Meta Analysis - Cognition, Amyloid, Tau
1 Libraries
library(meta)
library(metasens)
library(readxl)
library(metafor)
library(DescTools)
library(psych)
library(tidyverse)
library(metaviz)
library(table1)
library(stargazer)2 Data
#download data and subset it
data_full <- read_xlsx("/Users/carolinaferreiraatuesta/Documents/H-LAND/Meta Analysis/representative_meta_analysis2.xlsx", sheet = "representatives_global_memory")
data <- subset(data_full, table1 ==1, exclude !=0 ) 3 Tables 1
data_table1 <- subset(data_full, table1 == 1, exclude != 0)
#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$cross_long)3.1 Table descriptive amyloid
3.2 Table descriptive tau
3.3 Table summary included papers
newtab <-
data.frame(
data_table1$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
)
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 |
|---|---|---|---|---|---|---|---|---|---|
| Gu | 2012 | WHICAP | Ab42 | C | No | Yes | 813 | NA | 0.0670742 |
| Horn MM | 2018 | Dallas | PiB | C | No | Yes | 195 | NA | 0.9584521 |
| Hoscheidt SM | 2016 | IMPACT | ab42/ab40/ptau/ttau | C | No | Yes | 70 | NA | 0.0964365 |
| Hosokawa C | 2014 | Osakasayama, Japan, | PiB | C | No | No | 90 | 0.76280172922419232 | NA |
| Adamczuk K | 2014 | Leuven | Flutemetamol | C | No | Yes | 56 | 0.22718473369882641 | NA |
| Adamczuk K | 2014 | Leuven | Flutemetamol | C | No | Yes | 56 | NA | 0.2150714 |
| Aizenstein HJ | 2008 | PITT | PiB | C | No | No | 38 | -0.12150337320566384 | NA |
| Aizenstein HJ | 2008 | PITT | PiB | C | No | No | 34 | NA | -0.3791941 |
| Pomara N | 2005 | Nathan Kline | Ab42 | L | No | No | 34 | -0.35 | NA |
| Radanovic M | 2019 | Brazil | ab42/ptau | C | No | Yes | 54 | 0.20699999999999999 | NA |
| Radanovic M | 2019 | Brazil | Ab42 | C | No | Yes | 54 | 0.21099999999999999 | NA |
| Radanovic M | 2019 | Brazil | ptau | C | No | Yes | 54 | -0.28399999999999997 | NA |
| Rentz DM | 2016 | HABS | PiB | C | No | Yes | 133 | -9.0226611881499053E-2 | NA |
| Rentz DM | 2016 | HABS | T807 | C | No | Yes | 133 | -0.15079608745344522 | NA |
| Rentz DM | 2016 | HABS | PiB, T807 | C | No | Yes | 133 | -2.2839243131540413E-2 | NA |
| Rentz DM | 2016 | HABS | PiB, T807 | C | No | Yes | 133 | -0.11842018864844951 | NA |
| Rentz DM | 2010 | HABS | PiB | C | No | Yes | 66 | NA | 0.0000000 |
| Resnick SM | 2010 | BLSA | PiB | L | Yes | Yes | 51 | NA | -0.1386750 |
| Resnick SM | 2010 | BLSA | PiB | L | Yes | Yes | 51 | -0.2055566128505921 | NA |
| Roberts RO | 2018 | MCSA | PiB | L | No | Yes | 1492 | 3.6762938557297759E-2 | NA |
| Roberts RO | 2018 | MCSA | PiB | L | No | Yes | 1492 | NA | 0.0385758 |
| Roberts RO | 2018 | MCSA | PiB | C | No | No | 1492 | 0.2466142594127082 | NA |
| Roberts RO | 2018 | MCSA | PiB | C | No | No | 1492 | NA | 0.1638525 |
| Roe CM | 2013 | Knight | Florbetapir, ptau | L | No | Yes | 430 | 5.4877517454045184E-2 | NA |
| Roe CM | 2013 | Knight | Florbetapir, ab42/ptau | L | No | Yes | 430 | 9.2646696819342747E-2 | NA |
| Roe CM | 2013 | Knight | Florbetapir, ab42 | L | No | Yes | 430 | 6.7820743484453855E-2 | NA |
| Rosenberg PB | 2013 | John Hopkns | Florbetapir | C | No | No | 15 | NA | -0.1500000 |
| Rosenberg PB | 2013 | John Hopkns | Florbetapir | C | No | No | 15 | 0.16500000000000001 | NA |
| Sanabria A | 2018 | FACEHBI | PiB | C | No | Yes | 200 | NA | 0.0000000 |
| Schindler SE | 2017 | Knight | Ab42 | C | Yes | Yes | 233 | NA | 0.0695833 |
| Schindler SE | 2017 | Knight | Ab42 | C | Yes | Yes | 233 | 7.5499999999999998E-2 | NA |
| Schindler SE | 2017 | Knight | ptau | C | Yes | Yes | 233 | NA | -0.0282500 |
