Meta Analysis - Cognition, Amyloid, Tau
#Libraries
library(meta)
library(metasens)
library(readxl)
library(metafor)
library(DescTools)
library(psych)
library(tidyverse)
library(metaviz)
library(table1)
library(stargazer)#Data
#download data and subset it
data_full <- read_xlsx("/Users/ferrec14/Documents/Meta Analysis/meta_analysis_0321.xlsx", sheet= "representatives_global_memory")
data <- subset(data_full, table1 ==1 & exclude !=1 ) #Tables 1
data_table1 <- subset(data_full, table1 == 1 & exclude != 1)
#labels
label(data_table1$memory_composite) <- "Memory"
label(data_table1$global_composite) <- 'Global'
label(data_table1$author) <- 'Author'
label(data_table1$year) <- 'Year '
label(data_table1$amyloid) <- 'Amyloid'
label(data_table1$tau) <- 'Tau'
label(data_table1$interaction) <- 'Interaction'
label(data_table1$addition) <- 'Addition'
label(data_table1$selected_tau_amyloid) <- 'Selected ligand'
label(data_table1$ligand) <- 'Ligand'
label(data_table1$ligand_other) <- 'Ligand other'
label(data_table1$csf) <- 'CSF'
label(data_table1$csf_other) <- 'CSF other'
label(data_table1$serum) <- 'Serum'
label(data_table1$type_study) <- 'Type study'
label(data_table1$neurodeg) <- 'Neurodegeneration'
label(data_table1$method) <- 'Method'
label(data_table1$controlled) <- 'Controlled'
label(data_table1$cov_time) <- 'Time'
label(data_table1$sex) <- 'Sex'
label(data_table1$education) <- 'Education'
label(data_table1$apoe) <- 'Apoe'
label(data_table1$covaratiate_others) <- 'Other cov'
label(data_table1$cohort_database) <- 'Cohort'
label(data_table1$age_type) <- 'Age type'
#label(data_table1$age) <- 'Age'
#label(data_table1$age_variance) <- 'Age variance'
#abel(data_table1$total_nc) <- 'Total N'
#label(data_table1$nc_pos) <- 'N positive'
#label(data_table1$nc_neg) <- 'N negative'
label(data_table1$roi_lateral_temporal) <- 'ROI temporal lateral'
label(data_table1$roi_entorhinal_hippocampus) <-
'ROI entorhinal or hipocampus'
label(data_table1$roi_lateral_parietal) <- 'ROI parietal lateral'
label(data_table1$roi_cingulate) <- 'ROI cingulate'
label(data_table1$roi_frontal) <- 'ROI frontal'
label(data_table1$roi_precuneus) <- 'ROI precuneus'
label(data_table1$roi_occipital) <- 'ROI occipital'
label(data_table1$suvr_dvr) <- 'SUVR/DVR'
#label(data_table1$type_cognitive) <- ''
label(data_table1$cross_long) <- 'CS or Longitudinal'
label(data_table1$all_r) <- 'R value'
label(data_table1$exclude) <- ''
label(data_table1$fisherz) <- 'Fisher z'
label(data_table1$weight) <- 'Weighted (n-3)'
label(data_table1$weight_se) <- 'Weigted SE'
#as factors
data_table1$memory_composite <-
as.factor(data_table1$memory_composite)
data_table1$global_composite <-
as.factor(data_table1$global_composite)
data_table1$amyloid <- as.factor(data_table1$amyloid)
data_table1$tau <- as.factor(data_table1$tau)
data_table1$interaction <- as.factor(data_table1$interaction)
data_table1$addition <- as.factor(data_table1$addition)
data_table1$selected_tau_amyloid <-
as.factor(data_table1$selected_tau_amyloid)
data_table1$type_study <- as.factor(data_table1$type_study)
data_table1$neurodeg <- as.factor(data_table1$neurodeg)
data_table1$sex <- as.factor(data_table1$sex)
data_table1$education <- as.factor(data_table1$education)
data_table1$apoe <- as.factor(data_table1$apoe)
data_table1$age_type <- as.factor(data_table1$age_type)
data_table1$roi_lateral_temporal <-
as.factor(data_table1$roi_lateral_temporal)
data_table1$roi_entorhinal_hippocampus <-
as.factor(data_table1$roi_entorhinal_hippocampus)
data_table1$roi_lateral_parietal <-
as.factor(data_table1$roi_lateral_parietal)
data_table1$roi_cingulate <- as.factor(data_table1$roi_cingulate)
data_table1$roi_frontal <- as.factor(data_table1$roi_frontal)
data_table1$roi_precuneus <- as.factor(data_table1$roi_precuneus)
data_table1$roi_occipital <- as.factor(data_table1$roi_occipital)
data_table1$suvr_dvr <- as.factor(data_table1$suvr_dvr)
data_table1$type_cognitive <- as.factor(data_table1$type_cognitive)
data_table1$cross_long <- as.factor(data_table1$cross_long)##Table descriptive amyloid
##Table descriptive tau
##Table summary included papers
newtab <-
data.frame(
data_table1$author,
data_table1$year,
data_table1$cohort_database,
data_table1$method,
data_table1$cross_long,
data_table1$cov_time,
data_table1$controlled,
data_table1$total_nc,
data_table1$Global_composite,
data_table1$Memory_composite
)
newtab <-newtab %>% arrange(data_table1$cohort_database, desc(data_table1$total_nc), data_table1$method, data_table1$Global_composite)
names(newtab)[names(newtab) == "data_table1.author"] <- "Author"
names(newtab)[names(newtab) == "data_table1.method"] <- "Method"
names(newtab)[names(newtab) == "data_table1.year"] <- "Year"
names(newtab)[names(newtab) == "data_table1.cohort_database"] <- "Cohort"
names(newtab)[names(newtab) == "data_table1.cross_long"] <- "Type of study"
names(newtab)[names(newtab) == "data_table1.cov_time"] <- "Controlled by time of follow-up"
names(newtab)[names(newtab) == "data_table1.controlled"] <- "Controlled"
names(newtab)[names(newtab) == "data_table1.total_nc"] <- "N"
names(newtab)[names(newtab) == "data_table1.Global_composite"] <- "Global"
names(newtab)[names(newtab) == "data_table1.Memory_composite"] <- "Memory"
kableExtra::kbl(newtab) %>%
kableExtra::kable_paper(bootstrap_options = "striped", full_width = F)| Author | Year | Cohort | Method | Type of study | Controlled by time of follow-up | Controlled | N | Global | Memory |
|---|---|---|---|---|---|---|---|---|---|
| Kawas CH | 2012 | 90 + study | Florbetapir | C | No | No | 13 | 0.3097950 | NA |
| Kawas CH | 2012 | 90 + study | Florbetapir | C | No | No | 13 | NA | NA |
| Kawas CH | 2012 | 90 + study | Florbetapir | C | No | No | 13 | NA | -0.2974385 |
| Kawas CH | 2012 | 90 + study | Florbetapir | L | Yes | No | 7 | 0.9540240 | NA |
| Kawas CH | 2012 | 90 + study | Florbetapir | L | Yes | No | 7 | NA | NA |
| Kawas CH | 2012 | 90 + study | Florbetapir | L | Yes | No | 6 | NA | 0.3272785 |
| Sperling RA; | 2020 | A4S | Florbetapir | C | 0 | Yes | 6763 | -0.0546389 | NA |
| Sperling RA; | 2020 | A4S | Florbetapir | C | 0 | Yes | 6763 | NA | -0.0546389 |
| Doré, Vincent; | 2013 | ABLS | PiB | C | 0 | No | 93 | 0.1121239 | NA |
| Doré, Vincent; | 2013 | ABLS | PiB | L | 0 | No | 53 | 0.0748669 | NA |
| Groot C; | 2021 | ABLS | Flortaucipir | C | 0 | No | 47 | -0.3100000 | NA |
| Groot C; | 2021 | ABLS | Flortaucipir | C | 0 | No | 47 | NA | -0.2300000 |
