Metanalisis AGT
Ismael Calandri
5/6/2021
###data tidy####
data$`Recurrence as a main outcome`<-factor(data$`Recurrence as a main outcome`,
levels = c(0,1),
labels = c("No", "Yes"))
library(expss)##
## Attaching package: 'expss'## The following objects are masked from 'package:dplyr':
##
## between, compute, contains, first, last, na_if, recode, varsdata$recurrence_prevalence<-data$n_recurrency/data$n_total
data<-apply_labels(data,
recurrence_prevalence= "Prevalence of recurrency (%)",
n_recurrency = "Recurrency (n)",
n_total = "Total (n)")Prevalence metanalysis
Raw prevalence
data_prevalence<-data%>% select(n_recurrency,n_total,Study, TGA_definition, `Recurrence as a main outcome`)
data_prevalence<-na.omit(data_prevalence)
meta_prevalence<- metaprop(data_prevalence$n_recurrency, data_prevalence$n_total, studlab=data_prevalence$Study, sm="PFT", data=data_prevalence, method="Inverse", method.tau="DL")
summary(meta_prevalence)## Number of studies combined: k = 35
##
## proportion 95%-CI
## Fixed effect model 0.1081 [0.0988; 0.1177]
## Random effects model 0.1237 [0.0980; 0.1517]
##
## Quantifying heterogeneity:
## tau^2 = 0.0102 [0.0068; 0.0243]; tau = 0.1008 [0.0823; 0.1558]
## I^2 = 83.2% [77.5%; 87.5%]; H = 2.44 [2.11; 2.82]
##
## Test of heterogeneity:
## Q d.f. p-value
## 202.42 34 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Freeman-Tukey double arcsine transformationforest.meta(meta_prevalence,
comb.r=T,
comb.f=F,
prediction = T,
leftcols = c("Study","n_recurrency","n_total", "TGA_definition", "Recurrence as a main outcome"),
leftlabs = c("Author", "Events", "Total", "TGA criteria", "Recurrency outcome"),
xlab="Prevalence of recurrency")
## agregaria una columna de diagnostico de biasforest.meta(meta_prevalence,
comb.r=T,
comb.f=F,
prediction = T,
xlab="Prevalence of recurrence")
Outliers and influential analysis
a<-find.outliers(meta_prevalence)Estudios detectados como outliers (Random effects model)
a$out.study.random## [1] "Oliveira, 2020" "Romoli, 2020" "Eisele, 2019" "Han, 2019"
## [5] "Keret, 2016" "Moon, 2015" "Akkawi, 2005" "Fredericks, 1993"Modelo sin outliers (Random effects model)
a$m.random## proportion 95%-CI %W(fixed) %W(random)
## Lee DA, 2021 0.1250 [0.0641; 0.2127] 2.8 4.2
## Oliveira, 2020 0.2714 [0.1720; 0.3910] 0.0 0.0
## Morris, 2020 0.1370 [0.1167; 0.1593] 32.7 7.3
## Waliszewska-Prosol, 2020 0.0714 [0.0198; 0.1729] 1.8 3.3
## Tynas, 2020 0.1613 [0.0932; 0.2520] 2.9 4.3
## Romoli, 2020 0.0743 [0.0534; 0.1002] 0.0 0.0
## Eisele, 2019 0.0347 [0.0140; 0.0701] 0.0 0.0
## Han, 2019 0.4430 [0.3312; 0.5592] 0.0 0.0
## Alessandro, 2019 0.0788 [0.0457; 0.1248] 6.4 5.7
## Himeno, 2017 0.0667 [0.0337; 0.1162] 5.2 5.3
## Arena, 2017 0.1403 [0.0973; 0.1932] 6.9 5.8
## Keret, 2016 0.0260 [0.0071; 0.0652] 0.0 0.0
## Moon, 2015 0.3333 [0.1459; 0.5697] 0.0 0.0
## Kwon, 2014 0.1176 [0.0712; 0.1795] 4.8 5.2
## Buhr, 2012 0.1395 [0.0530; 0.2793] 1.4 2.8
## Uttner, 2012 0.0000 [0.0000; 0.1951] 0.5 1.4
## Auyeung, 2010 0.1852 [0.0630; 0.3808] 0.9 2.0
## Lee SY, 2009 0.0488 [0.0060; 0.1653] 1.3 2.7
## Agosti,2008 0.1385 [0.0842; 0.2100] 4.1 4.9
## Chung, 2007 0.1176 [0.0146; 0.3644] 0.5 1.4
## Quinette,2006 0.0634 [0.0294; 0.1169] 4.5 5.1
## Akkawi, 2005 0.0448 [0.0217; 0.0809] 0.0 0.0
## Toledo, 2005 0.2097 [0.1418; 0.2919] 3.9 4.8
## Pantoni, 2005 0.0784 [0.0218; 0.1888] 1.6 3.1
## Lampl, 2004 0.1875 [0.0405; 0.4565] 0.5 1.4
## Chen, 1999 0.1071 [0.0227; 0.2823] 0.9 2.1
## Pai, 1999 0.1200 [0.0255; 0.3122] 0.8 1.9
## Klotzsch, 1996 0.2264 [0.1228; 0.3621] 1.7 3.2
## Zorzon, 1995 0.0938 [0.0352; 0.1930] 2.0 3.5
## Fredericks, 1993 0.2632 [0.1554; 0.3966] 0.0 0.0
## Gallassi, 1993 0.2439 [0.1236; 0.4030] 1.3 2.7
## Melo,1992 0.0588 [0.0123; 0.1624] 1.6 3.1
## Gandolfo,1992 0.2157 [0.1404; 0.3081] 3.2 4.4
## Hodges, 1990 0.0789 [0.0367; 0.1446] 3.6 4.7
## Hinge, 1986 0.2162 [0.1289; 0.3272] 2.3 3.8
## exclude
## Lee DA, 2021
## Oliveira, 2020 *
## Morris, 2020
## Waliszewska-Prosol, 2020
## Tynas, 2020
## Romoli, 2020 *
## Eisele, 2019 *
## Han, 2019 *
## Alessandro, 2019
## Himeno, 2017
## Arena, 2017
## Keret, 2016 *
## Moon, 2015 *
## Kwon, 2014
## Buhr, 2012
## Uttner, 2012
## Auyeung, 2010
## Lee SY, 2009
## Agosti,2008
## Chung, 2007
## Quinette,2006
## Akkawi, 2005 *
## Toledo, 2005
## Pantoni, 2005
## Lampl, 2004
## Chen, 1999
## Pai, 1999
## Klotzsch, 1996
## Zorzon, 1995
## Fredericks, 1993 *
## Gallassi, 1993
## Melo,1992
## Gandolfo,1992
## Hodges, 1990
## Hinge, 1986
##
## Number of studies combined: k = 27
##
## proportion 95%-CI
## Fixed effect model 0.1207 [0.1091; 0.1327]
## Random effects model 0.1198 [0.0996; 0.1415]
##
## Quantifying heterogeneity:
## tau^2 = 0.0032 [0.0009; 0.0106]; tau = 0.0564 [0.0305; 0.1029]
## I^2 = 57.6% [34.9%; 72.3%]; H = 1.54 [1.24; 1.90]
##
## Test of heterogeneity:
## Q d.f. p-value
## 61.28 26 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Freeman-Tukey double arcsine transformation
## - Clopper-Pearson confidence interval for individual studiesInfluential analysis
inf.analysis <- InfluenceAnalysis(x =meta_prevalence,
random = TRUE)## [===========================================================================] DONEplot(inf.analysis, "influence")
Publication bias (Funnel Plot)
# Produce funnel plot
funnel.meta(meta_prevalence,cex = 0.8,
studlab = TRUE)
title("Funnel Plot (TGA recurrence prevalence)")
col.contour = c("gray75", "gray85", "gray95")
# Generate funnel plot (we do not include study labels here)
funnel.meta(meta_prevalence,studlab = TRUE, cex.studlab = 0.5,
contour = c(0.9, 0.95, 0.99),
col.contour = col.contour)
