1960
## 34 59.2580
## 35 58.7667
## 36 59.0542
## 37 58.8321
## 38 51.8981
## 39 58.7052
## 40 59.2878
## 41 57.0336
## 42 59.3831
## 43 57.7150
## 44 59.2573
## 45 59.2573
## 46 58.9974
## 47 58.9823
## 48 59.3434
## 49 58.8266
## 50 56.7613
## 51 58.0007
## 52 57.6422
## 53 57.4264
## 54 58.5362
## 55 57.4460
## 56 57.1383
## 57 56.8874
## 58 58.5533
## 59 58.4543
## 60 58.7197
## 61 57.6262
## 62 56.9540
## 63 59.2884
## 64 58.7009
## 65 58.4378
## 66 59.0449
## 67 58.7739
## 68 59.0828
## 69 59.1749
## 70 59.0137
## 71 59.2621
## 72 58.4713
## 73 58.7333
## 74 58.7399
## 75 48.9791
## 76 57.0593
## 77 57.7090
## 78 58.5588
## 79 59.0425
## 80 58.7569
## 81 59.1846
## 82 59.0904
## 83 59.1098
## 84 57.8288
## 85 58.6030
## 86 59.2987
## 87 57.3622
## 88 57.6578
## 89 59.2496
## 90 59.2508
## 91 58.8665
## 92 58.8967
## 93 58.8535
## 94 58.7800
## 95 58.7678
## 96 54.6909
## 97 53.3430
## 98 59.0799
## 99 57.0550
## 100 59.2820
## 101 58.6911
## 102 58.9082
## 103 59.2809
## 104 57.5599
## 105 59.0271
## 106 57.4091
## 107 58.8910
## 108 58.9821
## 109 58.8050
## 110 59.1209
## 111 59.1762
## 112 59.3715
## 113 59.3564
## 114 57.9693# 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 -0.6057 -0.0502 0.0025 1.0129 0.8987 6131.4779 0.0079 0.7880 -0.0502
## 2 -0.4390 -0.0355 0.0013 1.0165 0.9012 6126.6199 0.0088 0.8783 -0.0355
## 3 -0.3117 -0.0231 0.0005 1.0187 0.9029 6115.7120 0.0093 0.9302 -0.0231
## 4 -0.2387 -0.0156 0.0002 1.0195 0.9035 6113.5583 0.0094 0.9388 -0.0156
## 5 0.2767 0.0353 0.0013 1.0203 0.9042 6130.4595 0.0095 0.9520 0.0353
## 6 1.4144 0.1323 0.0173 1.0009 0.8856 6110.9155 0.0096 0.9575 0.1323
## 7 -0.2915 -0.0212 0.0005 1.0193 0.9032 6093.6187 0.0095 0.9510 -0.0212
## 8 -1.4611 -0.1555 0.0238 0.9949 0.8798 5815.4783 0.0096 0.9587 -0.1554
## 9 1.0286 0.1015 0.0103 1.0106 0.8949 6136.1755 0.0095 0.9493 0.1015
## 10 -1.4708 -0.1376 0.0188 0.9955 0.8824 6119.9387 0.0076 0.7571 -0.1378
## 11 0.9107 0.0923 0.0085 1.0131 0.8972 6137.2999 0.0096 0.9608 0.0923
## 12 0.5722 0.0603 0.0037 1.0169 0.9015 6139.7003 0.0089 0.8902 0.0602
## 13 2.1285 0.1796 0.0312 0.9758 0.8619 6097.4426 0.0093 0.9299 0.1795
## 14 -1.9069 -0.1121 0.0125 0.9924 0.8838 6132.2044 0.0030 0.2998 -0.1125
## 15 -1.4710 -0.1524 0.0229 0.9948 0.8802 6065.5634 0.0091 0.9116 -0.1524
## 16 -1.2924 -0.1304 0.0169 1.0001 0.8854 6084.2701 0.0090 0.9038 -0.1304
## 17 -2.2623 -0.2330 0.0524 0.9685 0.8563 6095.0306 0.0079 0.7937 -0.2336
## 18 -1.6352 -0.1660 0.0271 0.9899 0.8762 6097.4579 0.0085 0.8517 -0.1661
## 19 0.1798 0.0259 0.0007 1.0205 0.9044 6129.8120 0.0094 0.9443 0.0259
## 20 1.0128 0.1006 0.0101 1.0110 0.8952 6134.6604 0.0096 0.9581 0.1006
## 21 2.2752 0.1918 0.0352 0.9683 0.8545 5866.6536 0.0096 0.9649 0.1915
## 22 1.4409 0.1330 0.0175 1.0001 0.8850 6125.8946 0.0094 0.9380 0.1330
## 23 0.9318 0.0835 0.0070 1.0102 0.8964 6139.5385 0.0076 0.7603 0.0835
## 24 -0.2808 -0.0154 0.0002 1.0090 0.8983 6138.7897 0.0044 0.4446 -0.0153
## 25 0.1638 0.0232 0.0005 1.0190 0.9037 6137.1040 0.0087 0.8743 0.0232
## 26 0.1882 0.0262 0.0007 1.0199 0.9041 6135.1971 0.0092 0.9160 0.0262
## 27 0.1245 0.0185 0.0003 1.0176 0.9030 6138.1680 0.0081 0.8067 0.0185
## 28 -0.2592 -0.0168 0.0003 1.0161 0.9018 6135.5332 0.0078 0.7850 -0.0168
## 29 -0.2613 -0.0118 0.0001 1.0058 0.8968 6139.2789 0.0029 0.2888 -0.0118
## 30 -0.1346 -0.0051 0.0000 1.0187 0.9034 6132.9122 0.0088 0.8766 -0.0051
## 31 -2.8483 -0.1741 0.0300 0.9771 0.8691 6124.7300 0.0030 0.3004 -0.1756
## 32 1.1448 0.1107 0.0122 1.0080 0.8924 6133.5285 0.0095 0.9465 0.1107
## 33 0.1715 0.0251 0.0006 1.0205 0.9045 6129.4913 0.0094 0.9444 0.0251
## 34 0.3069 0.0376 0.0014 1.0198 0.9038 6135.9582 0.0093 0.9311 0.0376
## 35 -0.8825 -0.0835 0.0070 1.0101 0.8949 6104.8864 0.0090 0.9030 -0.0835
## 36 -0.4084 -0.0333 0.0011 1.0181 0.9021 6097.5290 0.0094 0.9434 -0.0333
## 37 0.3439 0.0418 0.0018 1.0201 0.9039 6129.5739 0.0096 0.9589 0.0418
## 38 0.6146 0.0670 0.0045 1.0179 0.9016 6125.0128 0.0097 0.9719 0.0670
## 39 -0.1756 -0.0092 0.0001 1.0203 0.9041 6070.2736 0.0096 0.9613 -0.0092
## 40 -0.0384 0.0045 0.0000 1.0202 0.9043 6126.6019 0.0093 0.9309 0.0045
## 41 0.3897 0.0463 0.0022 1.0200 0.9037 6107.4113 0.0097 0.9693 0.0463
## 42 0.0665 0.0139 0.0002 1.0187 0.9036 6136.6771 0.0086 0.8560 0.0139
## 43 -1.5468 -0.1593 0.0250 0.9925 0.8782 6082.7180 0.0089 0.8884 -0.1593
## 44 0.4908 0.0533 0.0029 1.0178 0.9023 6139.3211 0.0090 0.8961 0.0533
## 45 0.4908 0.0533 0.0029 1.0178 0.9023 6139.3211 0.0090 0.8961 0.0533
## 46 -0.0307 0.0055 0.0000 1.0208 0.9046 6107.4724 0.0096 0.9555 0.0055
## 47 0.0194 0.0104 0.0001 1.0209 0.9047 6110.4621 0.0096 0.9562 0.0104
## 48 0.0022 0.0084 0.0001 1.0199 0.9042 6131.5570 0.0091 0.9141 0.0084
## 49 -0.7408 -0.0691 0.0048 1.0132 0.8975 6084.8674 0.0094 0.9352 -0.0691
## 50 -2.0610 -0.2127 0.0439 0.9755 0.8627 6094.1751 0.0082 0.8186 -0.2131
## 51 0.2828 0.0362 0.0013 1.0206 0.9043 6106.4687 0.0097 0.9669 0.0362
## 52 1.3971 0.1312 0.0171 1.0014 0.8860 6107.6173 0.0096 0.9598 0.1311
## 53 1.4174 0.1329 0.0175 1.0008 0.8854 6096.6428 0.0096 0.9625 0.1328
## 54 1.0271 0.1013 0.0103 1.0106 0.8949 6136.3939 0.0095 0.9480 0.1013
## 55 1.6568 0.1496 0.0220 0.9933 0.8784 6104.8789 0.0095 0.9497 0.1495
## 56 1.9579 0.1658 0.0268 0.9834 0.8695 6121.0223 0.0090 0.8957 0.1658
## 57 0.1167 0.0202 0.0004 1.0212 0.9048 6024.8936 0.0097 0.9696 0.0202
## 58 0.3951 0.0466 0.0022 1.0199 0.9036 6128.8492 0.0096 0.9629 0.0467
## 59 -0.1920 -0.0109 0.0001 1.0203 0.9040 6044.2123 0.0096 0.9641 -0.0109
## 60 0.3000 0.0377 0.0014 1.0204 0.9042 6124.4170 0.0096 0.9612 0.0377
## 61 0.5911 0.0647 0.0042 1.0181 0.9019 6136.0889 0.0097 0.9677 0.0647
## 62 -1.7023 -0.1856 0.0336 0.9864 0.8718 5854.7467 0.0095 0.9532 -0.1855
## 63 0.0199 0.0103 0.0001 1.0204 0.9044 6128.2079 0.0093 0.9322 0.0103
## 64 0.8053 0.0829 0.0069 1.0148 0.8989 6139.6044 0.0095 0.9530 0.0829
## 65 -0.1380 -0.0054 0.0000 1.0206 0.9043 6052.1519 0.0096 0.9644 -0.0054
## 66 -0.4638 -0.0392 0.0015 1.0174 0.9015 6098.0397 0.0094 0.9402 -0.0392
## 67 -0.9317 -0.0846 0.0072 1.0081 0.8938 6124.0851 0.0082 0.8155 -0.0846
## 68 -0.6213 -0.0535 0.0029 1.0137 0.8989 6126.5737 0.0085 0.8484 -0.0535
## 69 -0.5015 -0.0416 0.0017 1.0154 0.9004 6127.6635 0.0086 0.8601 -0.0416
## 70 -0.5318 -0.0464 0.0022 1.0165 0.9006 6097.0543 0.0094 0.9372 -0.0464
## 71 -0.0668 0.0017 0.0000 1.0202 0.9043 6123.9576 0.0094 