| Jansen WJ | 2017 | ABS | PiB | C | No | Yes | 2908 | 0.0709225 | NA |
| Jansen WJ | 2017 | ABS | PiB | C | No | Yes | 2908 | NA | 0.0884409 |
| Xiong C | 2016 | ACS | Ab42 | C | No | Yes | 209 | 0.0200000 | NA |
| Xiong C | 2016 | ACS | Ab42 | L | No | Yes | 209 | 0.2200000 | NA |
| Xiong C | 2016 | ACS | PiB | C | No | Yes | 209 | 0.1500000 | NA |
| Xiong C | 2016 | ACS | PiB | L | No | Yes | 209 | 0.2700000 | NA |
| Xiong C | 2016 | ACS | ptau | C | No | Yes | 209 | 0.0600000 | NA |
| Xiong C | 2016 | ACS | ptau | L | No | Yes | 209 | 0.3300000 | NA |
| Donohue MC | 2017 | ADNI | Florbetapir, PiB | L | Yes | Yes | 445 | 0.0100000 | NA |
| Donohue MC | 2017 | ADNI | Florbetapir, PiB | C | No | No | 445 | 0.0398969 | NA |
| Donohue MC | 2017 | ADNI | Florbetapir, PiB | L | Yes | Yes | 445 | NA | 0.2400000 |
| Moscoso A; | 2021 | ADNI | Florbetapir, p-tau181 | C | Yes | Yes | 378 | -0.3300000 | NA |
| Moscoso A; | 2021 | ADNI | p-tau181 | L | 0 | Yes | 378 | -0.1200000 | NA |
| Guo T; | 2020 | ADNI | Florbetapir, Florbetaben | C | Yes | Yes | 299 | 0.0000000 | NA |
| Guo T; | 2020 | ADNI | Florbetapir, Florbetaben, Flortaucipir | C | Yes | Yes | 299 | 0.0000000 | NA |
| Guo T; | 2020 | ADNI | Flortaucipir | C | Yes | Yes | 299 | 0.0000000 | NA |
| Schott JM | 2010 | ADNI | Ab42 | C | No | Yes | 105 | 0.1331823 | NA |
| Schott JM | 2010 | ADNI | Ab42 | C | No | Yes | 105 | NA | -0.0303373 |
| Diouf I; | 2020 | ADNI | ttau/ab42 | C | Yes | Yes | 68 | -0.7035720 | NA |
| Aschenbrenner AJ | 2014 | ADRC | Ab42 | C | No | Yes | 113 | NA | -0.0650000 |
| Aschenbrenner AJ | 2014 | ADRC | PiB | C | No | Yes | 113 | NA | 0.0550000 |
| Aschenbrenner AJ | 2014 | ADRC | ptau | C | No | Yes | 113 | NA | 0.0250000 |
| Harrington KD | 2018 | AIBL | Florbetapir, PiB, FDDNP | C | No | No | 494 | 0.0829341 | NA |
| Harrington KD | 2018 | AIBL | Florbetapir, PiB, FDDNP | C | No | Yes | 494 | NA | 0.1105152 |
| Lim YY | 2018 | AIBL | Florbetapir, PiB, FDDNP | C | No | No | 447 | 0.2093804 | NA |
| Lim YY | 2018 | AIBL | Florbetapir, PiB, FDDNP | L | Yes | Yes | 447 | NA | 0.1505543 |
| Mil√†-Alom√† M; | 2020 | ALFA + | Aβ42/40 | C | 0 | Yes | 381 | -0.0500181 | NA |
| Mil√†-Alom√† M; | 2020 | ALFA + | Aβ42/40 | L | 0 | Yes | 381 | NA | 0.0000000 |
| Milà-Alomà M; | 2020 | ALFA + | ptau | C | 0 | Yes | 381 | 0.0336120 | NA |
| Milà-Alomà M; | 2020 | ALFA + | ptau | C | 0 | Yes | 381 | NA | 0.0000000 |
| Berenguer RG | 2014 | Alicante | Ab42 | C | No | No | 39 | NA | 0.3770000 |
| Berenguer RG | 2014 | Alicante | ab42/ptau | C | No | No | 39 | NA | 0.3440000 |
| van Harten AC | 2013 | Amsterdam | Ab42 | C | No | No | 132 | 0.1643990 | NA |
| van Harten AC | 2013 | Amsterdam | Ab42 | L | Yes | No | 132 | 0.8574929 | NA |
| van Harten AC | 2013 | Amsterdam | Ab42 | C | No | No | 132 | NA | 0.8516583 |
| van Harten AC | 2013 | Amsterdam | Ab42 | L | Yes | No | 132 | NA | 0.5547002 |
| van Harten AC | 2013 | Amsterdam | ab42/ttau | C | No | No | 132 | 0.0000000 | NA |
| van Harten AC | 2013 | Amsterdam | ab42/ttau | L | Yes | No | 132 | 0.8574929 | NA |
| van Harten AC | 2013 | Amsterdam | ab42/ttau | C | No | No | 132 | NA | 0.5547002 |
| van Harten AC | 2013 | Amsterdam | ab42/ttau | L | Yes | No | 132 | NA | 0.8682431 |
| van Harten AC | 2013 | Amsterdam | ptau | L | Yes | No | 132 | 0.1643990 | NA |
| van Harten AC | 2013 | Amsterdam | ptau | C | No | No | 132 | 0.4472136 | NA |
| van Harten AC | 2013 | Amsterdam | ptau | C | No | No | 132 | NA | 0.6000000 |
| van Harten AC | 2013 | Amsterdam | ptau | L | Yes | No | 132 | NA | 0.4472136 |
| Tolboom | 2009 | Amsterdam | FDDNP | C | No | Yes | 13 | 0.3900000 | NA |
| Tolboom | 2009 | Amsterdam | PiB | C | No | Yes | 13 | 0.7500000 | NA |
| Harrison TM; | 2020 | BACS | Flortaucepir | C | 0 | Yes | 125 | 0.1156070 | NA |
| Harrison TM; | 2020 | BACS | Flortaucepir | C | 0 | Yes | 125 | NA | -0.3380000 |
| Harrison TM; | 2020 | BACS | PiB | C | 0 | Yes | 125 | 0.1237862 | NA |
| Harrison TM; | 2020 | BACS | PiB | C | 0 | Yes | 125 | NA | 0.2000000 |
| Chen X; | 2021 | BACS | Flortaucepir | L | Yes | Yes | 124 | NA | -0.0217334 |
| Chen X; | 2021 | BACS | PiB | C | 0 | No | 124 | NA | 0.1455899 |
| Chen X; | 2021 | BACS | PiB, Flortaucepir | L | Yes | Yes | 124 | NA | -0.0260044 |
| Ossenkoppele R | 2013 | BACS | PiB | C | No | Yes | 81 | -0.2292357 | NA |
| Ossenkoppele R | 2013 | BACS | PiB | C | No | Yes | 81 | 0.0572249 | NA |
| Ossenkoppele R | 2013 | BACS | PiB | C | No | Yes | 81 | NA | 0.0833677 |
| Sierra-Rio A | 2015 | Barcelona | ab42/ptau | C | No | No | 55 | 0.0475204 | NA |
| Molinuevo JL | 2014 | Barcelona | ab42/ptau/ttau | C | No | Yes | 38 | 0.1724884 | NA |
| Molinuevo JL | 2014 | Barcelona | ab42/ptau/ttau | C | No | Yes | 38 | NA | 0.0722052 |
| Tort-Merino A; | 2019 | BCN | Ab42 | L | 0 | Yes | 32 | NA | -0.0400000 |
| Tort-Merino A; | 2019 | BCN | ptau | L | 0 | Yes | 32 | NA | -0.2400000 |
| Chen L; | 2021 | Biocard (JH) | PiB | C | 0 | Yes | 97 | 0.0558103 | NA |
| Insel PS | 2019 | BioFINDER | ab42/ab40 | C | No | Yes | 329 | 0.0458154 | NA |
| Palmqvist S | 2014 | BioFINDER | Flutemetamol | C | No | Yes | 118 | 0.3200000 | NA |
| Palmqvist S | 2014 | BioFINDER | Flutemetamol | C | No | Yes | 118 | NA | 0.2800000 |
| Bilgel M | 2018 | BLSA | PiB | L | Yes | No | 127 | 0.8434402 | NA |
| Bilgel M | 2018 | BLSA | PiB | L | Yes | Yes | 127 | NA | 0.0574776 |
| Ziontz J; | 2019 | BLSA | Flortaucepir | L | Yes | Yes | 54 | NA | 0.0000000 |
| Ziontz J; | 2019 | BLSA | PiB | L | Yes | Yes | 54 | NA | 0.0034419 |
| Resnick SM | 2010 | BLSA | PiB | L | Yes | Yes | 51 | 0.0135846 | NA |
| Resnick SM | 2010 | BLSA | PiB | L | Yes | Yes | 51 | NA | 0.0163589 |
| Radanovic M | 2019 | Brazil | Ab42 | C | No | Yes | 54 | 0.2110000 | NA |
| Radanovic M | 2019 | Brazil | ab42/ptau181 | C | No | Yes | 54 | 0.2070000 | NA |
| Radanovic M | 2019 | Brazil | ptau181 | C | No | Yes | 54 | 0.2840000 | NA |
| Stomrud E | 2010 | CMRU | Ab42 | C | No | Yes | 37 | -0.0005000 | NA |
| Stomrud E | 2010 | CMRU | Ab42 | L | No | Yes | 37 | 0.2300000 | NA |
| Stomrud E | 2010 | CMRU | Ab42 | C | No | Yes | 37 | NA | 0.1340000 |
| Stomrud E | 2010 | CMRU | Ab42 | L | No | Yes | 37 | NA | 0.4370000 |
| Stomrud E | 2010 | CMRU | ptau 181 | C | No | Yes | 37 | -0.0320000 | NA |
| Stomrud E | 2010 | CMRU | ptau 181 | L | No | Yes | 37 | 0.0600000 | NA |