# Add a legend
legend(x = 0.6, y = 0.001, cex = 0.8,
legend = c("p < 0.1", "p < 0.05", "p < 0.01"),
fill = col.contour)
# Add a title
title("Contour-Enhanced Funnel Plot (TGA recurrence prevalence)")
Publication bias (Egger´s test)
eggers.test(meta_prevalence)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 1.128 -0.52 - 2.78 1.338 0.1900994
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.Prevalence Stratified
###separados por seguimiento
data2<-data
data2$FU<-cut(data2$Follow_up_time,
breaks=c(0, 24, 48, 100),
labels=c("< 2 years","2 to 4 years","> 4years"))
data2<-data2 %>% filter(FU != "")
meta<-metaprop(data2$n_recurrency, data2$n_total, studlab=data2$Study, sm="PFT", data=data2, method="Inverse", method.tau="DL")
meta2<-update.meta(meta,
byvar = FU,
tau.common = FALSE)
meta2## proportion 95%-CI %W(fixed) %W(random) FU
## Oliveira, 2020 0.2714 [0.1720; 0.3910] 6.4 7.5 < 2 years
## Alessandro, 2019 0.0788 [0.0457; 0.1248] 18.4 9.5 < 2 years
## Buhr, 2012 0.1395 [0.0530; 0.2793] 3.9 6.3 < 2 years
## Uttner, 2012 0.0000 [0.0000; 0.1951] 1.6 3.8 < 2 years
## Auyeung, 2010 0.1852 [0.0630; 0.3808] 2.5 5.0 2 to 4 years
## Agosti,2008 0.1385 [0.0842; 0.2100] 11.8 8.8 < 2 years
## Toledo, 2005 0.2097 [0.1418; 0.2919] 11.3 8.7 > 4years
## Pantoni, 2005 0.0784 [0.0218; 0.1888] 4.7 6.7 > 4years
## Chen, 1999 0.1071 [0.0227; 0.2823] 2.6 5.1 2 to 4 years
## Zorzon, 1995 0.0938 [0.0352; 0.1930] 5.8 7.3 2 to 4 years
## Melo,1992 0.0588 [0.0123; 0.1624] 4.7 6.7 2 to 4 years
## Gandolfo,1992 0.2157 [0.1404; 0.3081] 9.3 8.4 > 4years
## Hodges, 1990 0.0789 [0.0367; 0.1446] 10.4 8.6 2 to 4 years
## Hinge, 1986 0.2162 [0.1289; 0.3272] 6.7 7.6 > 4years
##
## Number of studies combined: k = 14
##
## proportion 95%-CI
## Fixed effect model 0.1297 [0.1097; 0.1509]
## Random effects model 0.1297 [0.0928; 0.1712]
##
## Quantifying heterogeneity:
## tau^2 = 0.0074 [0.0021; 0.0269]; tau = 0.0859 [0.0461; 0.1639]
## I^2 = 69.3% [46.8%; 82.3%]; H = 1.81 [1.37; 2.38]
##
## Test of heterogeneity:
## Q d.f. p-value
## 42.39 13 < 0.0001
##
## Results for subgroups (fixed effect model):
## k proportion 95%-CI Q I^2
## FU = < 2 years 5 0.1154 [0.0865; 0.1474] 19.13 79.1%
## FU = 2 to 4 years 5 0.0870 [0.0551; 0.1245] 3.30 0.0%
## FU = > 4years 4 0.1904 [0.1504; 0.2338] 6.07 50.6%
##
## Test for subgroup differences (fixed effect model):
## Q d.f. p-value
## Between groups 13.90 2 0.0010
## Within groups 28.49 11 0.0027
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau
## FU = < 2 years 5 0.1195 [0.0542; 0.2036] 0.0117 0.1080
## FU = 2 to 4 years 5 0.0870 [0.0551; 0.1245] 0 0
## FU = > 4years 4 0.1841 [0.1280; 0.2475] 0.0030 0.0547
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 7.51 2 0.0233
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Freeman-Tukey double arcsine transformation
## - Clopper-Pearson confidence interval for individual studiesforest(meta2,
comb.r=T,
comb.f=F,
prediction = T,
xlab="Prevalence of recurrence" )
Metaregresion
data_prevalence2<-data%>% select(n_recurrency,n_total,Study, TGA_definition, `Recurrence as a main outcome`,Follow_up_time)
data_prevalence2<-na.omit(data_prevalence2)
meta_prevalence2<- metaprop(data_prevalence2$n_recurrency, data_prevalence2$n_total, studlab=data_prevalence2$Study, sm="PFT", data=data_prevalence2, method="Inverse", method.tau="DL")
meta_reg<-metareg(meta_prevalence2, ~Follow_up_time)
meta_reg##
## Mixed-Effects Model (k = 16; tau^2 estimator: DL)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0051 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0713
## I^2 (residual heterogeneity / unaccounted variability): 66.84%
## H^2 (unaccounted variability / sampling variability): 3.02
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 14) = 42.2142, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0891, p-val = 0.7653
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3739 0.0315 11.8840 <.0001 0.3122 0.4355 ***
## Follow_up_time 0.0001 0.0002 0.2985 0.7653 -0.0004 0.0006
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1bubble(meta_reg, studlab = T, cex.studlab = .5,
xlab = "Follow-up time (days)", xlim = c(0, 400),col = c("#E7B800"),bg = c("#fada96"),pos.studlab = 4,col.line = c("#b57e09"),
ylab = "Freeman-Tukey transformation proportion")
data_prevalence3<-data%>% select(n_recurrency,n_total,Study, TGA_definition, `Recurrence as a main outcome`,Year)
data_prevalence3<-na.omit(data_prevalence3)
meta_prevalence3<- metaprop(data_prevalence3$n_recurrency, data_prevalence3$n_total, studlab=data_prevalence3$Study, sm="PFT", data=data_prevalence3, method="Inverse", method.tau="DL")
meta_reg2<-metareg(meta_prevalence3, ~Year)
meta_reg2##
## Mixed-Effects Model (k = 35; tau^2 estimator: DL)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0105 (SE = 0.0044)
## tau (square root of estimated tau^2 value): 0.1025
## I^2 (residual heterogeneity / unaccounted variability): 83.28%
## H^2 (unaccounted variability / sampling variability): 5.98
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 197.3978, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9801, p-val = 0.3222
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 4.0248 3.6918 1.0902 0.2756 -3.2110 11.2607
## Year -0.0018 0.0018 -0.9900 0.3222 -0.0054 0.0018
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1bubble(meta_reg2, studlab = T, cex.studlab = .8)
data_prevalence4<-data%>% select(n_recurrency,n_total,Study, TGA_definition, `Recurrence as a main outcome`, TGA_definition)
data_prevalence4<-na.omit(data_prevalence4)