| Schindler SE | 2017 | Knight | ptau | C | Yes | Yes | 233 | -2.5000000000000005E-3 | NA |
| Schindler SE | 2017 | Knight | ab42/ptau | C | Yes | Yes | 233 | NA | -0.0950833 |
| Schindler SE | 2017 | Knight | ab42/ptau | C | Yes | Yes | 233 | -6.9500000000000006E-2 | NA |
| Schindler SE | 2017 | Knight | Ab42 | L | Yes | Yes | 233 | 4.0499999999999994E-2 | NA |
| Schindler SE | 2017 | Knight | Ab42 | L | Yes | Yes | 233 | NA | 0.0110000 |
| Schindler SE | 2017 | Knight | ptau | L | Yes | Yes | 233 | -2.3E-2 | NA |
| Schindler SE | 2017 | Knight | ptau | L | Yes | Yes | 233 | NA | -0.0175000 |
| Schindler SE | 2017 | Knight | ab42/ptau | L | Yes | Yes | 233 | -7.0500000000000007E-2 | NA |
| Schindler SE | 2017 | Knight | ab42/ptau | L | Yes | Yes | 233 | NA | -0.0210000 |
| Sala-Llonch R | 2017 | Norway | Ab42 | C | No | No | 89 | NA | -0.0524737 |
| Sala-Llonch R | 2017 | Norway | Ab42 | C | No | No | 89 | -4.6719648158868775E-2 | NA |
| Sala-Llonch R | 2017 | Norway | Ab42 | L | No | No | 89 | 0.11022825179754911 | NA |
| Sierra-Rio A | 2015 | Barcelona | ab42/ptau | C | No | No | 55 | 4.7520352686965583E-2 | NA |
| Song Z | 2016 | DLBS | Florbetapir | C | No | No | 82 | NA | 0.1287696 |
| Song Z | 2016 | DLBS | Florbetapir | C | No | No | 82 | 0.12252356361148627 | NA |
| Song Z | 2015 | UCSF | Florbetapir | C | No | Yes | 50 | NA | -0.0040000 |
| Song Z | 2015 | UCSF | Florbetapir | C | No | Yes | 50 | -0.02 | NA |
| Stomrud E | 2010 | CMRU | Ab42 | C | No | Yes | 37 | 7.9000000000000001E-2 | NA |
| Stomrud E | 2010 | CMRU | ptau | C | No | Yes | 37 | -3.2000000000000001E-2 | NA |
| Stomrud E | 2010 | CMRU | ptau | L | No | Yes | 37 | 0.06 | NA |
| Stomrud E | 2010 | CMRU | Ab42 | L | No | Yes | 37 | -0.23 | NA |
| Stomrud E | 2010 | CMRU | ptau | C | No | Yes | 37 | NA | -0.1310000 |
| Stomrud E | 2010 | CMRU | ptau | L | No | Yes | 37 | NA | -0.2655000 |
| Timmers T | 2019 | SCIENCe | Florbetapir | C | No | Yes | 107 | -0.1612406781552356 | NA |
| Timmers T | 2019 | SCIENCe | Florbetapir | C | No | Yes | 107 | NA | -0.0573891 |
| Timmers T | 2019 | SCIENCe | Florbetapir | L | No | Yes | 107 | -3.478145606686512E-2 | NA |
| Timmers T | 2019 | SCIENCe | Florbetapir | L | No | Yes | 107 | NA | -0.1288514 |
| Storandt M | 2009 | Knight | PiB | C | No | No | 135 | 0.1209121696163073 | NA |
| Storandt M | 2009 | Knight | PiB | L | Yes | Yes | 135 | 4.10690352043829E-3 | NA |
| Storandt M | 2009 | Knight | PiB | L | Yes | Yes | 135 | NA | 0.0041069 |
| Tardif CL | 2017 | INTREPAD | Ab42 | C | No | No | 46 | -0.11074397642818709 | NA |
| Tardif CL | 2017 | INTREPAD | ttau | C | No | No | 46 | 0.35701445353993361 | NA |
| Teipel SJ | 2017 | INSIGHT | Florbetapir | C | No | Yes | 318 | -0.11 | NA |
| Teipel SJ | 2017 | INSIGHT | Florbetapir | C | No | Yes | 318 | NA | -0.1100000 |
| van Bergen JMG | 2018 | zurich | Flutemetamol | C | No | Yes | 116 | -4.4784813577462355E-2 | NA |
| van Harten AC | 2013 | Amsterdam | Ab42 | C | No | No | 132 | -1.4504945216194559E-2 | NA |
| van Harten AC | 2013 | Amsterdam | ptau | C | No | No | 132 | NA | -0.0651405 |
| van Harten AC | 2013 | Amsterdam | ptau | C | No | No | 132 | -4.3478260869565216E-2 | NA |
| van Harten AC | 2013 | Amsterdam | ab42/ttau | C | No | No | 132 | NA | -0.0579284 |
| van Harten AC | 2013 | Amsterdam | ab42/ttau | C | No | No | 132 | 0 | NA |
| van Harten AC | 2013 | Amsterdam | Ab42 | C | No | No | 132 | NA | -0.1400443 |
| van Harten AC | 2013 | Amsterdam | Ab42 | L | Yes | No | 132 | -0.14356203435505357 | NA |