| Stomrud E | 2010 | CMRU | ptau 181 | C | No | Yes | 37 | NA | -0.1280000 |
| Stomrud E | 2010 | CMRU | ptau 181 | L | No | Yes | 37 | NA | -0.0940000 |
| Doraiswamy PM | 2014 | cohort | Florbetapir | C | No | No | 67 | 0.0693774 | NA |
| Doraiswamy PM | 2014 | cohort | Florbetapir | L | Yes | Yes | 67 | 0.1237229 | NA |
| Doraiswamy PM | 2014 | cohort | Florbetapir | C | No | No | 67 | NA | 0.2334509 |
| Joannette M; | 2020 | CRIUGM | PiB | C | 0 | Yes | 104 | NA | 0.5865151 |
| Kristofikova Z | 2014 | Czech Republic. | Ab42 | C | No | No | 15 | 0.1720000 | NA |
| Kristofikova Z | 2014 | Czech Republic. | ptau | C | No | No | 15 | 0.4450000 | NA |
| Horn MM | 2018 | Dallas | PiB | C | No | Yes | 195 | NA | 0.2500000 |
| Franzmeier N | 2018 | DELCODE | Florbetapir | C | No | No | 49 | 0.2613050 | NA |
| Franzmeier N | 2018 | DELCODE | Florbetapir | C | No | Yes | 49 | NA | 0.1203751 |
| Visser PJ | 2009 | DESCRIPA | ab42/ttau | C | No | Yes | 60 | -0.0576322 | NA |
| Visser PJ | 2009 | DESCRIPA | ab42 | L | No | Yes | 58 | NA | 0.0449278 |
| Visser PJ | 2009 | DESCRIPA | ab42/ttau | L | No | Yes | 58 | 0.0142401 | NA |
| Caballero MÁA; | 2020 | DLBS | Florbetapir | C | 0 | Yes | 294 | NA | 0.1373950 |
| Farrell ME | 2017 | DLBS | Florbetapir | L | Yes | No | 123 | 0.7180338 | NA |
| Farrell ME | 2017 | DLBS | Florbetapir | L | Yes | No | 123 | NA | 0.8046629 |
| Song Z | 2016 | DLBS | Florbetapir | C | No | No | 82 | 0.1225236 | NA |
| Song Z | 2016 | DLBS | Florbetapir | C | No | No | 82 | NA | 0.1287696 |
| Lu K | 2019 | DRC-UK | Florbetapir | C | No | Yes | 502 | 0.2050000 | NA |
| Lu K | 2019 | DRC-UK | Florbetapir | C | Yes | Yes | 502 | NA | 0.1200000 |
| Bos I; | 2019 | EMIF | ab42/40, ttau | C | No | Yes | 140 | 0.5397881 | NA |
| Gangishetti U | 2018 | Emory, Penn, Washu | Florbetapir | C | No | No | 44 | 0.1008711 | NA |
| Konijnenberg E | 2019 | European Information Framework for AD-PreclinAD | Flutemetamol | C | No | Yes | 196 | NA | 0.0775520 |
| Konijnenberg E | 2019 | European Information Framework for AD-PreclinAD | ab42/ab40 | C | No | Yes | 126 | NA | 0.0345742 |
| Kemppainen N | 2017 | FINGER | PiB | C | No | Yes | 48 | 0.1034905 | NA |
| Kemppainen N | 2017 | FINGER | PiB | C | No | Yes | 48 | NA | 0.0313408 |
| Haapalinna F | 2018 | Finland | Ab42 | C | No | No | 57 | NA | 0.2860000 |
| Martikainen IK | 2018 | Finnish Geriatric Intervention Study | PiB | C | No | Yes | 40 | 0.0304178 | NA |
| Martikainen IK | 2018 | Finnish Geriatric Intervention Study | PiB | C | No | Yes | 40 | NA | -0.1323262 |
| Kang JM | 2017 | Gachon, Korea | THK5351 | C | No | Yes | 43 | NA | 0.0677500 |
| Cho H; | 2019 | Gangnam | Florbetaben | L | 0 | No | 52 | -0.0209696 | NA |
| Cho H; | 2019 | Gangnam | Florbetaben | C | 0 | No | 52 | 0.0185606 | NA |
| Cho H; | 2019 | Gangnam | Florbetaben | C | 0 | No | 52 | NA | 0.0514524 |
| Cho H; | 2019 | Gangnam | Florbetaben | L | 0 | No | 52 | NA | 0.0468836 |
| Ecay-Torres M | 2018 | GAP | Ab42 | C | No | Yes | 238 | 0.0010000 | NA |
| Zhao Y | 2017 | GEM | PiB | C | No | No | 175 | NA | 0.0819069 |
| Rådestig MA; | 2021 | Gothenburg | Ab42 | C | 0 | No | 259 | 0.0311846 | NA |
| Rådestig MA; | 2021 | Gothenburg | Ab42 | C | 0 | No | 259 | NA | 0.3169288 |
| Rådestig MA; | 2021 | Gothenburg | ptau | C | 0 | No | 259 | NA | -0.0027847 |
| Hanseeuw BJ | 2017 | HABS | PiB | C | No | No | 277 | 0.1317225 | NA |
| Hanseeuw BJ | 2017 | HABS | PiB | L | No | No | 277 | 0.1777283 | NA |
| Hanseeuw BJ | 2017 | HABS | PiB | L | No | No | 277 | NA | -0.0411176 |
| Hanseeuw BJ | 2017 | HABS | PiB | C | No | No | 277 | NA | 0.0337453 |
| Jacobs HIL | 2018 | HABS | PiB | C | No | Yes | 256 | 0.0759581 | NA |
| Jacobs HIL | 2018 | HABS | PiB | C | No | Yes | 256 | NA | -0.0011623 |
| Chhatwal JP; | 2020 | HABS | NT1 | C | Yes | Yes | 112 | 0.0000000 | NA |
| Chhatwal JP; | 2020 | HABS | NT1 | L | Yes | Yes | 112 | 0.0000000 | NA |
| Chhatwal JP; | 2020 | HABS | PiB | L | Yes | Yes | 112 | 0.0000000 | NA |
| Chhatwal JP; | 2020 | HABS | PiB | L | Yes | Yes | 112 | 0.0000000 | NA |
| Hanseeuw BJ; | 2019 | HBS | PiB, Flortaucepir | NA | Yes | Yes | 60 | 0.0000000 | NA |
| Yaffe K | 2011 | Healthy ABC | ab42/ab40 | L | No | No | 659 | -0.0800631 | NA |
| Yaffe K | 2011 | Healthy ABC | ab42/ab40 | C | No | No | 659 | 0.0198521 | NA |
| Yaffe K | 2011 | Healthy ABC | Ab42 | L | No | No | 658 | -0.1112537 | NA |
| Yaffe K | 2011 | Healthy ABC | Ab42 | C | No | No | 658 | -0.0323689 | NA |
| Mok VC | 2016 | Hong Kong | PiB | C | No | Yes | 75 | 0.1400470 | NA |
| Mok VC | 2016 | Hong Kong | PiB | L | No | Yes | 53 | 0.0726713 | NA |
| Mok VC | 2016 | Hong Kong | PiB | L | No | Yes | 53 | 0.1850980 | NA |
| Mok VC | 2016 | Hong Kong | PiB | L | No | Yes | 53 | 0.3220456 | NA |
| Hamelin L | 2018 | IMABio3 | PiB | C | No | No | 17 | 0.1396219 | NA |
| Hamelin L | 2018 | IMABio3 | PiB | L | No | No | 17 | 0.1672821 | NA |
| Hamelin L | 2018 | IMABio3 | PiB | L | No | No | 17 | NA | 0.2762891 |
| Besson FL | 2015 | IMAP | 0 | C | No | No | 54 | -0.0504891 | NA |
| Besson FL | 2015 | IMAP | 0 | C | No | No | 54 | NA | -0.1110823 |
| Hoscheidt SM | 2016 | IMPACT | ab42/ab40/ptau/ttau | C | No | Yes | 70 | NA | 0.0964365 |
| Teipel SJ | 2017 | INSIGHT | Florbetapir | C | No | Yes | 318 | 0.1100000 | NA |
| Dubois B | 2018 | INSIGHT | Florbetapir | C | No | Yes | 318 | 0.1213773 | NA |
| Teipel SJ | 2017 | INSIGHT | Florbetapir | C | No | Yes | 318 | NA | 0.1220000 |
| Dubois B | 2018 | INSIGHT | Florbetapir | C | No | Yes | 318 | NA | 0.0090362 |
| Tardif CL | 2017 | INTREPAD | Ab42 | C | No | No | 46 | -0.1107440 | NA |
| Tardif CL | 2017 | INTREPAD | ttau | C | No | No | 46 | 0.3570145 | NA |
| Rosenberg PB | 2013 | John Hopkns | Florbetapir | C | No | No | 15 | 0.1650000 | NA |
| Rosenberg PB | 2013 | John Hopkns | Florbetapir | C | No | No | 15 | NA | 0.1500000 |
| Chatterjee P | 2019 | KARVIAH | Florbetaben | C | No | No | 100 | -0.1245904 | NA |
| Leahey TM | 2008 | Kent USA | Ab40 | C | No | Yes | 35 | 0.3400000 | NA |
| Bos I; | 2019 | Knight | Ab42 | C | 0 | Yes | 433 | 0.1145347 | NA |
| Bos I; | 2019 | Knight | ttau | C | 0 | Yes | 433 | 0.0742767 | NA |
| Roe CM | 2013 | Knight | ab42 | L | No | Yes | 430 | 0.0678207 | NA |
| Roe CM | 2013 | Knight | ptau181 | L | No | Yes | 430 | 0.0548775 | NA |