meta_prevalence4<- metaprop(data_prevalence4$n_recurrency, data_prevalence4$n_total, studlab=data_prevalence4$Study, sm="PFT", data=data_prevalence4, method="Inverse", method.tau="DL")
meta_reg3<-metareg(meta_prevalence4, ~TGA_definition)
meta_reg3##
## Mixed-Effects Model (k = 35; tau^2 estimator: DL)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0108 (SE = 0.0046)
## tau (square root of estimated tau^2 value): 0.1041
## I^2 (residual heterogeneity / unaccounted variability): 83.70%
## H^2 (unaccounted variability / sampling variability): 6.13
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 202.4243, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3075, p-val = 0.5792
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.3544 0.0348 10.1830 <.0001
## TGA_definitionHodges y Warlow (1991) 0.0239 0.0431 0.5545 0.5792
## ci.lb ci.ub
## intrcpt 0.2862 0.4226 ***
## TGA_definitionHodges y Warlow (1991) -0.0605 0.1083
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Risk factors
Female sex
m.sex <- metabin(data$`sex_fem_eventos en exp`,
data$`sex_fem_numero de expuestos`,
data$`sex_fem_eventos en no exp`,
data$sex_fem_n_no_exp,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.sex## OR 95%-CI %W(random)
## Lee DA, 2021 0.3931 [0.1077; 1.4352] 8.7
## Oliveira, 2020 5.0469 [1.0486; 24.2897] 6.7
## Morris, 2020 1.0252 [0.7193; 1.4612] 21.9
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 1.3333 [0.4404; 4.0368] 10.5
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 0.9278 [0.3342; 2.5759] 11.5
## Himeno, 2017 1.4400 [0.3668; 5.6531] 8.1
## Arena, 2017 0.7896 [0.3684; 1.6924] 15.1
## Keret, 2016 NA 0.0
## Moon, 2015 1.0000 [0.0748; 13.3670] 2.9
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 0.3750 [0.0272; 5.1688] 2.9
## Quinette,2006 NA 0.0
## Agosti, 2006 0.4359 [0.1206; 1.5761] 8.8
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 1.2500 [0.0885; 17.6531] 2.8
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 11
##
## OR 95%-CI t p-value
## Random effects model 0.9536 [0.6224; 1.4609] -0.25 0.8089
## Prediction interval [0.2936; 3.0966]
##
## Quantifying heterogeneity:
## tau^2 = 0.2344 [0.0000; 0.9965]; tau = 0.4842 [0.0000; 0.9982]
## I^2 = 0.0% [0.0%; 56.5%]; H = 1.00 [1.00; 1.52]
##
## Test of heterogeneity:
## Q d.f. p-value
## 9.15 10 0.5183
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects modelforest(m.sex,
xlab="OR",
lab.e = "Female",
lab.c = "Male")
CV risk factors
Prevalence of risk factors in the cohort
HBP
prevalencia<-read_sheet("https://docs.google.com/spreadsheets/d/1XRxMJi8JoNFNbDQuJeO9bnHdXum4w8IwtwT-dPmZxTU/edit?usp=sharing")## Reading from "prevalencias"## Range "Hoja 1"prev_hta<-prevalencia %>% select(HTA, HTA_total, Study)
prev_hta<-na.omit(prev_hta)
metaprop(prev_hta$HTA, prev_hta$HTA_total, studlab=prev_hta$Study, sm="PFT", data=prev_hta, method="Inverse", method.tau="DL")## proportion 95%-CI %W(fixed) %W(random)
## Tynas, 2020 0.5161 [0.4101; 0.6211] 10.6 10.9
## Oliveira, 2020 0.5000 [0.3780; 0.6220] 8.0 8.3
## Alessandro, 2019 0.5074 [0.4365; 0.5781] 23.2 22.8
## Himeno, 2017 0.4970 [0.4183; 0.5757] 18.8 18.8
## Arena, 2017 0.4163 [0.3506; 0.4843] 25.2 24.7
## Moon, 2016 0.4762 [0.2571; 0.7022] 2.4 2.6
## Lampl, 2004 0.5000 [0.2465; 0.7535] 1.9 2.0
## Agosti, 2006 0.5647 [0.4528; 0.6720] 9.7 10.0
##
## Number of studies combined: k = 8
##
## proportion 95%-CI
## Fixed effect model 0.4872 [0.4535; 0.5209]
## Random effects model 0.4877 [0.4532; 0.5224]
##
## Quantifying heterogeneity:
## tau^2 < 0.0001 [0.0000; 0.0056]; tau = 0.0093 [0.0000; 0.0748]
## I^2 = 3.4% [0.0%; 68.7%]; H = 1.02 [1.00; 1.79]
##
## Test of heterogeneity:
## Q d.f. p-value
## 7.25 7 0.4033
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Freeman-Tukey double arcsine transformation
## - Clopper-Pearson confidence interval for individual studiesDLP
prev_dlp<-prevalencia %>% select(DLP, DLP_total, Study)
prev_dlp<-na.omit(prev_dlp)
metaprop(DLP, DLP_total, studlab=Study, sm="PFT", data=prev_dlp, method="Inverse", method.tau="DL")## proportion 95%-CI %W(fixed) %W(random)
## Tynas, 2020 0.4624 [0.3584; 0.5688] 11.6 15.2
## Alessandro, 2019 0.5320 [0.4609; 0.6022] 25.2 16.2
## Himeno, 2017 0.3758 [0.3017; 0.4544] 20.5 16.0
## Arena, 2017 0.2670 [0.2099; 0.3304] 27.4 16.3
## Moon, 2016 0.1429 [0.0305; 0.3634] 2.7 11.1
## Lampl, 2004 0.1250 [0.0155; 0.3835] 2.0 10.0
## Agosti, 2006 0.1765 [0.1023; 0.2743] 10.6 15.1
##
## Number of studies combined: k = 7
##
## proportion 95%-CI
## Fixed effect model 0.3562 [0.3228; 0.3903]
## Random effects model 0.3074 [0.2016; 0.4240]
##
## Quantifying heterogeneity:
## tau^2 = 0.0214 [0.0076; 0.1346]; tau = 0.1463 [0.0871; 0.3668]
## I^2 = 90.1% [82.3%; 94.5%]; H = 3.19 [2.37; 4.28]
##
## Test of heterogeneity:
## Q d.f. p-value
## 60.89 6 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Freeman-Tukey double arcsine transformation
## - Clopper-Pearson confidence interval for individual studiesDBT
prev_dbt<-prevalencia %>% select(DBT, DBT_toal, Study)
prev_dbt<-na.omit(prev_dbt)
metaprop(DBT, DBT_toal, studlab=Study, sm="PFT", data=prev_dbt, method="Inverse", method.tau="DL")## proportion 95%-CI %W(fixed) %W(random)
## Tynas, 2020 0.0645 [0.0240; 0.1352] 12.1 15.7
## Oliveira, 2020 0.1714 [0.0918; 0.2803] 9.1 13.3
## Alessandro, 2019 0.0640 [0.0345; 0.1070] 26.4 22.6
## Himeno, 2017 0.0606 [0.0294; 0.1086] 21.5 20.8
## Arena, 2017 0.0452 [0.0219; 0.0816] 28.7 23.3
## Lampl, 2004 0.0625 [0.0016; 0.3023] 2.1 4.3
##
## Number of studies combined: k = 6
##
## proportion 95%-CI
## Fixed effect model 0.0611 [0.0442; 0.0802]
## Random effects model 0.0665 [0.0408; 0.0973]
##
## Quantifying heterogeneity:
## tau^2 = 0.0021 [0.0000; 0.0292]; tau = 0.0455 [0.0000; 0.1708]
## I^2 = 49.9% [0.0%; 80.1%]; H = 1.41 [1.00; 2.24]
##
## Test of heterogeneity:
## Q d.f. p-value
## 9.98 5 0.0758
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Freeman-Tukey double arcsine transformation