| van Harten AC | 2013 | Amsterdam | ptau | L | Yes | No | 132 | NA | -0.0434783 |
| van Harten AC | 2013 | Amsterdam | ptau | L | Yes | No | 132 | -1.4504945216194559E-2 | NA |
| van Harten AC | 2013 | Amsterdam | ab42/ttau | L | Yes | No | 132 | NA | -0.1505812 |
| van Harten AC | 2013 | Amsterdam | ab42/ttau | L | Yes | No | 132 | -0.14356203435505357 | NA |
| van Harten AC | 2013 | Amsterdam | Ab42 | L | Yes | No | 132 | NA | -0.0579284 |
| Villemagne VL | 2014 | AIBL | 18F-THK523 | C | No | No | 10 | NA | 0.0000000 |
| Visser PJ | 2009 | DESCRIPA | ab42/ttau | L | No | Yes | 58 | 1.4240069406182738E-2 | NA |
| Visser PJ | 2009 | DESCRIPA | ab42/ttau | C | No | Yes | 60 | -5.7632212122421742E-2 | NA |
| Visser PJ | 2009 | DESCRIPA | ab42 | L | No | Yes | 58 | NA | 0.0449278 |
| Xiong C | 2016 | ACS | ptau | C | No | Yes | 209 | -0.11 | NA |
| Xiong C | 2016 | ACS | PiB | C | No | Yes | 209 | -0.15 | NA |
| Xiong C | 2016 | ACS | ptau | L | No | Yes | 209 | -0.33 | NA |
| Xiong C | 2016 | ACS | PiB | L | No | Yes | 209 | -0.27 | NA |
| Xiong C | 2016 | ACS | Ab42 | C | No | Yes | 209 | -0.02 | NA |
| Xiong C | 2016 | ACS | Ab42 | L | No | Yes | 209 | 0.22 | NA |
| Huang KL | 2018 | Taiwan | Florbetapir | C | No | Yes | 11 | NA | -0.5600000 |
| Insel PS | 2019 | BioFINDER | ab42/ab40 | C | No | Yes | 329 | 4.5815407865608927E-2 | NA |
| Janelidze S | 2018 | Mälmo Diet Cancer Study +BioFINDER | ab42/ab40 | C | No | Yes | 508 | 0.11616246899850097 | NA |
| Jansen WJ | 2017 | ABS | PiB | C | No | Yes | 2908 | 7.0922545772955314E-2 | NA |
| Jansen WJ | 2017 | ABS | PiB | C | No | Yes | 2908 | NA | 0.0884409 |
| Kang JM | 2017 | Gachon, Korea | THK5351 | C | No | Yes | 43 | NA | -0.0677500 |
| Kato M | 2012 | japan | Florbetapir | C | No | Yes | 100 | 0.43692104500000001 | NA |
| Kawas CH | 2012 | 90 + study | Florbetapir | C | No | No | 13 | 0.30979504447013906 | NA |
| Kawas CH | 2012 | 90 + study | Florbetapir | C | No | No | 13 | NA | -0.2974385 |
| Kawas CH | 2012 | 90 + study | Florbetapir | L | Yes | No | 7 | 0.95402399962294337 | NA |
| Kawas CH | 2012 | 90 + study | Florbetapir | L | Yes | No | 6 | NA | 0.3272785 |
| Kemppainen N | 2017 | FINGER | PiB | C | No | Yes | 48 | 0.2069809037440738 | NA |
| Kemppainen N | 2017 | FINGER | PiB | C | No | Yes | 48 | NA | -0.0148763 |
| 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 | Flutemetamol | C | No | Yes | 188 | -5.2024094995183782E-2 | NA |
| Konijnenberg E | 2019 | European Information Framework for AD-PreclinAD | ab42/ab40 | C | No | Yes | 126 | NA | 0.0345742 |
| Konijnenberg E | 2019 | European Information Framework for AD-PreclinAD | ab42/ab40 | C | No | Yes | 126 | 5.3376051385375656E-2 | NA |
| Kristofikova Z | 2014 | Czech Republic. | Ab42 | C | No | No | 15 | -0.17199999999999999 | NA |
| Kristofikova Z | 2014 | Czech Republic. | ptau | C | No | No | 15 | -0.44500000000000001 | NA |
| Lafirdeen ASM | 2019 | Multicenter France | Ab42 | C | No | Yes | 3562 | 0.22187885542906988 | NA |
| Leahey TM | 2008 | Kent USA | Ab40 | C | No | Yes | 35 | -0.34 | NA |
| Liguori C | 2017 | Rome | ab40/ptau/ttau | C | No | No | 50 | NA | 0.7250000 |
| Lilamand M | 2019 | MAPT | Florbetapir | C | No | No | 269 | 0.10568077493153641 | NA |
| Lilamand M | 2019 | MAPT | Florbetapir | L | Yes | Yes | 269 | -0.69044882565599996 | NA |
| Lilamand M | 2019 | MAPT | Florbetapir | L | Yes | Yes | 269 | NA | -0.5013711 |
| Lim YY | 2015 | Rhode Island | Florbetapir | L | No | No | 63 | 6.0604144036885937E-2 | NA |
| Lim YY | 2015 | Rhode Island | Florbetapir | C | No | No | 63 | -4.5207787830456279E-2 | NA |