| Roe CM | 2013 | Knight | ptau181/ab42 | L | No | Yes | 430 | 0.0926467 | NA |
| Schindler SE | 2017 | Knight | Ab42 | L | Yes | Yes | 233 | 0.0405000 | NA |
| Schindler SE | 2017 | Knight | Ab42 | C | Yes | Yes | 233 | 0.0755000 | NA |
| Schindler SE | 2017 | Knight | Ab42 | C | Yes | Yes | 233 | NA | 0.0695833 |
| Schindler SE | 2017 | Knight | Ab42 | L | Yes | Yes | 233 | NA | 0.0110000 |
| Schindler SE | 2017 | Knight | ptau | C | Yes | Yes | 233 | 0.0025000 | NA |
| Schindler SE | 2017 | Knight | ptau | L | Yes | Yes | 233 | 0.0135000 | NA |
| Schindler SE | 2017 | Knight | ptau | C | Yes | Yes | 233 | NA | 0.0282500 |
| Schindler SE | 2017 | Knight | ptau | L | Yes | Yes | 233 | NA | 0.0175000 |
| Schindler SE | 2017 | Knight | ptau/ab42 | L | Yes | Yes | 233 | 0.0385000 | NA |
| Schindler SE | 2017 | Knight | ptau/ab42 | C | Yes | Yes | 233 | 0.0695000 | NA |
| Schindler SE | 2017 | Knight | ptau/ab42 | C | Yes | Yes | 233 | NA | 0.0950833 |
| Schindler SE | 2017 | Knight | ptau/ab42 | L | Yes | Yes | 233 | NA | 0.0210000 |
| Storandt M | 2009 | Knight | PiB | L | Yes | Yes | 135 | 0.0041069 | NA |
| Storandt M | 2009 | Knight | PiB | C | No | No | 135 | 0.1209122 | NA |
| Storandt M | 2009 | Knight | PiB | L | Yes | Yes | 135 | NA | 0.0041069 |
| Moon YS | 2011 | Korean | ab42 | C | No | Yes | 123 | 0.2500000 | NA |
| Adamczuk K | 2014 | Leuven | Flutemetamol | C | No | Yes | 56 | 0.2271847 | NA |
| Adamczuk K | 2014 | Leuven | Flutemetamol | C | No | Yes | 56 | NA | 0.2150714 |
| Janelidze S | 2018 | Mälmo Diet Cancer Study +BioFINDER | ab42/ab40 | C | No | Yes | 508 | 0.1161625 | NA |
| Lilamand M | 2019 | MAPT | Florbetapir | C | No | No | 269 | 0.1056808 | NA |
| Lilamand M | 2019 | MAPT | Florbetapir | L | Yes | Yes | 269 | 0.4713930 | NA |
| Lilamand M | 2019 | MAPT | Florbetapir | L | Yes | Yes | 269 | NA | 0.1433694 |
| Roberts RO | 2018 | MCSA | PiB | L | No | Yes | 1492 | -0.0367629 | NA |
| Roberts RO | 2018 | MCSA | PiB | C | No | No | 1492 | 0.2087571 | NA |
| Roberts RO | 2018 | MCSA | PiB | L | No | Yes | 1492 | NA | -0.0385758 |
| Roberts RO | 2018 | MCSA | PiB | C | No | No | 1492 | NA | 0.1569886 |
| Mielke MM | 2012 | MCSA | PiB | C | No | Yes | 483 | -0.0270166 | NA |
| Mielke MM | 2012 | MCSA | PiB | C | No | Yes | 483 | NA | -0.0220700 |
| Mielke MM | 2017 | MCSA | PiB | L | Yes | Yes | 115 | 0.0116555 | NA |
| Mielke MM | 2017 | MCSA | PiB | L | Yes | Yes | 115 | NA | 0.0058281 |
| Nakamura A | 2018 | MULNIAD | PiB | C | No | Yes | 38 | -0.0189523 | NA |
| Nakamura A | 2018 | MULNIAD | PiB | C | No | Yes | 38 | NA | -0.0141493 |
| Lafirdeen ASM | 2019 | Multicenter France | ab42 | C | No | Yes | 3562 | 0.2219107 | NA |
| Lafirdeen ASM | 2019 | Multicenter France | ptau/ttau | C | No | Yes | 3562 | 0.1733867 | NA |
| Snitz BE; | 2020 | MYHAT‐NI | Florbetapir | C | 0 | Yes | 118 | -0.0300000 | NA |
| Snitz BE; | 2020 | MYHAT‐NI | Florbetapir | C | 0 | Yes | 118 | NA | 0.0200000 |
| Snitz BE; | 2020 | MYHAT‐NI | NA | C | 0 | Yes | 118 | -0.1600000 | NA |
| Snitz BE; | 2020 | MYHAT‐NI | NA | C | 0 | Yes | 118 | NA | 0.0200000 |
| Pomara N | 2005 | Nathan Kline | Ab42 | L | No | No | 34 | 0.4100000 | NA |
| Sala-Llonch R | 2017 | Norway | Ab42 | C | No | No | 89 | -0.0467196 | NA |
| Sala-Llonch R | 2017 | Norway | Ab42 | L | No | No | 89 | 0.1102283 | NA |
| Sala-Llonch R | 2017 | Norway | Ab42 | C | No | No | 89 | NA | -0.0524737 |
| Harrington MG | 2013 | Pasadena | ab42/ttau | C | No | No | 46 | NA | -0.0663486 |
| Meng Y | 2015 | Peking | APL1b28 | C | No | No | 35 | 0.0250556 | NA |
| Nebes RD | 2013 | PITT | PiB | C | No | Yes | 71 | NA | 0.1295316 |
| Aizenstein HJ | 2008 | PITT | PiB | C | No | No | 38 | -0.1215034 | NA |
| Aizenstein HJ | 2008 | PITT | PiB | C | No | No | 34 | NA | -0.3791941 |
| Jiao F; | 2020 | PLA Beijing | PiB, ab42 | C | 0 | No | 57 | 0.0325000 | NA |
| Jiao F; | 2020 | PLA Beijing | PiB, ab42, ttau | C | 0 | No | 57 | -0.1020000 | NA |
| Jiao F; | 2020 | PLA Beijing | ttau | C | 0 | No | 57 | -0.1160000 | NA |
| McMillan CT | 2016 | PPMI | Ab42 | C | No | Yes | 174 | NA | -0.3465076 |
| McMillan CT | 2016 | PPMI | ab42/ptau/ttau | C | No | Yes | 174 | 0.3727037 | NA |
| Lim YY | 2015 | Rhode Island | Florbetapir | C | No | No | 63 | -0.0452078 | NA |
| Lim YY | 2015 | Rhode Island | Florbetapir | L | No | No | 63 | 0.0606041 | NA |
| Lim YY | 2015 | Rhode Island | Florbetapir | C | No | No | 63 | NA | 0.1836993 |
| Liguori C | 2017 | Rome | Ab42 | C | No | No | 50 | NA | 0.7250000 |
| Ebenau JL; | 2020 | science | PiB, Florbetaben, Florbetapir, Flutematamol, ab42, ptau, ttau | C | 0 | Yes | 693 | 0.0000000 | NA |
| Timmers T | 2019 | SCIENCe | Florbetapir | L | No | Yes | 107 | 0.4860464 | NA |
| Timmers T | 2019 | SCIENCe | Florbetapir | C | No | Yes | 107 | 0.7456244 | NA |
| Timmers T | 2019 | SCIENCe | Florbetapir | C | No | Yes | 107 | NA | 0.6711077 |
| Timmers T | 2019 | SCIENCe | Florbetapir | L | No | Yes | 107 | NA | 0.3085927 |
| Llado-Saz S | 2015 | Seville | Florbetapir | C | No | Yes | 120 | NA | 0.0285706 |
| Hahn A; | 2020 | south korea | Florbetapir, Florbetaben, Flutemetamol | C | 0 | Yes | 423 | 0.0000000 | NA |
| Hahn A; | 2020 | south korea | Florbetapir, Florbetaben, Flutemetamol | C | 0 | Yes | 423 | NA | 0.0000000 |
| Huang KL | 2018 | Taiwan | Florbetapir | C | No | Yes | 11 | NA | -0.5600000 |
| Karikari TK; | 2020 | TRIAD | Florbetapir, ab42, p-tau181 | C | 0 | Yes | 85 | 0.0000000 | NA |
| Karikari TK; | 2020 | TRIAD | Florbetapir, ab42, p-tau182 | L | 0 | Yes | 85 | 0.0000000 | NA |
| Han JW; | 2020 | UCD ADRC | PiB | C | 0 | Yes | 154 | NA | -0.0012607 |
| Han JW; | 2020 | UCD ADRC | PiB | L | Yes | Yes | 154 | NA | -0.0130733 |
| Mueller SG | 2018 | UCSF | Florbetapir | C | No | No | 51 | 0.1524001 | NA |
| Mueller SG | 2018 | UCSF | Florbetapir | C | No | No | 51 | NA | 0.0639220 |
| Song Z | 2015 | UCSF | Florbetapir | C | No | Yes | 50 | 0.0200000 | NA |
| Song Z | 2015 | UCSF | Florbetapir | C | No | Yes | 50 | NA | 0.0040000 |
| Hammers DB | 2017 | Utah | Flutemetamol | C | No | Yes | 27 | 0.1912685 | NA |
| Hammers DB | 2017 | Utah | Flutemetamol | C | No | Yes | 27 | NA | 0.3002590 |
| Gu | 2012 | WHICAP | Ab42 | C | No | Yes | 813 | NA | 0.0670742 |
| Cosentino SA | 2010 | WHICAP | Ab42 | C | No | Yes | 481 | 0.0200000 | NA |
| Cosentino SA | 2010 | WHICAP | Ab42 | C | Yes | Yes | 481 | NA | 0.0233333 |