0.9350 0.0017
## 72 -0.9029 -0.0880 0.0077 1.0102 0.8945 6025.4433 0.0095 0.9504 -0.0880
## 73 -0.8906 -0.0849 0.0072 1.0100 0.8947 6097.7231 0.0091 0.9139 -0.0849
## 74 0.0790 0.0164 0.0003 1.0210 0.9048 6103.5913 0.0096 0.9614 0.0164
## 75 2.0691 0.1794 0.0311 0.9772 0.8628 4123.3113 0.0097 0.9720 0.1792
## 76 -2.4712 -0.1878 0.0348 0.9761 0.8667 6123.4444 0.0046 0.4585 -0.1892
## 77 -2.4011 -0.1443 0.0207 0.9850 0.8767 6128.5852 0.0030 0.3002 -0.1452
## 78 -1.0378 -0.1009 0.0102 1.0067 0.8916 6097.6044 0.0090 0.9033 -0.1009
## 79 -0.5413 -0.0472 0.0022 1.0162 0.9005 6103.3109 0.0093 0.9307 -0.0473
## 80 0.8188 0.0839 0.0071 1.0145 0.8987 6139.5464 0.0095 0.9493 0.0839
## 81 -0.4902 -0.0402 0.0016 1.0153 0.9004 6129.1700 0.0085 0.8477 -0.0401
## 82 -0.5824 -0.0454 0.0021 1.0117 0.8984 6134.7455 0.0070 0.6995 -0.0454
## 83 -0.5527 -0.0427 0.0018 1.0120 0.8987 6135.0019 0.0070 0.6992 -0.0426
## 84 -1.6425 -0.1504 0.0223 0.9913 0.8789 6121.4548 0.0071 0.7058 -0.1506
## 85 -1.0677 -0.0990 0.0098 1.0054 0.8912 6121.2500 0.0082 0.8161 -0.0991
## 86 0.2981 0.0366 0.0014 1.0196 0.9038 6136.6950 0.0092 0.9204 0.0366
## 87 0.2900 0.0369 0.0014 1.0206 0.9043 6092.7519 0.0097 0.9688 0.0369
## 88 0.3171 0.0395 0.0016 1.0205 0.9042 6104.6502 0.0097 0.9680 0.0395
## 89 0.0600 0.0142 0.0002 1.0205 0.9045 6127.3125 0.0094 0.9390 0.0142
## 90 0.5379 0.0554 0.0031 1.0162 0.9014 6139.7528 0.0084 0.8371 0.0554
## 91 0.2274 0.0307 0.0010 1.0207 0.9045 6122.3553 0.0096 0.9589 0.0307
## 92 0.1507 0.0233 0.0006 1.0209 0.9047 6117.2375 0.0096 0.9585 0.0233
## 93 0.7349 0.0767 0.0059 1.0158 0.8999 6139.8962 0.0095 0.9482 0.0767
## 94 0.8232 0.0842 0.0071 1.0144 0.8986 6139.5353 0.0095 0.9474 0.0842
## 95 0.6392 0.0686 0.0047 1.0173 0.9012 6139.4434 0.0096 0.9560 0.0686
## 96 2.4780 0.2038 0.0393 0.9588 0.8453 5814.2283 0.0096 0.9644 0.2035
## 97 2.1496 0.1845 0.0327 0.9738 0.8596 5383.0439 0.0097 0.9704 0.1842
## 98 -0.6052 -0.0529 0.0028 1.0146 0.8993 6120.8024 0.0089 0.8861 -0.0529
## 99 1.7998 0.1599 0.0250 0.9882 0.8735 6084.3226 0.0095 0.9536 0.1598
## 100 -0.1246 -0.0041 0.0000 1.0199 0.9040 6124.5153 0.0093 0.9280 -0.0041
## 101 -0.9724 -0.0919 0.0084 1.0079 0.8931 6113.2571 0.0087 0.8720 -0.0919
## 102 0.6762 0.0715 0.0052 1.0166 0.9007 6139.8064 0.0095 0.9480 0.0715
## 103 0.1587 0.0237 0.0006 1.0203 0.9044 6132.2195 0.0093 0.9331 0.0237
## 104 -1.7154 -0.1686 0.0279 0.9881 0.8750 6109.9472 0.0079 0.7928 -0.1688
## 105 -0.4872 -0.0416 0.0017 1.0171 0.9012 6096.3240 0.0094 0.9402 -0.0417
## 106 -2.6810 -0.1629 0.0263 0.9802 0.8721 6126.2358 0.0030 0.3003 -0.1641
## 107 -0.7303 -0.0675 0.0046 1.0132 0.8976 6098.5359 0.0092 0.9238 -0.0675
## 108 0.2758 0.0353 0.0013 1.0204 0.9042 6128.5545 0.0096 0.9553 0.0353
## 109 -0.4254 -0.0353 0.0013 1.0181 0.9020 6060.6732 0.0096 0.9565 -0.0353
## 110 -0.5567 -0.0478 0.0023 1.0152 0.8999 6122.2290 0.0089 0.8859 -0.0478
## 111 -0.3981 -0.0320 0.0010 1.0178 0.9020 6115.6821 0.0092 0.9230 -0.0320
## 112 0.1597 0.0230 0.0005 1.0194 0.9039 6136.5266 0.0089 0.8882 0.0230
## 113 -0.1288 -0.0046 0.0000 1.0186 0.9033 6133.6295 0.0087 0.8679 -0.0046
## 114 1.5248 0.1376 0.0187 0.9977 0.8829 6129.0787 0.0092 0.9155 0.1376
# 