| Lim YY | 2015 | Rhode Island | Florbetapir | C | No | No | 63 | NA | 0.1836993 |
| Llado-Saz S | 2015 | Seville | Florbetapir | C | No | Yes | 120 | NA | 0.0285706 |
| Lu K | 2019 | DRC-UK | Florbetapir | C | No | Yes | 502 | -0.32500000000000001 | NA |
| Lu K | 2019 | DRC-UK | Florbetapir | C | Yes | Yes | 502 | NA | -0.1200000 |
| Martikainen IK | 2018 | Finnish Geriatric Intervention Study | PiB | C | No | Yes | 40 | 3.0417779310137852E-2 | NA |
| Martikainen IK | 2018 | Finnish Geriatric Intervention Study | PiB | C | No | Yes | 40 | NA | -0.1323262 |
| McMillan CT | 2016 | PPMI | ab42/ptau/ttau | C | No | Yes | 174 | 0.37270369729452846 | NA |
| McMillan CT | 2016 | PPMI | Ab42 | C | No | Yes | 174 | NA | -0.3465076 |
| Mecca AP | 2017 | Yale | PiB | C | No | Yes | 45 | NA | 0.1270000 |
| Aschenbrenner AJ | 2014 | ADRC | ptau | C | No | Yes | 113 | NA | -0.1300000 |
| Aschenbrenner AJ | 2014 | ADRC | Ab42 | C | No | Yes | 113 | NA | 0.0650000 |
| Aschenbrenner AJ | 2014 | ADRC | PiB | C | No | Yes | 113 | NA | -0.0550000 |
| 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 |
| Besson FL | 2015 | IMAP | 0 | C | No | No | 54 | -0.131025450776667 | NA |
| Besson FL | 2015 | IMAP | 0 | C | No | No | 54 | NA | -0.1110823 |
| Bilgel M | 2018 | BLSA | PiB | L | Yes | Yes | 127 | NA | -0.0156667 |
| Bilgel M | 2018 | BLSA | PiB | L | Yes | No | 127 | −0.073 | NA |
| Casaletto KB | 2017 | Wisconsin ADRC | ptau | C | No | No | 132 | NA | -0.1400000 |
| Casaletto KB | 2017 | Wisconsin ADRC | Ab42 | C | No | No | 132 | NA | -0.0200000 |
| Chatterjee P | 2019 | KARVIAH | Florbetaben | C | No | No | 100 | -0.1245904061987721 | NA |
| Cosentino SA | 2010 | WHICAP | Ab42 | L | No | Yes | 481 | -0.02 | NA |
| Cosentino SA | 2010 | WHICAP | ptau | L | No | Yes | 481 | 0.01 | NA |
| Cosentino SA | 2010 | WHICAP | Ab42 | L | Yes | Yes | 481 | NA | -0.0187500 |
| Donohue MC | 2017 | ADNI | Florbetapir, PiB | C | No | No | 445 | 0.15781752754953882 | NA |
| Donohue MC | 2017 | ADNI | Florbetapir, PiB | L | Yes | Yes | 445 | 0.25 | NA |
| Donohue MC | 2017 | ADNI | Florbetapir, PiB | L | Yes | Yes | 445 | NA | 0.2400000 |
| Donohue MC | 2017 | ADNI | Florbetapir, PiB | L | Yes | Yes | 445 | -0.23 | NA |
| Doraiswamy PM | 2014 | cohort | Florbetapir | C | No | No | 69 | -9.5012822661194873E-3 | NA |
| Doraiswamy PM | 2014 | cohort | Florbetapir | C | No | No | 69 | NA | 0.2334509 |
| Doraiswamy PM | 2014 | cohort | Florbetapir | L | Yes | Yes | 69 | -0.2204826970771171 | NA |
| Doraiswamy PM | 2014 | cohort | Florbetapir | L | Yes | Yes | 69 | NA | 0.8284213 |
| Dubois B | 2018 | INSIGHT | Florbetapir | C | No | Yes | 318 | 0.12137734024004183 | NA |
| Ecay-Torres M | 2018 | GAP | Ab42 | C | No | Yes | 238 | 0 | NA |
| Farrell ME | 2017 | DLBS | Florbetapir | L | Yes | No | 123 | NA | -0.1212977 |
| Farrell ME | 2017 | DLBS | Florbetapir | L | Yes | No | 123 | -9.2620498947187713E-2 | NA |
| Farrell ME | 2017 | DLBS | Florbetapir | L | No | No | 123 | -1.3382959870743005E-2 | NA |
| Franzmeier N | 2018 | DELCODE | Florbetapir | C | No | No | 49 | 0.26130499504717453 | NA |
| Franzmeier N | 2018 | DELCODE | Florbetapir | C | No | Yes | 49 | NA | 0.1203751 |
| Gangishetti U | 2018 | Emory, Penn, Washu | Florbetapir | C | No | No | 44 | 0.10087105160045133 | NA |
| Haapalinna F | 2018 | Finland | Ab42 | C | No | No | 57 | NA | -0.2860000 |
| Hamelin L | 2018 | IMABio3 | PiB | L | No | No | 17 | 0.16728214978760356 | NA |
| Hamelin L | 2018 | IMABio3 | PiB | C | No | No | 17 | 0.13962189511877476 | NA |