## - Clopper-Pearson confidence interval for individual studiesSmoking
#smk
prev_smk<-prevalencia %>% select(TBq, TBQ_toal, Study)
prev_smk<-na.omit(prev_smk)
metaprop(TBq, TBQ_toal, studlab=Study, sm="PFT", data=prev_smk, method="Inverse", method.tau="DL")## proportion 95%-CI %W(fixed) %W(random)
## Tynas, 2020 0.3548 [0.2583; 0.4609] 12.4 19.4
## Oliveira, 2020 0.1714 [0.0918; 0.2803] 9.3 18.6
## Alessandro, 2019 0.3645 [0.2983; 0.4348] 27.0 20.7
## Himeno, 2017 0.1030 [0.0612; 0.1598] 21.9 20.4
## Arena, 2017 0.1765 [0.1286; 0.2332] 29.4 20.8
##
## Number of studies combined: k = 5
##
## proportion 95%-CI
## Fixed effect model 0.2235 [0.1942; 0.2543]
## Random effects model 0.2254 [0.1271; 0.3418]
##
## Quantifying heterogeneity:
## tau^2 = 0.0195 [0.0055; 0.1701]; tau = 0.1396 [0.0740; 0.4124]
## I^2 = 91.9% [84.0%; 95.9%]; H = 3.51 [2.50; 4.92]
##
## Test of heterogeneity:
## Q d.f. p-value
## 49.22 4 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Freeman-Tukey double arcsine transformation
## - Clopper-Pearson confidence interval for individual studiesAF
#fa
prev_fa<-prevalencia %>% select(FA, FA_toal, Study)
prev_fa<-na.omit(prev_fa)
metaprop(FA, FA_toal, studlab=Study, sm="PFT", data=prev_fa, method="Inverse", method.tau="DL")## proportion 95%-CI %W(fixed) %W(random)
## Tynas, 2020 0.0430 [0.0118; 0.1065] 18.0 18.0
## Alessandro, 2019 0.0394 [0.0172; 0.0762] 39.2 39.2
## Arena, 2017 0.0452 [0.0219; 0.0816] 42.7 42.7
##
## Number of studies combined: k = 3
##
## proportion 95%-CI
## Fixed effect model 0.0421 [0.0259; 0.0617]
## Random effects model 0.0421 [0.0259; 0.0617]
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.0012]; tau = 0 [0.0000; 0.0346]
## I^2 = 0.0%; H = 1.00
##
## Test of heterogeneity:
## Q d.f. p-value
## 0.10 2 0.9525
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Freeman-Tukey double arcsine transformation
## - Clopper-Pearson confidence interval for individual studiesAssociation w/recurrence
Hypertension
m.hta <- metabin(data$`HTA_eventos en exp`,
data$`HTA_numero de expuestos`,
data$`HTA_eventos en noexp`,
data$`HTA_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.hta## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 2.0870 [0.7065; 6.1645] 15.0
## Morris, 2020 NA 0.0
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 0.7896 [0.2609; 2.3900] 14.8
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 1.6846 [0.5884; 4.8233] 15.2
## Himeno, 2017 0.3560 [0.0910; 1.3927] 12.9
## Arena, 2017 1.0148 [0.4701; 2.1906] 17.4
## Keret, 2016 NA 0.0
## Moon, 2015 1.7778 [0.2842; 11.1200] 9.8
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 0.3295 [0.0908; 1.1954] 13.4
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 49.2353 [0.0823; 29450.1833] 1.4
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 8
##
## OR 95%-CI t p-value
## Random effects model 0.9875 [0.4721; 2.0658] -0.04 0.9691
## Prediction interval [0.1000; 9.7522]
##
## Quantifying heterogeneity:
## tau^2 = 0.7785 [0.0000; 4.5219]; tau = 0.8823 [0.0000; 2.1265]
## I^2 = 28.2% [0.0%; 67.8%]; H = 1.18 [1.00; 1.76]
##
## Test of heterogeneity:
## Q d.f. p-value
## 9.75 7 0.2032
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.hta,
xlab="OR",
lab.e = "HBP",
lab.c = "No HBP")
Dyslipidemia
m.dlp <- metabin(data$`DLP_eventos en exp`,
data$`DLP_numero de expuestos`,
data$`DLP_eventos en noexp`,
data$`DLP_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.dlp ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 NA 0.0
## Morris, 2020 NA 0.0
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 1.4041 [0.4636; 4.2527] 19.2
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 1.5136 [0.5286; 4.3341] 21.0
## Himeno, 2017 0.9458 [0.2654; 3.3712] 15.0
## Arena, 2017 1.1455 [0.4945; 2.6535] 30.7
## Keret, 2016 NA 0.0
## Moon, 2015 1.0000 [0.0748; 13.3670] 3.9
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 0.9231 [0.1804; 4.7221] 9.5
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 0.1705 [0.0003; 109.7893] 0.6
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 7
##
## OR 95%-CI t p-value
## Random effects model 1.1815 [0.9304; 1.5004] 1.71 0.1384
## Prediction interval [0.6510; 2.1445]
##
## Quantifying heterogeneity:
## tau^2 = 0.0442; tau = 0.2103; I^2 = 0.0%; H = 1.00
##
## Test of heterogeneity:
## Q d.f. p-value
## 0.88 6 0.9899
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.dlp,
xlab="OR",
lab.e = "DLP",
lab.c = "No DLP")
Smoking
m.tbq <- metabin(data$`TBQ_eventos en exp`,
data$`TBQ_numero de expuestos`,
data$`TBQ_eventos en noexp`,
data$`TBQ_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.tbq ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 1.4333 [0.3767; 5.4543] 16.7
## Morris, 2020 NA 0.0
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 0.8929 [0.2774; 2.8738] 21.8
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 1.0500 [0.3656; 3.0158] 26.7
## Himeno, 2017 0.8625 [0.1035; 7.1841] 6.7
## Arena, 2017 0.8824 [0.3161; 2.4628] 28.2
## Keret, 2016 NA 0.0
## Moon, 2015 NA 0.0
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 NA 0.0
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 5