| Cosentino SA | 2010 | WHICAP | Ab43 | L | No | Yes | 481 | 0.0100000 | NA |
| Casaletto KB | 2017 | Wisconsin ADRC | Ab42 | C | No | No | 132 | NA | 0.0200000 |
| Casaletto KB | 2017 | Wisconsin ADRC | ptau | C | No | No | 132 | NA | -0.1400000 |
| Mecca AP | 2017 | Yale | PiB | C | No | Yes | 45 | NA | 0.1270000 |
| van Bergen JMG | 2018 | zurich | Flutemetamol | C | No | Yes | 116 | -0.0447848 | NA |
# stargazer(
# newtab,
# summary = FALSE,
# single.row = TRUE,
# no.space = TRUE,
# digits = 2,
# column.sep.width = "1pt",
# font.size = "small",
# type = 'text',
# out= "summary.text"
# )#Meta analysis ##Grouping by cognitive domain, tracer, time of analysis
#random-effects-model for r values
#data_global <- subset(data, global_composite ==1)
data$memory_composite <- factor(data$memory_composite,
levels = c(0, 1),
labels = c("", "Memory"))
data$global_composite <- factor(data$global_composite,
levels = c(0, 1),
labels = c("", "Global"))
data$cross_long <- factor(data$cross_long,
levels = c("C", "L"),
labels = c("Baseline", "Followup"))
data$amyloid <- factor(data$amyloid,
levels = c(0, 1),
labels = c("", "Amyloid "))
data$tau <- factor(data$tau,
levels = c(0, 1),
labels = c("", "Tau"))
data$interaction <- factor(data$interaction,
levels = c(0, 1),
labels = c("", "Interaction"))
first <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data,
n = data$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR",
#The summary measure we want to calculate- fisher z
byvar = paste(
memory_composite,
global_composite,
cross_long,
amyloid,
tau
),
#grouping variable
print.byvar = gs("print.byvar")
) #print grouping variable
summary(first, digits = 2, pval=TRUE)## Number of studies combined: k = 255
##
## COR 95%-CI t p-value
## Random effects model 0.1339 [ 0.0975; 0.1700] 7.19 < 0.0001
## Prediction interval [-0.4041; 0.6031]
##
## Quantifying heterogeneity:
## tau^2 = 0.0814 [0.0608; 0.0950]; tau = 0.2854 [0.2465; 0.3083]
## I^2 = 90.9% [90.0%; 91.7%]; H = 3.32 [3.17; 3.47]
##
## Test of heterogeneity:
## Q d.f. p-value
## 2661.15 242 0
##
## Results for subgroups (random effects model):
## k COR 95%-CI tau^2
## byvar = Memory Baseline Amyloid 56 0.1286 [ 0.0574; 0.1986] 0.0626
## byvar = Memory Baseline Amyloid Tau 9 0.1332 [-0.0867; 0.3407] 0.0667
## byvar = Global Baseline Amyloid 69 0.0940 [ 0.0547; 0.1329] 0.0216
## byvar = Global Followup Amyloid 30 0.2626 [ 0.1139; 0.3998] 0.1630
## byvar = Global Baseline Amyloid Tau 15 0.0343 [-0.1567; 0.2229] 0.1083
## byvar = Global Baseline Tau 17 0.0917 [-0.0192; 0.2004] 0.0391
## byvar = Memory Followup Amyloid 20 0.1511 [ 0.0167; 0.2801] 0.0706
## byvar = Global Followup Tau 8 0.0823 [-0.0507; 0.2125] 0.0202
## byvar = Global Followup Amyloid Tau 6 0.2812 [-0.2770; 0.6975] 0.3010
## byvar = Memory Baseline Tau 11 0.0036 [-0.1745; 0.1814] 0.0612
## byvar = Memory Followup Tau 5 0.1256 [-0.2313; 0.4527] 0.0726
## byvar = Memory Followup Amyloid Tau 5 0.2687 [-0.4273; 0.7647] 0.3364
## byvar = Followup Amyloid 2 0.8739 [-1.0000; 1.0000] 1.5368
## byvar = Baseline Amyloid 1 0.1837 [-0.4086; 0.6672] --
## byvar = Global NA Amyloid Tau 1 NA --
## tau Q I^2
## byvar = Memory Baseline Amyloid 0.2501 502.72 89.1%
## byvar = Memory Baseline Amyloid Tau 0.2583 77.68 89.7%
## byvar = Global Baseline Amyloid 0.1469 403.15 83.6%
## byvar = Global Followup Amyloid 0.4038 602.04 95.3%
## byvar = Global Baseline Amyloid Tau 0.3291 178.93 92.7%
## byvar = Global Baseline Tau 0.1977 60.51 78.5%
## byvar = Memory Followup Amyloid 0.2657 220.70 91.4%
## byvar = Global Followup Tau 0.1421 30.61 83.7%
## byvar = Global Followup Amyloid Tau 0.5486 167.60 97.6%
## byvar = Memory Baseline Tau 0.2475 82.64 87.9%
## byvar = Memory Followup Tau 0.2694 22.72 86.8%
## byvar = Memory Followup Amyloid Tau 0.5800 179.29 97.8%
## byvar = Followup Amyloid 1.2397 12.73 92.1%
## byvar = Baseline Amyloid -- 0.00 --
## byvar = Global NA Amyloid Tau -- -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 11.44 14 0.6515
##
## Prediction intervals for subgroups:
## 95%-PI
## byvar = Memory Baseline Amyloid [-0.3603; 0.5621]
## byvar = Memory Baseline Amyloid Tau [-0.4757; 0.6558]
## byvar = Global Baseline Amyloid [-0.1990; 0.3715]
## byvar = Global Followup Amyloid [-0.5172; 0.8042]
## byvar = Global Baseline Amyloid Tau [-0.6060; 0.6477]
## byvar = Global Baseline Tau [-0.3311; 0.4838]
## byvar = Memory Followup Amyloid [-0.3989; 0.6211]
## byvar = Global Followup Tau [-0.2836; 0.4274]
## byvar = Global Followup Amyloid Tau [-0.8753; 0.9590]
## byvar = Memory Baseline Tau [-0.5265; 0.5316]
## byvar = Memory Followup Tau [-0.6784; 0.7927]
## byvar = Memory Followup Amyloid Tau [-0.9416; 0.9802]
## byvar = Followup Amyloid
## byvar = Baseline Amyloid
## byvar = Global NA Amyloid Tau
##
## Details on meta-analytical method:
## - Inverse variance 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
## - Fisher's z transformation of correlations##Forest plot
forest(
first,
print.I2.ci = TRUE,
digits.sd = 2,
layout = "subgroup",
#layout = "subgroup",
squaresize = 0.8,
plotwidth = unit(8, "cm"),
fontsize = 5,
addspace = FALSE,
xlab = "Fisher z",
calcwidth.hetstat = TRUE
)
#option 1 -all studies
# fun <- trimfill(first)
# funnel(
# fun,
# pch = ifelse(fun$trimfill, 1, 16),
# level = 0.9,
# comb.random = FALSE
# )
#
# funnel(first,
# main = "Standard Error")
# funnel(first,
# yaxis = "invvar")
# funnel(first,
# yaxis = "size",
# main = "Sampling size",
# title = "hsjs")#Memory ##Amyloid
###Memory amyloid baseline
data <- subset(data_full, table1 == 1 & exclude != 1)
data_memory_amyloid <-
subset(data, table1== "1" & data$memory_composite == "1" & data$amyloid == "1" & data$tau != "1" & data$cross_long == "C" )
MA <- metacor(
as.numeric(all_r),
#column with r values
data = data_memory_amyloid,
n = data_memory_amyloid$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(MA, digits = 2, pval=TRUE)## Number of studies combined: k = 56
##
## COR 95%-CI t p-value
## Random effects model 0.1286 [ 0.0574; 0.1986] 3.61 0.0007
## Prediction interval [-0.3603; 0.5621]
##
## Quantifying heterogeneity:
## tau^2 = 0.0626 [0.0379; 0.0980]; tau = 0.2501 [0.1948; 0.3131]
## I^2 = 89.1% [86.6%; 91.1%]; H = 3.02 [2.73; 3.35]
##
## Test of heterogeneity:
## Q d.f. p-value
## 502.72 55 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance 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
## - Fisher's z transformation of correlations####Funnel
funnel(