| Hamelin L | 2018 | IMABio3 | PiB | L | No | No | 17 | NA | 0.2762891 |
| Zhao Y | 2017 | GEM | PiB | C | No | No | 175 | NA | 0.0819069 |
| Yaffe K | 2011 | Healthy ABC | Ab42 | C | No | No | 658 | -3.2368919736014842E-2 | NA |
| Yaffe K | 2011 | Healthy ABC | Ab42 | L | No | No | 658 | -0.11125370080159087 | NA |
| Yaffe K | 2011 | Healthy ABC | ab42/ab40 | C | No | No | 659 | 1.9852102269772645E-2 | NA |
| Yaffe K | 2011 | Healthy ABC | ab42/ab40 | L | No | No | 659 | -8.0063087950813211E-2 | NA |
| Meng Y | 2015 | Peking | APL1b28 | C | No | No | 35 | 2.5055555555555539E-2 | NA |
| Merrill DA | 2013 | UCLA | FDDNP | C | No | Yes | 75 | 0 | NA |
| Merrill DA | 2013 | UCLA | FDDNP | C | No | Yes | 75 | NA | 0.0000000 |
| Mielke MM | 2017 | MCSA | PiB | L | Yes | Yes | 115 | NA | 0.0058281 |
| Mielke MM | 2017 | MCSA | PiB | L | Yes | Yes | 115 | 1.1655518211812148E-2 | NA |
| Mielke MM | 2012 | MCSA | PiB | C | No | Yes | 483 | -7.9837760726405749E-2 | NA |
| Mielke MM | 2012 | MCSA | PiB | C | No | Yes | 483 | NA | -0.0509937 |
| Mok VC | 2016 | Hong Kong | PiB | C | No | Yes | 75 | 0.14004696424834814 | NA |
| Mok VC | 2016 | Hong Kong | PiB | L | No | Yes | 53 | 7.2671332373525077E-2 | NA |
| Mok VC | 2016 | Hong Kong | PiB | L | No | Yes | 53 | 0.1850980014974006 | NA |
| Mok VC | 2016 | Hong Kong | PiB | L | No | Yes | 53 | 0.32204560863404641 | NA |
| Molinuevo JL | 2014 | Barcelona | ab42/ptau/ttau | C | No | Yes | 38 | 0.17248843060677599 | NA |
| Molinuevo JL | 2014 | Barcelona | ab42/ptau/ttau | C | No | Yes | 38 | NA | 0.0722052 |
| Moon YS | 2011 | Korean | ab42 | C | No | Yes | 123 | -0.25 | NA |
| Moon YS | 2011 | Korean | Ab42 | C | No | Yes | 123 | 0.99240974591340037 | NA |
| Mueller SG | 2018 | UCSF | Florbetapir | C | No | No | 51 | 0.15240009195007251 | NA |
| Mueller SG | 2018 | UCSF | Florbetapir | C | No | No | 51 | NA | 0.0639220 |
| Mukaetova-Ladinska EB | 2018 | Leicester | ptau-181 | C | No | Yes | 26 | 0 | NA |
| Nakamura A | 2018 | MULNIAD | PiB | C | No | Yes | 38 | -1.8952300933460446E-2 | NA |
| Nakamura A | 2018 | MULNIAD | PiB | C | No | Yes | 38 | NA | -0.0141493 |
| Nebes RD | 2013 | PITT | PiB | C | No | Yes | 71 | NA | 0.1295316 |
| Ossenkoppele R | 2013 | BACS | PiB | C | No | Yes | 81 | 5.7224929398986019E-2 | NA |
| Ossenkoppele R | 2013 | BACS | PiB | C | No | Yes | 81 | NA | 0.0833677 |
| Ossenkoppele R | 2013 | BACS | PiB | C | No | Yes | 81 | -0.22923566668277359 | NA |
| Palmqvist S | 2014 | BioFINDER | Flutemetamol | C | No | Yes | 118 | -0.32 | NA |
| Palmqvist S | 2014 | BioFINDER | Flutemetamol | C | No | Yes | 118 | NA | -0.2800000 |
| Hammers DB | 2017 | Utah | Flutemetamol | C | No | Yes | 27 | NA | 0.7335282 |
| Hammers DB | 2017 | Utah | Flutemetamol | C | No | Yes | 27 | 0.728768833518325 | NA |
| Hanseeuw BJ | 2017 | HABS | PiB | L | No | No | 277 | 0.17772830136798845 | NA |
| Hanseeuw BJ | 2017 | HABS | PiB | L | No | No | 277 | NA | -0.0411176 |
| Hanseeuw BJ | 2017 | HABS | PiB | C | No | No | 277 | 0.13172254119910687 | NA |
| Hanseeuw BJ | 2017 | HABS | PiB | C | No | No | 277 | NA | 0.0337453 |
| Harrington KD | 2018 | AIBL | Florbetapir, PiB, FDDNP | C | No | No | 494 | 8.2934123085769973E-2 | NA |
| Harrington KD | 2018 | AIBL | Florbetapir, PiB, FDDNP | C | No | Yes | 494 | NA | -0.0572521 |
| Harrington MG | 2013 | Pasadena | ab42/ttau | C | No | No | 46 | NA | -0.0663486 |
| Jacobs HIL | 2018 | HABS | PiB | C | No | Yes | 256 | 7.5958082736127308E-2 | NA |
| Jacobs HIL | 2018 | HABS | PiB | C | No | Yes | 256 | NA | -0.0011623 |