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: 114
## Columns: 20
## $ Sequence <chr> "RSMA01", "RSMA02", "RSMA03", "RSMA04", "RSMA05", "RSMA06…
## $ code <chr> "RS001", "RS001", "RS001", "RS002", "RS002", "RS002", "RS…
## $ author <chr> "Abdullahi_2008", "Abdullahi_2008", "Abdullahi_2008", "Ad…
## $ author2 <chr> "Abdullahi", "Abdullahi", "Abdullahi", "Adetifa", "Adetif…
## $ year_pub <dbl> 2008, 2008, 2008, 2012, 2012, 2012, 2019, 2014, 2013, 201…
## $ 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 1.00000000 0.10310418 -0.14669601 0.0905834527 0.097926321
## year_pub 0.10310418 1.00000000 0.27337796 0.1062859841 0.385707374
## age -0.14669601 0.27337796 1.00000000 -0.1299246564 0.092943947
## sample 0.09058345 0.10628598 -0.12992466 1.0000000000 0.109530359
## stage 0.09792632 0.38570737 0.09294395 0.1095303594 1.000000000
## type_prev 0.23997288 -0.27648646 -0.19831317 0.1436559956 -0.045862759
## comorb -0.12026995 -0.12263831 0.10634396 0.1525897404 0.075080440
## type_morb 0.11542366 0.19332116 -0.03889823 -0.1500740916 -0.003367930
## meth_iden -0.01252151 0.02793861 0.23635694 -0.0008169272 0.074169742
## type_samp 0.03547548 0.20328959 0.15208371 -0.0655212712 -0.000827407
## quality 0.02031117 0.10873628 0.13815777 -0.1099059245 0.110509045
## type_prev comorb type_morb meth_iden type_samp
## prev 0.23997288 -0.12026995 0.11542366 -0.0125215147 0.035475481
## year_pub -0.27648646 -0.12263831 0.19332116 0.0279386090 0.203289586
## age -0.19831317 0.10634396 -0.03889823 0.2363569416 0.152083713
## sample 0.14365600 0.15258974 -0.15007409 -0.0008169272 -0.065521271
## stage -0.04586276 0.07508044 -0.00336793 0.0741697419 -0.000827407
## type_prev 1.00000000 0.13322770 -0.11781620 -0.0121452734 -0.018265743
## comorb 0.13322770 1.00000000 -0.94364112 0.1123207495 0.206434119
## type_morb -0.11781620 -0.94364112 1.00000000 -0.0931527017 -0.208166261
## meth_iden -0.01214527 0.11232075 -0.09315270 1.0000000000 0.409617845
## type_samp -0.01826574 0.20643412 -0.20816626 0.4096178449 1.000000000
## quality -0.21004191 -0.11163024 0.08786861 -0.0268484023 0.048143408
## quality
## prev 0.02031117
## year_pub 0.10873628
## age 0.13815777
## sample -0.10990592
## stage 0.11050905
## type_prev -0.21004191
## comorb -0.11163024
## type_morb 0.08786861
## meth_iden -0.02684840
## type_samp 0.04814341
## 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 = 114; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1853)
## 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 = 112) = 0.5508, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 112) = 0.5342, p-val = 0.4664
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1409 0.0385 3.6614 112 0.0004 0.0647 0.2172 ***
## comorb -0.0154 0.0211 -0.7309 112 0.4664 -0.0572 0.0264
##
## ---
## 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 = 114; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1853)
## 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 = 112) = 0.5508, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 112) = 0.5342, p-val = 0.4664
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1409 0.0385 3.6614 112 0.0004 0.0647 0.2172 ***