##
## OR 95%-CI t p-value
## Random effects model 1.0033 [0.7860; 1.2808] 0.04 0.9715
## Prediction interval [0.7178; 1.4026]
##
## Quantifying heterogeneity:
## tau^2 = 0.0033; tau = 0.0578; I^2 = 0.0%; H = 1.00
##
## Test of heterogeneity:
## Q d.f. p-value
## 0.40 4 0.9826
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Hartung-Knapp adjustment for random effects modelforest(m.tbq,
xlab="OR",
lab.e = "Smoking",
lab.c = "No")
Diabetes
m.dbt <- metabin(data$`DBT_eventos en exp`,
data$`DBT_numero de expuestos`,
data$`DBT_eventos en noexp`,
data$`DBT_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.dbt ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 0.4824 [0.0955; 2.4375] 29.2
## Morris, 2020 NA 0.0
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 1.0429 [0.1131; 9.6200] 17.7
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 0.0825 [0.0002; 42.4399] 2.6
## Himeno, 2017 1.6111 [0.1852; 14.0133] 18.5
## Arena, 2017 1.5690 [0.3173; 7.7577] 29.7
## Keret, 2016 NA 0.0
## Moon, 2015 NA 0.0
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 NA 0.0
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 0.3548 [0.0005; 258.9388] 2.3
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 6
##
## OR 95%-CI t p-value
## Random effects model 0.9304 [0.4342; 1.9936] -0.24 0.8174
## Prediction interval [0.1879; 4.6064]
##
## Quantifying heterogeneity:
## tau^2 = 0.2440 [0.0000; 2.4619]; tau = 0.4940 [0.0000; 1.5690]
## I^2 = 0.0% [0.0%; 35.3%]; H = 1.00 [1.00; 1.24]
##
## Test of heterogeneity:
## Q d.f. p-value
## 1.96 5 0.8546
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.dbt,
xlab="OR",
lab.e = "DBT",
lab.c = "No")
Stroke
m.acv <- metabin(data$`ACV_eventos en exp`,
data$`ACV_numero de expuestos`,
data$`ACV_eventos en noexp`,
data$`ACV_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.acv ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 1.3824 [0.2318; 8.2442] 29.8
## Morris, 2020 NA 0.0
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 1.3750 [0.3368; 5.6136] 38.0
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 0.8923 [0.1091; 7.2956] 24.5
## Himeno, 2017 NA 0.0
## Arena, 2017 0.0348 [0.0001; 17.6350] 4.1
## Keret, 2016 NA 0.0
## Moon, 2015 0.1677 [0.0002; 115.8494] 3.7
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 NA 0.0
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 5
##
## OR 95%-CI t p-value
## Random effects model 0.9874 [0.3229; 3.0194] -0.03 0.9764
## Prediction interval [0.0581; 16.7715]
##
## Quantifying heterogeneity:
## tau^2 = 0.6300 [0.0000; 14.7908]; tau = 0.7937 [0.0000; 3.8459]
## I^2 = 0.0% [0.0%; 51.0%]; H = 1.00 [1.00; 1.43]
##
## Test of heterogeneity:
## Q d.f. p-value
## 1.70 4 0.7913
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.acv,
xlab="OR",
lab.e = "Stroke",
lab.c = "No")
Coronary artery disease
m.coro <- metabin(data$`coro_eventos en exp`,
data$`coro_numero de expuestos`,
data$`coro_eventos en noexp`,
data$`coro_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.coro ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 NA 0.0
## Morris, 2020 NA 0.0
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 1.3462 [0.2563; 7.0706] 28.6
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 0.7644 [0.0944; 6.1921] 20.8
## Himeno, 2017 NA 0.0
## Arena, 2017 0.6000 [0.0741; 4.8590] 20.9
## Keret, 2016 NA 0.0
## Moon, 2015 0.1677 [0.0002; 115.8494] 2.8
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 3.2273 [0.2694; 38.6553] 16.1
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 6.0000 [0.2571; 140.0446] 10.8
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 6
##
## OR 95%-CI t p-value
## Random effects model 1.2896 [0.4970; 3.3467] 0.69 0.5234
## Prediction interval [0.1614; 10.3060]
##
## Quantifying heterogeneity:
## tau^2 = 0.4227 [0.0000; 5.0601]; tau = 0.6502 [0.0000; 2.2495]
## I^2 = 0.0% [0.0%; 50.6%]; H = 1.00 [1.00; 1.42]
##
## Test of heterogeneity:
## Q d.f. p-value
## 2.57 5 0.7662
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.coro,
xlab="OR",
lab.e = "CAD",
lab.c = "No")
Atrial fibrillation
m.fa <- metabin(data$`FA_eventos en exp`,
data$`FA_numero de expuestos`,
data$`FA_eventos en noexp`,
data$`FA_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.fa ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 NA 0.0
## Morris, 2020 NA 0.0
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 5.8462 [0.7554; 45.2464] 61.9
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 0.1373 [0.0003; 71.6350] 19.0
## Himeno, 2017 NA 0.0
## Arena, 2017 0.0573 [0.0001; 29.4107] 19.1
## Keret, 2016 NA 0.0
## Moon, 2015 NA 0.0
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 NA 0.0
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 3
##
## OR 95%-CI t p-value
## Random effects model 1.1847 [0.0023; 609.6083] 0.12 0.9177
## Prediction interval [0.0000; 2992499958917.7930]
##
## Quantifying heterogeneity:
## tau^2 = 2.9459 [0.0000; >100.0000]; tau = 1.7164 [0.0000; >10.0000]
## I^2 = 30.6% [0.0%; 92.8%]; H = 1.20 [1.00; 3.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 2.88 2 0.2368
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.fa,
xlab="OR",
lab.e = "AF",
lab.c = "No AF")
m.mig <- metabin(data$`Mig_eventos en exp`,
data$`Mig_numero de expuestos`,
data$`Mig_eventos en noexp`,
data$`Mig_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.mig## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 2.6786 [0.7086; 10.1255] 10.1
## Morris, 2020 2.2889 [1.5687; 3.3398] 32.4
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 1.1264 [0.3637; 3.4887] 12.7
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 3.8880 [1.2991; 11.6365] 13.2