| Tolboom | 2009 | Amsterdam | PiB | C | No | Yes | 13 | -0.75 | NA |
| Tolboom | 2009 | Amsterdam | FDDNP | C | No | Yes | 13 | -0.39 | NA |
| Schott JM | 2010 | ADNI | Ab42 | C | No | Yes | 105 | 0.13318228989215805 | NA |
| Schott JM | 2010 | ADNI | Ab42 | C | No | Yes | 105 | NA | -0.0303373 |
| Lim YY | 2018 | AIBL | Florbetapir, PiB, FDDNP | C | No | No | 447 | 0.20938035235433539 | NA |
| Lim YY | 2018 | AIBL | Florbetapir, PiB, FDDNP | L | Yes | Yes | 447 | NA | 0.1505543 |
# stargazer(
# newtab,
# summary = FALSE,
# single.row = TRUE,
# no.space = TRUE,
# digits = 2,
# column.sep.width = "1pt",
# font.size = "small",
# type = 'text',
# out= "summary.text"
# )4 Meta analysis
4.1 Grouping by cognitive domain, amyloid and tau, cross and long
#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 = 208
##
## COR 95%-CI t p-value
## Random effects model 0.0352 [-0.0128; 0.0830] 1.45 0.1497
## Prediction interval [-0.5551; 0.6019]
##
## Quantifying heterogeneity:
## tau^2 = 0.1118 [0.0870; 0.1376]; tau = 0.3343 [0.2950; 0.3709];
## I^2 = 93.3% [92.7%; 93.9%]; H = 3.88 [3.70; 4.06]
##
## Quantifying residual heterogeneity:
## I^2 = 92.9% [92.2%; 93.6%]; H = 3.77 [3.59; 3.95]
##
## Test of heterogeneity:
## Q d.f. p-value
## 3004.34 200 0
##
## Results for subgroups (random effects model):
## k COR 95%-CI
## byvar = Memory Baseline Amyloid 45 0.0677 [-0.0436; 0.1774]
## byvar = Memory Baseline Amyloid Tau 10 -0.0053 [-0.2564; 0.2465]
## byvar = Global Baseline Amyloid 60 0.0840 [-0.0314; 0.1972]
## byvar = Global Followup Amyloid 28 -0.0223 [-0.1450; 0.1011]
## byvar = Global Baseline Amyloid Tau 14 0.0669 [-0.0437; 0.1759]
## byvar = Global Baseline Tau 9 -0.0695 [-0.2260; 0.0906]
## byvar = Memory Followup Amyloid 15 0.0384 [-0.1645; 0.2381]
## byvar = Global Followup Tau 5 -0.0632 [-0.2679; 0.1470]
## byvar = Global Followup Amyloid Tau 7 -0.0085 [-0.1632; 0.1467]
## byvar = Memory Baseline Tau 7 -0.0820 [-0.1286; -0.0351]
## byvar = Memory Followup Tau 3 -0.0660 [-0.3230; 0.2000]
## byvar = Memory Followup Amyloid Tau 5 0.0347 [-0.1546; 0.2217]
## tau^2 tau Q I^2
## byvar = Memory Baseline Amyloid 0.1233 0.3511 781.57 94.8%
## byvar = Memory Baseline Amyloid Tau 0.1146 0.3386 72.58 87.6%
## byvar = Global Baseline Amyloid 0.1843 0.4293 1250.03 95.4%
## byvar = Global Followup Amyloid 0.1081 0.3288 272.46 90.5%
## byvar = Global Baseline Amyloid Tau 0.0274 0.1654 85.41 84.8%
## byvar = Global Baseline Tau 0.0358 0.1892 15.35 54.4%
## byvar = Memory Followup Amyloid 0.1204 0.3470 192.68 92.7%
## byvar = Global Followup Tau 0.0216 0.1470 23.11 82.7%
## byvar = Global Followup Amyloid Tau 0.0227 0.1506 60.49 90.1%
## byvar = Memory Baseline Tau 0.0004 0.0205 1.50 0.0%
## byvar = Memory Followup Tau 0.0075 0.0867 1.92 0.0%
## byvar = Memory Followup Amyloid Tau 0.0164 0.1280 21.87 81.7%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 20.23 11 0.0423
##
## 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 correlations4.2 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$trimfill, 1, 16),
level = 0.9,
comb.random = FALSE
)
funnel(first,
main = "Standard Error")
funnel(first,
yaxis = "invvar")
funnel(first,
yaxis = "size",
main = "Sampling size",
title = "hsjs")
#option 2 - only memory-amyloid, yaxis = 1/SE
data_memory_amyloid <-
subset(data, table1== "1" & data$memory_composite == "1" & data$amyloid == "1" & data$tau != "1")
memory_amyloid <-
cbind(as.numeric(data$all_r), as.numeric(data$weight_se))
viz_funnel(
memory_amyloid,
y_axis = "precision",
contours = TRUE,
trim_and_fill = TRUE,
trim_and_fill_side = "left",