## comorb -0.0154 0.0211 -0.7309 112 0.4664 -0.0572 0.0264
##
## ---
## 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 = 114; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1853)
## 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 = 112) = 0.5500, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 112) = 0.6976, p-val = 0.4054
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0961 0.0226 4.2484 112 <.0001 0.0513 0.1410 ***
## meth_iden 0.0151 0.0180 0.8352 112 0.4054 -0.0207 0.0508
##
## ---
## 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 = 114; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1853)
## 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 = 112) = 0.5081, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 112) = 9.9941, p-val = 0.0020
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0720 0.0156 4.6201 112 <.0001 0.0411 0.1029 ***
## type_samp 0.0260 0.0082 3.1613 112 0.0020 0.0097 0.0423 **
##
## ---
## 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 = 114; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1853)
## 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 = 112) = 0.5091, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 112) = 9.7507, p-val = 0.0023
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.0652 0.0579 -1.1272 112 0.2621 -0.1799 0.0494
## quality 0.0545 0.0175 3.1226 112 0.0023 0.0199 0.0891 **
##
## ---
## 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 = 114; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1853)
## 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 = 108) = 0.4774, p-val = 1.0000
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 108) = 3.4390, p-val = 0.0064
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.0546 0.0809 -0.6744 108 0.5015 -0.2150 0.1058
## stage 0.0074 0.0161 0.4586 108 0.6474 -0.0245 0.0392
## comorb -0.0167 0.0209 -0.7997 108 0.4257 -0.0581 0.0247
## meth_iden 0.0139 0.0178 0.7815 108 0.4362 -0.0214 0.0492
## type_samp 0.0197 0.0089 2.2291 108 0.0279 0.0022 0.0373 *
## quality 0.0419 0.0188 2.2255 108 0.0281 0.0046 0.0792 *
##
## ---
## 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 = 114; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1586)
## 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 = 108) = 0.6680, p-val = 1.0000
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 108) = 0.5575, p-val = 0.7323
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0909 0.0781 1.1642 108 0.2469 -0.0639 0.2456
## age -0.0025 0.0072 -0.3442 108 0.7314 -0.0168 0.0118
## stage 0.0219 0.0184 1.1917 108 0.2360 -0.0145 0.0583
## comorb -0.0201 0.0207 -0.9743 108 0.3321 -0.0611 0.0208
## meth_iden -0.0078 0.0176 -0.4425 108 0.6590 -0.0427 0.0271
## quality 0.0064 0.0186 0.3472 108 0.7291 -0.0304 0.0433
##
## ---
## 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 = 114; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1461)
## 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 = 108) = 0.8017, p-val = 1.0000
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 108) = 1.3883, p-val = 0.2344
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0439 0.0798 0.5495 108 0.5838 -0.1144 0.2021