## Himeno, 2017 NA 0.0
## Arena, 2017 1.2917 [0.5156; 3.2358] 16.6
## Keret, 2016 NA 0.0
## Moon, 2015 1.0000 [0.0748; 13.3670] 3.2
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 NA 0.0
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 0.3548 [0.0005; 258.9388] 0.5
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 3.2667 [0.9484; 11.2520] 11.2
##
## Number of studies combined: k = 8
##
## OR 95%-CI t p-value
## Random effects model 2.0795 [1.3892; 3.1128] 4.29 0.0036
## Prediction interval [0.7314; 5.9126]
##
## Quantifying heterogeneity:
## tau^2 = 0.1533 [0.0000; 0.7264]; tau = 0.3915 [0.0000; 0.8523]
## I^2 = 0.0% [0.0%; 53.0%]; H = 1.00 [1.00; 1.46]
##
## Test of heterogeneity:
## Q d.f. p-value
## 4.83 7 0.6806
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.mig,
xlab="OR",
lab.e = "Migraine",
lab.c = "No migraine")
Prevalence of migraine
#prevalence of migraine
prevalencia<-read_sheet("https://docs.google.com/spreadsheets/d/1XRxMJi8JoNFNbDQuJeO9bnHdXum4w8IwtwT-dPmZxTU/edit?usp=sharing")## Reading from "prevalencias"## Range "Hoja 1"prev_mig<-prevalencia %>% select(Migraine, MIgraine_toal, Study)
prev_mig<-na.omit(prev_mig)
metaprop(prev_mig$Migraine, prev_mig$MIgraine_toal, studlab=prev_mig$Study, sm="PFT", data=prev_mig, method="Inverse", method.tau="DL")## proportion 95%-CI %W(fixed) %W(random)
## Tynas, 2020 0.3763 [0.2779; 0.4828] 5.4 12.8
## Morris, 2020 0.2222 [0.1973; 0.2487] 59.8 21.0
## Oliveira, 2020 0.1571 [0.0811; 0.2638] 4.0 11.3
## Alessandro, 2019 0.1527 [0.1062; 0.2097] 11.7 16.7
## Arena, 2017 0.1900 [0.1405; 0.2481] 12.7 17.1
## Moon, 2016 0.1429 [0.0305; 0.3634] 1.2 5.3
## Lampl, 2004 0.0625 [0.0016; 0.3023] 0.9 4.3
## Hinge, 1986 0.2027 [0.1181; 0.3122] 4.3 11.6
##
## Number of studies combined: k = 8
##
## proportion 95%-CI
## Fixed effect model 0.2064 [0.1872; 0.2262]
## Random effects model 0.1981 [0.1544; 0.2454]
##
## Quantifying heterogeneity:
## tau^2 = 0.0036 [0.0003; 0.0318]; tau = 0.0598 [0.0174; 0.1783]
## I^2 = 68.3% [33.6%; 84.9%]; H = 1.78 [1.23; 2.57]
##
## Test of heterogeneity:
## Q d.f. p-value
## 22.12 7 0.0024
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Freeman-Tukey double arcsine transformation
## - Clopper-Pearson confidence interval for individual studiesm.dep <- metabin(data$`Depresion_eventos en exp`,
data$`Depresion_numero de expuestos`,
data$`Depresion_eventos en noexp`,
data$`Depresion_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.dep ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 4.2000 [1.3260; 13.3034] 51.4
## Morris, 2020 NA 0.0
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 4.8125 [1.4695; 15.7605] 48.6
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 NA 0.0
## Himeno, 2017 NA 0.0
## Arena, 2017 NA 0.0
## Keret, 2016 NA 0.0
## Moon, 2015 NA 0.0
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 NA 0.0
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 2
##
## OR 95%-CI t p-value
## Random effects model 4.4871 [1.8902; 10.6517] 22.06 0.0288
##
## Quantifying heterogeneity:
## tau^2 = 0.0001; tau = 0.0109; I^2 = 0.0%; H = 1.00
##
## Test of heterogeneity:
## Q d.f. p-value
## 0.03 1 0.8719
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Hartung-Knapp adjustment for random effects modelforest(m.dep,
xlab="OR",
lab.e = "Depression",
lab.c = "No")
Prevalence of drepression
prev_dep<-prevalencia %>% select(Depresion, Depresion_total, Study)
prev_dep<-na.omit(prev_dep)
metaprop(prev_dep$Depresion, prev_dep$Depresion_total, studlab=prev_dep$Study, sm="PFT", data=prev_dep, method="Inverse", method.tau="DL")## proportion 95%-CI %W(fixed) %W(random)
## Tynas, 2020 0.2043 [0.1277; 0.3005] 57.0 57.0
## Oliveira, 2020 0.2571 [0.1601; 0.3756] 43.0 43.0
##
## Number of studies combined: k = 2
##
## proportion 95%-CI
## Fixed effect model 0.2264 [0.1647; 0.2945]
## Random effects model 0.2264 [0.1647; 0.2945]
##
## Quantifying heterogeneity:
## tau^2 = 0; tau = 0; I^2 = 0.0%; H = 1.00
##
## Test of heterogeneity:
## Q d.f. p-value
## 0.63 1 0.4257
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Freeman-Tukey double arcsine transformation
## - Clopper-Pearson confidence interval for individual studiesm.tig <- metabin(data$`gatillante_eventos en exp`,
data$`gatillante_numero de expuestos`,
data$`gatillante_eventos en noexp`,
data$`gatillante_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.tig ## OR 95%-CI %W(random)
## Lee DA, 2021 1.6111 [0.3954; 6.5641] 14.3
## Oliveira, 2020 1.0390 [0.3571; 3.0226] 16.9
## Morris, 2020 1.3327 [0.9180; 1.9347] 21.7
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 NA 0.0
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 1.2864 [0.4290; 3.8572] 16.7
## Himeno, 2017 NA 0.0
## Arena, 2017 0.8760 [0.3692; 2.0785] 18.5
## Keret, 2016 NA 0.0
## Moon, 2015 0.3000 [0.0277; 3.2499] 8.5
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 31.3279 [0.0507; 19354.5775] 1.7
## Quinette,2006 NA 0.0
## Agosti, 2006 41.8606 [0.0813; 21557.6477] 1.8
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 8
##
## OR 95%-CI t p-value
## Random effects model 1.1964 [0.5948; 2.4064] 0.61 0.5632
## Prediction interval [0.1070; 13.3825]
##
## Quantifying heterogeneity:
## tau^2 = 0.8865 [0.0000; 5.7724]; tau = 0.9415 [0.0000; 2.4026]
## I^2 = 0.0% [0.0%; 50.2%]; H = 1.00 [1.00; 1.42]
##
## Test of heterogeneity:
## Q d.f. p-value
## 4.56 7 0.7134
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.tig,
xlab="OR",
lab.e = "Triggers",
lab.c = "No")
Triggers prevalence
prev_tig<-prevalencia %>% select(Trigger, Trigger_total, Study)
prev_tig<-na.omit(prev_tig)