egger = TRUE
)
#option 3 - only memory-amyloid, yaxis = SE
viz_funnel(
memory_amyloid,
y_axis = "se",
contours = TRUE,
trim_and_fill = TRUE,
trim_and_fill_side = "left",
egger = TRUE
)
5 Using metafor package
5.1 Memory amyloid
data <- subset(data_full, table1 == 1, exclude != 0)
data_memory_amyloid <-
subset(data, table1== "1" & data$memory_composite == "1" & data$amyloid == "1" & data$tau != "1")
dat <-
escalc(
measure = "ZCOR",
ri = as.numeric(data_memory_amyloid$all_r),
ni = as.numeric(data_memory_amyloid$total_nc),
data = data_memory_amyloid
) #
# meta-analysis of the transformed correlations
res <- rma(yi, vi, data = dat, method = "DL")
print(res)##
## Random-Effects Model (k = 57; tau^2 estimator: DL)
##
## tau^2 (estimated amount of total heterogeneity): 0.0722 (SE = 0.0278)
## tau (square root of estimated tau^2 value): 0.2688
## I^2 (total heterogeneity / total variability): 94.37%
## H^2 (total variability / sampling variability): 17.77
##
## Test for Heterogeneity:
## Q(df = 56) = 994.9870, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0611 0.0391 1.5643 0.1177 -0.0155 0.1377
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# method="DL" = DerSimonian-Laird estimator
# method="HE" = Hedges estimator
# method="HS" = Hunter-Schmidt estimator
# method="SJ" = Sidik-Jonkman estimator
# method="ML" = maximum-likelihood estimator
# method="REML" = restricted maximum-likelihood estimator
# method="EB" = empirical Bayes estimator
# method="PM" = Paule-Mandel estimator
# method="GENQ" = generalized Q-statistic estimator
#funnel plot
funnel(
res,
yaxis = "sei",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE,
studlab = TRUE
)
funnel(
res,
yaxis = "seinv",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE,
studlab = TRUE
)
funnel(
res,
yaxis = "ni",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE,
studlab = TRUE
)
funnel(
res,
yaxis = "ninv",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE,
studlab = TRUE
)
funnel(
res,
yaxis = "sqrtni",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE,
studlab = TRUE
)
funnel(
res,
yaxis = "wi",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE,
studlab = TRUE
)
5.2 Memory tau
data_memory_tau <-
subset(data, table1== "1" & data$memory_composite == "1" & data$tau == "1" & data$amyloid != "1" )
dat <-
escalc(
measure = "ZCOR",
ri = as.numeric(data_memory_tau$all_r),
ni = as.numeric(data_memory_tau$total_nc),
data = data_memory_tau
) #
# meta-analysis of the transformed correlations
res <- rma(yi, vi, data = dat, method = "DL")
print(res)##
## Random-Effects Model (k = 9; tau^2 estimator: DL)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0044)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 8) = 3.6994, p-val = 0.8832
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0691 0.0306 -2.2539 0.0242 -0.1291 -0.0090 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Standard funnel plot (Plot A)
funnel(
res,
yaxis = "sei",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "seinv",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "ni",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "ninv",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "sqrtni",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "wi",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
5.3 Global Amyloid
data_global_amyloid <-
subset(data, table1== "1" & data$global_composite == "1" & data$amyloid == "1" & data$tau != "1")
dat <-
escalc(
measure = "ZCOR",
ri = as.numeric(data_global_amyloid$all_r),
ni = as.numeric(data_global_amyloid$total_nc),