## age -0.0125 0.0070 -1.7868 108 0.0768 -0.0264 0.0014 .
## stage 0.0253 0.0199 1.2732 108 0.2057 -0.0141 0.0646
## comorb -0.0173 0.0222 -0.7778 108 0.4384 -0.0613 0.0267
## type_samp 0.0036 0.0089 0.4025 108 0.6881 -0.0141 0.0213
## quality 0.0199 0.0184 1.0839 108 0.2808 -0.0165 0.0563
##
## ---
## 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 = 114; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.2685)
## 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 = 108) = 0.4974, p-val = 1.0000
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 108) = 0.7320, p-val = 0.6010
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0468 0.0818 0.5715 108 0.5688 -0.1155 0.2090
## age -0.0125 0.0083 -1.4927 108 0.1384 -0.0290 0.0041
## stage 0.0211 0.0228 0.9236 108 0.3578 -0.0241 0.0663
## meth_iden 0.0119 0.0192 0.6189 108 0.5373 -0.0262 0.0499
## type_samp 0.0073 0.0102 0.7124 108 0.4778 -0.0130 0.0276
## quality 0.0091 0.0208 0.4394 108 0.6613 -0.0321 0.0503
##
## ---
## 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 1.00000000 0.10310418 -0.14669601 0.0905834527 0.097926321
## year_pub 0.10310418 1.00000000 0.27337796 0.1062859841 0.385707374
## age -0.14669601 0.27337796 1.00000000 -0.1299246564 0.092943947
## sample 0.09058345 0.10628598 -0.12992466 1.0000000000 0.109530359
## stage 0.09792632 0.38570737 0.09294395 0.1095303594 1.000000000
## type_prev 0.23997288 -0.27648646 -0.19831317 0.1436559956 -0.045862759
## comorb -0.12026995 -0.12263831 0.10634396 0.1525897404 0.075080440
## type_morb 0.11542366 0.19332116 -0.03889823 -0.1500740916 -0.003367930
## meth_iden -0.01252151 0.02793861 0.23635694 -0.0008169272 0.074169742
## type_samp 0.03547548 0.20328959 0.15208371 -0.0655212712 -0.000827407
## quality 0.02031117 0.10873628 0.13815777 -0.1099059245 0.110509045
## type_prev comorb type_morb meth_iden type_samp
## prev 0.23997288 -0.12026995 0.11542366 -0.0125215147 0.035475481
## year_pub -0.27648646 -0.12263831 0.19332116 0.0279386090 0.203289586
## age -0.19831317 0.10634396 -0.03889823 0.2363569416 0.152083713
## sample 0.14365600 0.15258974 -0.15007409 -0.0008169272 -0.065521271
## stage -0.04586276 0.07508044 -0.00336793 0.0741697419 -0.000827407
## type_prev 1.00000000 0.13322770 -0.11781620 -0.0121452734 -0.018265743
## comorb 0.13322770 1.00000000 -0.94364112 0.1123207495 0.206434119
## type_morb -0.11781620 -0.94364112 1.00000000 -0.0931527017 -0.208166261
## meth_iden -0.01214527 0.11232075 -0.09315270 1.0000000000 0.409617845
## type_samp -0.01826574 0.20643412 -0.20816626 0.4096178449 1.000000000
## quality -0.21004191 -0.11163024 0.08786861 -0.0268484023 0.048143408
## quality
## prev 0.02031117
## year_pub 0.10873628
## age 0.13815777
## sample -0.10990592
## stage 0.11050905
## type_prev -0.21004191
## comorb -0.11163024
## type_morb 0.08786861
## meth_iden -0.02684840
## type_samp 0.04814341
## 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 = 114; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1853)
## 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 = 112) = 0.5508, p-val = 1.0000
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 112) = 0.5342, p-val = 0.4664
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1409 0.0385 3.6614 112 0.0004 0.0647 0.2172 ***
## comorb -0.0154 0.0211 -0.7309 112 0.4664 -0.0572 0.0264
##
## ---
## 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 = 114; tau^2 estimator: ML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1853)
## 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 = 106) = 0.4420, p-val = 1.0000
##
## Test of Moderators (coefficients 2:8):
## F(df1 = 7, df2 = 106) = 3.8180, p-val = 0.0010
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -7.6557 3.4527 -2.2173 106 0.0287 -14.5010 -0.8104 *