metaprop(prev_tig$Trigger, prev_tig$Trigger_total, studlab=prev_tig$Study, sm="PFT", data=prev_tig, method="Inverse", method.tau="DL")## proportion 95%-CI %W(fixed) %W(random)
## Lee, 2021 0.6364 [0.5269; 0.7363] 5.1 14.4
## Morris, 2020 0.2960 [0.2684; 0.3247] 60.0 15.2
## Oliveira, 2020 0.4143 [0.2977; 0.5383] 4.1 14.1
## Alessandro, 2019 0.6355 [0.5652; 0.7017] 11.7 14.9
## Arena, 2017 0.2805 [0.2224; 0.3447] 12.7 14.9
## Moon, 2016 0.2857 [0.1128; 0.5218] 1.2 12.0
## Agosti, 2006 0.7778 [0.6779; 0.8587] 5.2 14.4
##
## Number of studies combined: k = 7
##
## proportion 95%-CI
## Fixed effect model 0.3768 [0.3539; 0.4000]
## Random effects model 0.4784 [0.3226; 0.6363]
##
## Quantifying heterogeneity:
## tau^2 = 0.0421 [0.0152; 0.2367]; tau = 0.2052 [0.1234; 0.4865]
## I^2 = 96.7% [95.0%; 97.9%]; H = 5.52 [4.47; 6.82]
##
## Test of heterogeneity:
## Q d.f. p-value
## 182.74 6 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Freeman-Tukey double arcsine transformation
## - Clopper-Pearson confidence interval for individual studiesm.estres <- metabin(data$`estres_eventos en exp`,
data$`estres_numero de expuestos`,
data$`estres_eventos en noexp`,
data$`estres_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.estres ## OR 95%-CI %W(random)
## Lee DA, 2021 1.0000 [0.2689; 3.7184] 17.4
## Oliveira, 2020 NA 0.0
## Morris, 2020 0.9046 [0.4683; 1.7475] 39.8
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 NA 0.0
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 2.1366 [0.7330; 6.2275] 23.2
## Himeno, 2017 NA 0.0
## Arena, 2017 0.1941 [0.0004; 106.5910] 1.0
## Keret, 2016 NA 0.0
## Moon, 2015 NA 0.0
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 1.6558 [0.4736; 5.7899] 18.6
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 5
##
## OR 95%-CI t p-value
## Random effects model 1.2392 [0.7040; 2.1812] 1.05 0.3517
## Prediction interval [0.3107; 4.9423]
##
## Quantifying heterogeneity:
## tau^2 = 0.1475 [0.0000; 2.1973]; tau = 0.3840 [0.0000; 1.4823]
## I^2 = 0.0% [0.0%; 66.3%]; H = 1.00 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 2.47 4 0.6508
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.estres,
xlab="OR",
lab.e = "Stress",
lab.c = "No stress")
m.exe <- metabin(data$`exerc_eventos en exp`,
data$`exerc_numero de expuestos`,
data$`exerc_eventos en noexp`,
data$`exerc_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.exe ## OR 95%-CI %W(random)
## Lee DA, 2021 1.4889 [0.2803; 7.9096] 23.8
## Oliveira, 2020 NA 0.0
## Morris, 2020 1.3138 [0.7653; 2.2554] 37.2
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 NA 0.0
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 0.0708 [0.0001; 36.2834] 3.8
## Himeno, 2017 NA 0.0
## Arena, 2017 0.3625 [0.0463; 2.8359] 19.7
## Keret, 2016 NA 0.0
## Moon, 2015 0.0812 [0.0001; 49.3877] 3.6
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 6.5000 [0.2796; 151.1228] 11.8
## Quinette,2006 NA 0.0
## Agosti, 2006 NA 0.0
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 6
##
## OR 95%-CI t p-value
## Random effects model 1.0237 [0.2939; 3.5655] 0.05 0.9635
## Prediction interval [0.0425; 24.6511]
##
## Quantifying heterogeneity:
## tau^2 = 1.0773 [0.0000; 13.2823]; tau = 1.0380 [0.0000; 3.6445]
## I^2 = 0.0% [0.0%; 68.5%]; H = 1.00 [1.00; 1.78]
##
## Test of heterogeneity:
## Q d.f. p-value
## 4.03 5 0.5448
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.exe,
xlab="Trigger: physical exercise")
m.show <- metabin(data$`shower_eventos en exp`,
data$`shower_numero de expuestos`,
data$`shower_eventos en noexp`,
data$`shower_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.show ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 NA 0.0
## Morris, 2020 1.7666 [0.8269; 3.7744] 69.5
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 NA 0.0
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 0.5475 [0.0009; 317.9098] 3.2
## Himeno, 2017 NA 0.0
## Arena, 2017 0.0970 [0.0002; 50.7792] 3.3
## Keret, 2016 NA 0.0
## Moon, 2015 0.6111 [0.0516; 7.2402] 17.8
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 0.2971 [0.0004; 199.3570] 3.0
## Quinette,2006 NA 0.0
## Agosti, 2006 0.0769 [0.0001; 40.7210] 3.3
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 6
##
## OR 95%-CI t p-value
## Random effects model 1.0972 [0.4173; 2.8850] 0.25 0.8149
## Prediction interval [0.1582; 7.6079]
##
## Quantifying heterogeneity:
## tau^2 = 0.3450 [0.0000; 4.0678]; tau = 0.5874 [0.0000; 2.0169]
## I^2 = 0.0% [0.0%; 51.3%]; H = 1.00 [1.00; 1.43]
##
## Test of heterogeneity:
## Q d.f. p-value
## 2.61 5 0.7605
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.show,
xlab="Trigger: shower")
m.sexual <- metabin(data$`intercourse_eventos en exp`,
data$`intercourse_numero de expuestos`,
data$`intercourse_eventos en noexp`,
data$`intercourse_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.sexual ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 NA 0.0
## Morris, 2020 1.3838 [0.6309; 3.0351] 61.9
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 NA 0.0
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 1.1929 [0.2526; 5.6341] 16.4
## Himeno, 2017 NA 0.0
## Arena, 2017 2.1149 [0.4072; 10.9852] 14.6
## Keret, 2016 NA 0.0
## Moon, 2015 NA 0.0
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 2.1212 [0.2021; 22.2589] 7.2
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 4
##
## OR 95%-CI t p-value
## Random effects model 1.4814 [1.0341; 2.1222] 3.48 0.0401
## Prediction interval [0.8050; 2.7261]
##
## Quantifying heterogeneity:
## tau^2 = 0.0073 [0.0000; 0.4740]; tau = 0.0856 [0.0000; 0.6885]
## I^2 = 0.0%; H = 1.00
##
## Test of heterogeneity:
## Q d.f. p-value
## 0.37 3 0.9458
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects modelforest(m.sexual,