MA,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = MA)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 1.545 0.44 - 2.65 2.732 0.008479106
##
## Eggers' test indicates the presence of funnel plot asymmetry.Timmers T doi: 10.1016/j.pdpdt.2019.04.022 , Amsterdam, 107 NC , Florbetepan - checked Merrill DA doi: 10.3233/JAD-121903, 75 NC, FRS, FDDNP - checked Liguori doi: 10.1093/sleep/zsx011, 50 NC with, 35 with obstructuve sleep apnea, CSF ab42 - checked van Harten
####Memory amyloid baseline - no outliers
data <- subset(data_full, table1 == 1 & exclude != 1)
data_memory_amyloid <-
subset(data, table1== "1" & data$memory_composite == "1" & data$amyloid == "1" & data$tau != "1" & data$cross_long == "C" & data$outliers == "0")
MA_out <- metacor(
as.numeric(all_r),
#column with r values
data = data_memory_amyloid,
n = data_memory_amyloid$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(MA_out, digits = 2, pval=TRUE)## Number of studies combined: k = 52
##
## COR 95%-CI t p-value
## Random effects model 0.0852 [ 0.0441; 0.1260] 4.15 0.0001
## Prediction interval [-0.1850; 0.3434]
##
## Quantifying heterogeneity:
## tau^2 = 0.0180 [0.0052; 0.0251]; tau = 0.1341 [0.0720; 0.1586]
## I^2 = 76.3% [69.1%; 81.8%]; H = 2.05 [1.80; 2.34]
##
## Test of heterogeneity:
## Q d.f. p-value
## 214.83 51 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Fisher's z transformation of correlations####Funnel
funnel(
MA_out,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = MA_out)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 1.006 0.22 - 1.79 2.501 0.01571119
##
## Eggers' test indicates the presence of funnel plot asymmetry.###Memory amyloid longitudinal
data <- subset(data_full, table1 == 1 & exclude != 1)
data_memory_amyloidL <-
subset(data, table1== "1" & data$memory_composite == "1" & data$amyloid == "1" & data$tau != "1" & data$cross_long == "L")
MAL <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_memory_amyloidL,
n = data_memory_amyloidL$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(MAL, digits = 2, pval=TRUE)## Number of studies combined: k = 20
##
## COR 95%-CI t p-value
## Random effects model 0.1511 [ 0.0167; 0.2801] 2.35 0.0297
## Prediction interval [-0.3989; 0.6211]
##
## Quantifying heterogeneity:
## tau^2 = 0.0706 [0.0368; 0.1597]; tau = 0.2657 [0.1919; 0.3996]
## I^2 = 91.4% [88.1%; 93.8%]; H = 3.41 [2.90; 4.00]
##
## Test of heterogeneity:
## Q d.f. p-value
## 220.70 19 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Fisher's z transformation of correlations####Funnel
funnel(
MAL,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = MAL)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 1.946 -0.85 - 4.74 1.365 0.1890963
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.Farrell, doi: 10.1001/jamaneurol.2017.0892, 123 NC, florbetapir) van Harten
####Memory amyloid longitudinal - no outliers
data <- subset(data_full, table1 == 1 & exclude != 1)
data_memory_amyloidL <-
subset(data, table1== "1" & data$memory_composite == "1" & data$amyloid == "1" & data$tau != "1" & data$cross_long == "L" & data$outliers =="0")
MAL_out <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_memory_amyloidL,
n = data_memory_amyloidL$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(MAL_out, digits = 2, pval=TRUE)## Number of studies combined: k = 16
##
## COR 95%-CI t p-value
## Random effects model 0.0487 [-0.0032; 0.1003] 2.00 0.0641
## Prediction interval [-0.1136; 0.2085]
##
## Quantifying heterogeneity:
## tau^2 = 0.0052 [0.0009; 0.0136]; tau = 0.0719 [0.0295; 0.1166]
## I^2 = 63.4% [37.3%; 78.6%]; H = 1.65 [1.26; 2.16]
##
## Test of heterogeneity:
## Q d.f. p-value
## 40.94 15 0.0003
##
## Details on meta-analytical method:
## - Inverse variance 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
## - Fisher's z transformation of correlations####Funnel
funnel(
MAL_out,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = MAL_out)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 0.483 -1.14 - 2.11 0.581 0.5701537
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.##Amyloid and tau ###Memory amyloid and tau baseline
data <- subset(data_full, table1 == 1 & exclude != 1)
data_memory_tau <-
subset(data, table1== "1" & data$memory_composite == "1" & data$tau == "1" & data$amyloid == "1" & data$cross_long == "C")
MT <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_memory_tau ,
n = data_memory_tau $total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(MT, digits = 2, pval=TRUE)## Number of studies combined: k = 9
##
## COR 95%-CI t p-value
## Random effects model 0.1332 [-0.0867; 0.3407] 1.40 0.1994
## Prediction interval [-0.4757; 0.6558]
##
## Quantifying heterogeneity:
## tau^2 = 0.0667 [0.0256; 0.2697]; tau = 0.2583 [0.1600; 0.5193]
## I^2 = 89.7% [82.7%; 93.9%]; H = 3.12 [2.40; 4.04]
##
## Test of heterogeneity:
## Q d.f. p-value
## 77.68 8 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Fisher's z transformation of correlations####Funnel
funnel(
MT,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = MT)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 1.629 -4.69 - 7.94 0.506 0.6285625
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.####Memory amyloid and tau baseline - no outliers
data <- subset(data_full, table1 == 1 & exclude != 1)
data_memory_tau <-
subset(data, table1== "1" & data$memory_composite == "1" & data$tau == "1" & data$amyloid == "1" & data$cross_long == "C" & data$outliers =="0")
MT_out <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_memory_tau ,
n = data_memory_tau $total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(MT_out, digits = 2, pval=TRUE)## Number of studies combined: k = 7
##
## COR 95%-CI t p-value
## Random effects model 0.1302 [ 0.0123; 0.2445] 2.70 0.0356
## Prediction interval [-0.1396; 0.3820]
##
## Quantifying heterogeneity:
## tau^2 = 0.0088 [0.0000; 0.0757]; tau = 0.0938 [0.0000; 0.2752]
## I^2 = 0.0% [0.0%; 67.8%]; H = 1.00 [1.00; 1.76]
##
## Test of heterogeneity:
## Q d.f. p-value
## 5.44 6 0.4892
##
## Details on meta-analytical method:
## - Inverse variance method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Fisher's z transformation of correlations####Funnel
funnel(
MT_out,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = MT_out)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 0.602 -1.52 - 2.73 0.556 0.6020448