## year_pub 0.0037 0.0017 2.1886 106 0.0308 0.0004 0.0071 *
## stage -0.0050 0.0168 -0.2981 106 0.7662 -0.0383 0.0283
## comorb -0.0159 0.0205 -0.7761 106 0.4394 -0.0565 0.0247
## type_prev 0.0357 0.0150 2.3863 106 0.0188 0.0060 0.0654 *
## meth_iden 0.0264 0.0185 1.4251 106 0.1571 -0.0103 0.0631
## type_samp 0.0136 0.0089 1.5213 106 0.1312 -0.0041 0.0312
## quality 0.0490 0.0184 2.6575 106 0.0091 0.0125 0.0856 **
##
## ---
## 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 9 358.9783 383.6041 360.7091 -170.4892 0.4420 0.0000
## Reduced 3 347.0871 355.2957 347.3053 -170.5436 0.1088 1.0000 0.5508 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 = 114; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1886)
## 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 = 110) = 0.5488, p-val = 1.0000
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 110) = 0.3075, p-val = 0.8199
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1015 0.0948 1.0717 110 0.2862 -0.0862 0.2893
## age 0.0342 0.0759 0.4508 110 0.6531 -0.1162 0.1845
## comorb 0.0092 0.0509 0.1816 110 0.8563 -0.0916 0.1100
## age:comorb -0.0212 0.0403 -0.5263 110 0.5997 -0.1011 0.0587
##
## ---
## 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 = 112) = 0.5342, p-val = 0.5850
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt 0.1409 0.0385 3.6614 112 0.2380 0.0647 0.2172
## comorb -0.0154 0.0211 -0.7309 112 0.5850 -0.0572 0.0264
##
## ---
## 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 = 106) = 3.8180, p-val = 0.0300
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt -7.6557 3.4527 -2.2173 106 0.0870 -14.5010 -0.8104 .
## year_pub 0.0037 0.0017 2.1886 106 0.0910 0.0004 0.0071 .
## stage -0.0050 0.0168 -0.2981 106 0.7830 -0.0383 0.0283
## comorb -0.0159 0.0205 -0.7761 106 0.5590 -0.0565 0.0247
## type_prev 0.0357 0.0150 2.3863 106 0.0620 0.0060 0.0654 .
## meth_iden 0.0264 0.0185 1.4251 106 0.2410 -0.0103 0.0631
## type_samp 0.0136 0.0089 1.5213 106 0.2380 -0.0041 0.0312
## quality 0.0490 0.0184 2.6575 106 0.0420 0.0125 0.0856 *
##
## ---
## 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 = 114; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1885)
## 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 = 110) = 0.5461, p-val = 1.0000
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 110) = 0.4919, p-val = 0.6887
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0745 0.0485 1.5352 110 0.1276 -0.0217 0.1707
## age 0.0131 0.0329 0.3991 110 0.6906 -0.0521 0.0783
## meth_iden 0.0389 0.0362 1.0743 110 0.2850 -0.0329 0.1107
## age:meth_iden -0.0151 0.0213 -0.7101 110 0.4792 -0.0574 0.0271
##
## ---
## 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 = 112) = 0.5342, p-val = 0.5840
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt 0.1409 0.0385 3.6614 112 0.2400 0.0647 0.2172
## comorb -0.0154 0.0211 -0.7309 112 0.5840 -0.0572 0.0264
##
## ---
## 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 = 106) = 3.8180, p-val = 0.0310
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt -7.6557 3.4527 -2.2173 106 0.0900 -14.5010 -0.8104 .
## year_pub 0.0037 0.0017 2.1886 106 0.0880 0.0004 0.0071 .
## stage -0.0050 0.0168 -0.2981 106 0.8100 -0.0383 0.0283
## comorb -0.0159 0.0205 -0.7761 106 0.5650 -0.0565 0.0247
## type_prev 0.0357 0.0150 2.3863 106 0.0580 0.0060 0.0654 .
## meth_iden 0.0264 0.0185 1.4251 106 0.2650 -0.0103 0.0631
## type_samp 0.0136 0.0089 1.5213 106 0.2390 -0.0041 0.0312
## quality 0.0490 0.0184 2.6575 106 0.0360 0.0125 0.0856 *
##
## ---
## 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 = 114; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1885)
## 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 = 110) = 0.4854, p-val = 1.0000
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 110) = 5.1400, p-val = 0.0023
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0258 0.0339 0.7588 110 0.4496 -0.0415 0.0930
## age 0.0339 0.0243 1.3963 110 0.1654 -0.0142 0.0821
## type_samp 0.0580 0.0167 3.4729 110 0.0007 0.0249 0.0911 ***
## age:type_samp -0.0231 0.0107 -2.1601 110 0.0329 -0.0443 -0.0019 *
##
## ---
## 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 = 112) = 0.5342, p-val = 0.5750
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt 0.1409 0.0385 3.6614 112 0.2480 0.0647 0.2172
## comorb -0.0154 0.0211 -0.7309 112 0.5750 -0.0572 0.0264
##
## ---
## 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 = 106) = 3.8180, p-val = 0.0350
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt -7.6557 3.4527 -2.2173 106 0.0950 -14.5010 -0.8104 .