xlab="Trigger: sexual intercourse")
m.vom <- metabin(data$`vomiting_eventos en exp`,
data$`vomiting_numero de expuestos`,
data$`vomiting_eventos en noexp`,
data$`vomiting_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.vom ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 NA 0.0
## Morris, 2020 NA 0.0
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 NA 0.0
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 0.2774 [0.0005; 150.1113] 10.9
## Himeno, 2017 NA 0.0
## Arena, 2017 1.0222 [0.1188; 8.7922] 89.1
## Keret, 2016 NA 0.0
## Moon, 2015 NA 0.0
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 NA 0.0
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 2
##
## OR 95%-CI t p-value
## Random effects model 0.8872 [0.0051; 154.1137] -0.29 0.8174
##
## Quantifying heterogeneity:
## tau^2 = 0.0585; tau = 0.2419; I^2 = 0.0%; H = 1.00
##
## Test of heterogeneity:
## Q d.f. p-value
## 0.15 1 0.7007
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.vom,
xlab="Trigger: vomits")
DWI lesions
m.difu <- metabin(data$`DWI_eventos en exp`,
data$`DWI_numero de expuestos`,
data$`DWI_eventos en noexp`,
data$`DWI_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")## Warning in metabin(data$`DWI_eventos en exp`, data$`DWI_numero de expuestos`, :
## Studies with non-positive values for n.e and / or n.c get no weight in meta-
## analysis.m.difu ## OR 95%-CI %W(random)
## Lee DA, 2021 0.7250 [0.2030; 2.5899] 24.8
## Oliveira, 2020 241.0000 [0.4291; 135362.0532] 6.2
## Morris, 2020 0.8342 [0.1808; 3.8499] 23.5
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 NA 0.0
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 NA 0.0
## Himeno, 2017 NA 0.0
## Arena, 2017 0.2818 [0.0005; 169.8720] 6.1
## Keret, 2016 NA 0.0
## Moon, 2015 0.6818 [0.0853; 5.4475] 20.5
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 13.6000 [1.2245; 151.0446] 18.8
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 NA 0.0
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 6
##
## OR 95%-CI t p-value
## Random effects model 1.7385 [0.2365; 12.7784] 0.71 0.5079
## Prediction interval [0.0094; 323.0415]
##
## Quantifying heterogeneity:
## tau^2 = 2.9391 [0.0000; 31.6675]; tau = 1.7144 [0.0000; 5.6274]
## I^2 = 36.6% [0.0%; 74.7%]; H = 1.26 [1.00; 1.99]
##
## Test of heterogeneity:
## Q d.f. p-value
## 7.88 5 0.1627
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.difu,
xlab="DWI lesions")
reflux
m.reflux <- metabin(data$`reflux_eventos en exp`,
data$`reflux_numero de expuestos`,
data$`reflux_eventos en noexp`,
data$`reflux_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")
m.reflux ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 NA 0.0
## Morris, 2020 NA 0.0
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 NA 0.0
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 NA 0.0
## Himeno, 2017 NA 0.0
## Arena, 2017 NA 0.0
## Keret, 2016 NA 0.0
## Moon, 2015 NA 0.0
## Kwon, 2014 NA 0.0
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 14.0769 [0.0228; 8696.8220] 6.4
## Quinette,2006 NA 0.0
## Agosti, 2006 1.9333 [0.5125; 7.2935] 93.6
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 2
##
## OR 95%-CI t p-value
## Random effects model 2.1947 [0.0046; 1047.6526] 1.62 0.3522
##
## Quantifying heterogeneity:
## tau^2 = 0.2947; tau = 0.5428; I^2 = 0.0%; H = 1.00
##
## Test of heterogeneity:
## Q d.f. p-value
## 0.35 1 0.5532
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Hartung-Knapp adjustment for random effects model
## - Continuity correction of 0.1 in studies with zero cell frequenciesforest(m.reflux,
xlab="Yugular reflux")
EEG
m.eeg <- metabin(data$`EEG_eventos en exp`,
data$`EEG_numero de expuestos`,
data$`EEG_eventos en noexp`,
data$`EEG_n no exp`,
data = data,
studlab = data$Study,
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
incr = 0.1,
sm = "OR")## Warning in metabin(data$`EEG_eventos en exp`, data$`EEG_numero de expuestos`, :
## Studies with non-positive values for n.e and / or n.c get no weight in meta-
## analysis.m.eeg ## OR 95%-CI %W(random)
## Lee DA, 2021 NA 0.0
## Oliveira, 2020 0.5263 [0.0739; 3.7460] 10.1
## Morris, 2020 1.1053 [0.4783; 2.5541] 42.8
## Waliszewska-Prosol, 2020 NA 0.0
## Tynas, 2020 NA 0.0
## Romoli, 2020 NA 0.0
## Eisele, 2019 NA 0.0
## Han, 2019 NA 0.0
## Alessandro, 2019 NA 0.0
## Himeno, 2017 NA 0.0
## Arena, 2017 1.4848 [0.3444; 6.4010] 17.3
## Keret, 2016 NA 0.0
## Moon, 2015 NA 0.0
## Kwon, 2014 1.8276 [0.6315; 5.2887] 29.8
## Buhr, 2012 NA 0.0
## Uttner, 2012 NA 0.0
## Auyeung, 2010 NA 0.0
## Lee SY, 2009 NA 0.0
## Agosti,2008 NA 0.0
## Chung, 2007 NA 0.0
## Quinette,2006 NA 0.0
## Agosti, 2006 NA 0.0
## Akkawi, 2005 NA 0.0
## Toledo, 2005 NA 0.0
## Pantoni, 2005 NA 0.0
## Lampl, 2004 NA 0.0
## Chen, 1999 NA 0.0
## Pai, 1999 NA 0.0
## Klotzsch, 1996 NA 0.0
## Zorzon, 1995 NA 0.0
## Fredericks, 1993 NA 0.0
## Gallassi, 1993 NA 0.0
## Melo,1992 NA 0.0
## Gandolfo,1992 NA 0.0
## Hodges, 1990 NA 0.0
## Hinge, 1986 NA 0.0
##
## Number of studies combined: k = 4
##
## OR 95%-CI t p-value
## Random effects model 1.2534 [0.6457; 2.4328] 1.08 0.3579
## Prediction interval [0.2913; 5.3919]
##
## Quantifying heterogeneity:
## tau^2 = 0.0716 [0.0000; 3.3616]; tau = 0.2675 [0.0000; 1.8335]
## I^2 = 0.0% [0.0%; 66.5%]; H = 1.00 [1.00; 1.73]
##
## Test of heterogeneity:
## Q d.f. p-value
## 1.37 3 0.7119
##
## Details on meta-analytical method:
## - Mantel-Haenszel method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects modelforest(m.eeg,
xlab="Abnormal EEG")