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.McMillan - Parkisnons
###Memory amyloid tau longitudinal
data <- subset(data_full, table1 == 1 & exclude != 1)
data_memory_tauL <-
subset(data, table1== "1" & data$memory_composite == "1" & data$tau == "1" & data$amyloid == "1" & data$cross_long == "L")
MTL <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_memory_tauL ,
n = data_memory_tauL$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(MTL, digits = 2, pval=TRUE)## Number of studies combined: k = 5
##
## COR 95%-CI t p-value
## Random effects model 0.2687 [-0.4273; 0.7647] 1.04 0.3551
## Prediction interval [-0.9416; 0.9802]
##
## Quantifying heterogeneity:
## tau^2 = 0.3364 [0.1162; 2.8508]; tau = 0.5800 [0.3409; 1.6884]
## I^2 = 97.8% [96.5%; 98.6%]; H = 6.69 [5.33; 8.42]
##
## Test of heterogeneity:
## Q d.f. p-value
## 179.29 4 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Fisher's z transformation of correlations####Funnel
funnel(
MTL,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = MTL)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 1.684 -29.5 - 32.86 0.106 0.922387
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.####Memory amyloid tau longitudinal - no outliers
data <- subset(data_full, table1 == 1 & exclude != 1)
data_memory_tauL <-
subset(data, table1== "1" & data$memory_composite == "1" & data$tau == "1" & data$amyloid == "1" & data$cross_long == "L"& data$outliers =="0")
MTL_out <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_memory_tauL ,
n = data_memory_tauL$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(MTL_out, digits = 2, pval=TRUE)## Number of studies combined: k = 4
##
## COR 95%-CI t p-value
## Random effects model 0.0093 [-0.0314; 0.0499] 0.72 0.5211
## Prediction interval [-0.0539; 0.0723]
##
## Quantifying heterogeneity:
## tau^2 < 0.0001 [0.0000; 0.0015]; tau = 0.0072 [0.0000; 0.0393]
## I^2 = 0.0%; H = 1.00
##
## Test of heterogeneity:
## Q d.f. p-value
## 0.25 3 0.9685
##
## Details on meta-analytical method:
## - Inverse variance 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
## - Fisher's z transformation of correlations####Funnel
funnel(
MTL_out,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = MTL_out)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 0.075 -1.36 - 1.51 0.102 0.9282354
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.#Global ##Amyloid ###Global amyloid baseline
data <- subset(data_full, table1 == 1 & exclude != 1)
data_global_amyloid <-
subset(data, table1== "1" & data$global_composite == "1" & data$amyloid == "1" & data$tau != "0" & data$cross_long == "C")
GA <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_global_amyloid,
n = data_global_amyloid$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(GA, digits = 2, pval=TRUE)## Number of studies combined: k = 15
##
## COR 95%-CI t p-value
## Random effects model 0.0343 [-0.1567; 0.2229] 0.38 0.7074
## Prediction interval [-0.6060; 0.6477]
##
## Quantifying heterogeneity:
## tau^2 = 0.1083 [0.0513; 0.2993]; tau = 0.3291 [0.2264; 0.5471]
## I^2 = 92.7% [89.5%; 95.0%]; H = 3.71 [3.08; 4.46]
##
## Test of heterogeneity:
## Q d.f. p-value
## 178.93 13 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance 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
## - Fisher's z transformation of correlations####Funnel
funnel(
GA,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = GA) ## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 1.071 -3.28 - 5.42 0.483 0.6379013
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.Timmers T doi: 10.1016/j.pdpdt.2019.04.022 , Amsterdam, 107 NC , Florbetepan - checked Merrill DA doi: 10.3233/JAD-121903, 75 NC, FRS, FDDNP - checked Roberts, Tolboom, 9 NC individuals
####Global amyloid baseline - no outliers
data <- subset(data_full, table1 == 1 & exclude != 1)
data_global_amyloid <-
subset(data, table1== "1" & data$global_composite == "1" & data$amyloid == "1" & data$tau != "0" & data$cross_long == "C" & data$outliers =="0")
GA_out <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_global_amyloid,
n = data_global_amyloid$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(GA_out, digits = 2, pval=TRUE)## Number of studies combined: k = 13
##
## COR 95%-CI t p-value
## Random effects model 0.0055 [-0.2093; 0.2199] 0.06 0.9568
## Prediction interval [-0.6517; 0.6580]
##
## Quantifying heterogeneity:
## tau^2 = 0.1168 [0.0524; 0.3600]; tau = 0.3417 [0.2289; 0.6000]
## I^2 = 92.7% [89.1%; 95.1%]; H = 3.70 [3.03; 4.53]
##
## Test of heterogeneity:
## Q d.f. p-value
## 150.79 11 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Fisher's z transformation of correlations####Funnel
funnel(
GA_out,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = GA_out) ## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 0.95 -3.46 - 5.35 0.423 0.6815968
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.###Global amyloid longitudinal
data <- subset(data_full, table1 == 1 & exclude != 1)
data_global_amyloidL <-
subset(data, table1== "1" & data$global_composite == "1" & data$amyloid == "1" & data$tau != "0" & data$cross_long == "L")
GAL <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_global_amyloidL,
n = data_global_amyloidL$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
#The summary
) #print grouping variable
summary(GAL, digits = 2, pval=TRUE)## Number of studies combined: k = 6
##
## COR 95%-CI t p-value
## Random effects model 0.2812 [-0.2770; 0.6975] 1.30 0.2517
## Prediction interval [-0.8753; 0.9590]
##
## Quantifying heterogeneity:
## tau^2 = 0.3010 [0.1035; 2.5583]; tau = 0.5486 [0.3217; 1.5995]
## I^2 = 97.6% [96.2%; 98.5%]; H = 6.47 [5.12; 8.18]
##
## Test of heterogeneity:
## Q d.f. p-value
## 167.60 4 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Fisher's z transformation of correlations####Funnel
funnel(
GAL,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = GAL)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 4.664 -14.54 - 23.87 0.476 0.6665892
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.Kawas, 13 NC Farrell, doi: 10.1001/jamaneurol.2017.0892, 123 NC, florbetapir) Timmers T doi: 10.1016/j.pdpdt.2019.04.022 , Amsterdam, 107 NC , Florbetepan - checked Lilamnad van Harten Bilgel, doi: 10.1093/brain/awy150, 171 NC BLSA, CVR thresholdwas 1.062 which is the lowest for DVR taht we have.