## year_pub 0.0037 0.0017 2.1886 106 0.0980 0.0004 0.0071 .
## stage -0.0050 0.0168 -0.2981 106 0.8030 -0.0383 0.0283
## comorb -0.0159 0.0205 -0.7761 106 0.5500 -0.0565 0.0247
## type_prev 0.0357 0.0150 2.3863 106 0.0630 0.0060 0.0654 .
## meth_iden 0.0264 0.0185 1.4251 106 0.2450 -0.0103 0.0631
## type_samp 0.0136 0.0089 1.5213 106 0.2370 -0.0041 0.0312
## quality 0.0490 0.0184 2.6575 106 0.0420 0.0125 0.0856 *
##
## ---
## 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 = 114; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1477)
## 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 = 110) = 0.8521, p-val = 1.0000
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 110) = 0.0503, p-val = 0.9850
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0891 0.0704 1.2653 110 0.2085 -0.0504 0.2286
## meth_iden 0.0122 0.0656 0.1855 110 0.8532 -0.1179 0.1422
## type_samp 0.0034 0.0250 0.1357 110 0.8923 -0.0461 0.0529
## meth_iden:type_samp -0.0012 0.0206 -0.0606 110 0.9518 -0.0421 0.0396
##
## ---
## 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 = 112) = 0.5342, p-val = 0.5800
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt 0.1409 0.0385 3.6614 112 0.2200 0.0647 0.2172
## comorb -0.0154 0.0211 -0.7309 112 0.5800 -0.0572 0.0264
##
## ---
## 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 = 106) = 3.8180, p-val = 0.0330
##
## Model Results:
##
## estimate se tval df pval¹ ci.lb ci.ub
## intrcpt -7.6557 3.4527 -2.2173 106 0.0740 -14.5010 -0.8104 .
## year_pub 0.0037 0.0017 2.1886 106 0.0770 0.0004 0.0071 .
## stage -0.0050 0.0168 -0.2981 106 0.8030 -0.0383 0.0283
## comorb -0.0159 0.0205 -0.7761 106 0.5380 -0.0565 0.0247
## type_prev 0.0357 0.0150 2.3863 106 0.0700 0.0060 0.0654 .
## meth_iden 0.0264 0.0185 1.4251 106 0.2490 -0.0103 0.0631
## type_samp 0.0136 0.0089 1.5213 106 0.2260 -0.0041 0.0312
## quality 0.0490 0.0184 2.6575 106 0.0370 0.0125 0.0856 *
##
## ---
## 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 + -0.01838 0.01226 0.04699 0.02769 0.01863 7 -162.931 340.9
## 55 + 0.01313 0.05000 0.02649 0.01724 6 -164.083 341.0
## 119 + 0.02646 0.05122 0.03446 0.01240 0.003595 7 -162.957 341.0
## 40 + -0.01495 0.01416 0.04272 0.01981 6 -164.097 341.0
## 120 + -0.01718 0.02552 0.04840 0.03550 0.01376 0.003558 8 -161.815 341.0
## delta weight
## 56 0.00 0.205
## 55 0.03 0.201
## 119 0.05 0.199
## 40 0.06 0.198
## 120 0.08 0.196
## Models ranked by AICc(x)
##
##
## Multimodel Inference Coefficients
## --------------------------
##
##
## Estimate Std. Error z value Pr(>|z|)
## intrcpt -3.223649014 4.091822547 0.7878272 0.4307978
## comorb -0.009023724 0.017776636 0.5076171 0.6117219
## meth_iden 0.011384944 0.018147954 0.6273403 0.5304362
## quality 0.026939868 0.028619950 0.9412968 0.3465528
## type_prev 0.015347016 0.018793692 0.8166046 0.4141544
## type_samp 0.011587034 0.012462604 0.9297443 0.3525035
## year_pub 0.001593749 0.002030993 0.7847144 0.4326210
## stage 0.001697317 0.012543606 0.1353133 0.8923641
##
##
## Predictor Importance
## --------------------------
##
##
## model importance
## 1 meth_iden 0.5844712
## 2 type_samp 0.5606120
## 3 quality 0.5388151
## 4 type_prev 0.5122403
## 5 comorb 0.5082750
## 6 year_pub 0.4930939
## 7 stage 0.4868201
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
## 0.0847 0.0716 0.1000 0.0142 0.3734# 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 = -7.8078, p < .0001
## Limit Estimate (as sei -> 0): b = -1.6211 (CI: -1.8604, -1.3818)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