####Global amyloid longitudinal - no outliers
data <- subset(data_full, table1 == 1 & exclude != 1)
data_global_amyloidL <-
subset(data, table1== "1" & data$global_composite == "1" & data$amyloid == "1" & data$tau != "0" & data$cross_long == "L" & data$outliers ==0)
GAL_out <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_global_amyloidL,
n = data_global_amyloidL$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
) #print grouping variable
summary(GAL_out, digits = 2, pval=TRUE)## Number of studies combined: k = 5
##
## COR 95%-CI t p-value
## Random effects model 0.0602 [ 0.0131; 0.1071] 3.55 0.0239
## Prediction interval [-0.0145; 0.1343]
##
## Quantifying heterogeneity:
## tau^2 = 0.0003 [0.0000; 0.0144]; tau = 0.0162 [0.0000; 0.1201]
## I^2 = 0.0% [0.0%; 51.3%]; H = 1.00 [1.00; 1.43]
##
## Test of heterogeneity:
## Q d.f. p-value
## 0.94 3 0.8148
##
## Details on meta-analytical method:
## - Inverse variance 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
## - Fisher's z transformation of correlations####Funnel
funnel(
GAL_out,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = GAL_out)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## -1.187 -2.21 - -0.16 -2.276 0.1505914
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.##Amyloid and tau ###Global amyloid tau baseline
data <- subset(data_full, table1 == 1 & exclude != 1)
data_global_tau <-
subset(data, table1== "1" & data$global_composite == "1" & data$tau == "1" & data$amyloid != "1" & data$cross_long == "C")
GT <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_global_tau,
n = data_global_tau$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(GT, digits = 2, pval=TRUE)## Number of studies combined: k = 17
##
## COR 95%-CI t p-value
## Random effects model 0.0917 [-0.0192; 0.2004] 1.75 0.0985
## Prediction interval [-0.3311; 0.4838]
##
## Quantifying heterogeneity:
## tau^2 = 0.0391 [0.0112; 0.1206]; tau = 0.1977 [0.1059; 0.3473]
## I^2 = 78.5% [64.5%; 87.0%]; H = 2.16 [1.68; 2.77]
##
## Test of heterogeneity:
## Q d.f. p-value
## 60.51 13 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance 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
## - Fisher's z transformation of correlations####Funnel
funnel(
GT,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = GT)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## -0.937 -2.56 - 0.69 -1.132 0.2799272
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.van harten
####Global amyloid tau baseline - no outliers
data <- subset(data_full, table1 == 1 & exclude != 1)
data_global_tau <-
subset(data, table1== "1" & data$global_composite == "1" & data$tau == "1" & data$amyloid != "1" & data$cross_long == "C" & data$outliers =="0")
GT_out <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_global_tau,
n = data_global_tau$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(GT_out, digits = 2, pval=TRUE)## Number of studies combined: k = 15
##
## COR 95%-CI t p-value
## Random effects model 0.0441 [-0.0620; 0.1492] 0.89 0.3883
## Prediction interval [-0.3364; 0.4122]
##
## Quantifying heterogeneity:
## tau^2 = 0.0308 [0.0013; 0.1141]; tau = 0.1756 [0.0366; 0.3377]
## I^2 = 53.9% [11.4%; 76.0%]; H = 1.47 [1.06; 2.04]
##
## Test of heterogeneity:
## Q d.f. p-value
## 23.85 11 0.0134
##
## Details on meta-analytical method:
## - Inverse variance method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Fisher's z transformation of correlations####Funnel
funnel(
GT_out,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = GT_out)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 0.231 -1.72 - 2.18 0.233 0.8208065
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.###Global amyloid tau longitudinal
data <- subset(data_full, table1 == 1 & exclude != 1)
data_global_tauL <-
subset(data, table1== "1" & data$global_composite == "1" & data$tau == "1" & data$amyloid != "1" & data$cross_long == "L")
GTL <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_global_tauL,
n = data_global_tauL$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(GTL, digits = 2, pval=TRUE)## Number of studies combined: k = 8
##
## COR 95%-CI t p-value
## Random effects model 0.0823 [-0.0507; 0.2125] 1.46 0.1865
## Prediction interval [-0.2836; 0.4274]
##
## Quantifying heterogeneity:
## tau^2 = 0.0202 [0.0054; 0.1423]; tau = 0.1421 [0.0735; 0.3773]
## I^2 = 83.7% [65.9%; 92.2%]; H = 2.47 [1.71; 3.58]
##
## Test of heterogeneity:
## Q d.f. p-value
## 30.61 5 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Sidik-Jonkman estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Fisher's z transformation of correlations####Funnel
funnel(
GTL,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = GTL)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 2.367 -4.06 - 8.79 0.722 0.5102399
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.Xiong
####Global amyloid tau longitudinal - no ouliers
data <- subset(data_full, table1 == 1 & exclude != 1)
data_global_tauL <-
subset(data, table1== "1" & data$global_composite == "1" & data$tau == "1" & data$amyloid != "1" & data$cross_long == "L" & data$outliers=="0")
GTL_out <- metacor(
as.numeric(all_r),
#column with r values
#pooled_se, #this tells R to use the seTE column to retrieve the standard error for each study
data = data_global_tauL,
n = data_global_tauL$total_nc,
#sample sizes
studlab = paste(author),
# labels for each study
comb.fixed = FALSE,
#Whether to use a fixed-effects model
comb.random = TRUE,
#Whether to use a random-effects model
method.tau = "SJ",
#Which estimator to use for the between-study variance
hakn = TRUE,
#Which estimator to use for the between-study variance
prediction = TRUE,
#Whether to print a prediction interval for the effect of future studies based on present evidence
sm = "ZCOR"
)
summary(GTL_out, digits = 2, pval=TRUE)## Number of studies combined: k = 7
##
## COR 95%-CI t p-value
## Random effects model 0.0672 [-0.0896; 0.2208] 1.05 0.3349
## Prediction interval [-0.3499; 0.4621]
##
## Quantifying heterogeneity:
## tau^2 = 0.0242 [0.0062; 0.2317]; tau = 0.1556 [0.0787; 0.4814]
## I^2 = 86.1% [69.7%; 93.7%]; H = 2.68 [1.82; 3.97]
##
## Test of heterogeneity:
## Q d.f. p-value
## 28.82 4 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance 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
## - Fisher's z transformation of correlations####Funnel
funnel(
GTL_out,
yaxis= "se",
legend = TRUE,
main = "Standard Error",
studlab = TRUE,
xlim = c(-1,1),
level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour
## [1] "#808080" "#B3B3B3" "#E6E6E6"dmetar::eggers.test(x = GTL_out)## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 2.017 -5.98 - 10.02 0.494 0.6552297
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.