data = data_global_amyloid
) #
# meta-analysis of the transformed correlations
res <- rma(yi, vi, data = dat, method = "DL")
print(res)##
## Random-Effects Model (k = 86; tau^2 estimator: DL)
##
## tau^2 (estimated amount of total heterogeneity): 0.0789 (SE = 0.0232)
## tau (square root of estimated tau^2 value): 0.2809
## I^2 (total heterogeneity / total variability): 94.64%
## H^2 (total variability / sampling variability): 18.65
##
## Test for Heterogeneity:
## Q(df = 85) = 1585.1128, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0515 0.0330 1.5608 0.1186 -0.0132 0.1162
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#funnel plot
funnel(
res,
yaxis = "sei",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "seinv",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "ni",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "ninv",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "sqrtni",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "wi",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
5.4 Global tau
data_global_tau <-
subset(data, table1== "1" & data$global_composite == "1" & data$tau == "1" & data$amyloid != "1")
dat <-
escalc(
measure = "ZCOR",
ri = as.numeric(data_global_tau$all_r),
ni = as.numeric(data_global_tau$total_nc),
data = data_global_tau
) #
# meta-analysis of the transformed correlations
res <- rma(yi, vi, data = dat, method = "DL")
print(res)##
## Random-Effects Model (k = 13; tau^2 estimator: DL)
##
## tau^2 (estimated amount of total heterogeneity): 0.0163 (SE = 0.0109)
## tau (square root of estimated tau^2 value): 0.1276
## I^2 (total heterogeneity / total variability): 68.87%
## H^2 (total variability / sampling variability): 3.21
##
## Test for Heterogeneity:
## Q(df = 12) = 38.5489, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0657 0.0458 -1.4356 0.1511 -0.1555 0.0240
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Standard funnel plot (Plot A)
funnel(
res,
yaxis = "sei",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "seinv",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "ni",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "ninv",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "sqrtni",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
funnel(
res,
yaxis = "wi",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0,
legend = TRUE
)
6 Extra
6.1 Meta analysis domain, method, cross, long
#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(1, 2),
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"))
second <- 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,
method
),
#grouping variable
print.byvar = gs("print.byvar")
) #print grouping variable
print(second, digits = 2)## COR 95%-CI %W(random)
## Gu 0.07 [ 0.00; 0.14] 0.6
## Horn MM 0.96 [ 0.95; 0.97] 0.5
## Hoscheidt SM 0.10 [-0.14; 0.32] 0.5
## Hosokawa C 0.76 [ 0.66; 0.84] 0.5
## Adamczuk K 0.23 [-0.04; 0.46] 0.5
## Adamczuk K 0.22 [-0.05; 0.45] 0.5
## Aizenstein HJ -0.12 [-0.42; 0.21] 0.4
## Aizenstein HJ -0.38 [-0.64; -0.05] 0.4
## Pomara N -0.35 [-0.62; -0.01] 0.4
## Radanovic M 0.21 [-0.06; 0.45] 0.5
## Radanovic M 0.21 [-0.06; 0.45] 0.5
## Radanovic M -0.28 [-0.51; -0.02] 0.5
## Rentz DM -0.09 [-0.26; 0.08] 0.5
## Rentz DM -0.15 [-0.31; 0.02] 0.5
## Rentz DM -0.02 [-0.19; 0.15] 0.5
## Rentz DM -0.12 [-0.28; 0.05] 0.5
## Rentz DM NA 0.0
## Resnick SM -0.14 [-0.40; 0.14] 0.5
## Resnick SM -0.21 [-0.46; 0.07] 0.5
## Roberts RO 0.04 [-0.01; 0.09] 0.6
## Roberts RO 0.04 [-0.01; 0.09] 0.6
## Roberts RO 0.25 [ 0.20; 0.29] 0.6
## Roberts RO 0.16 [ 0.11; 0.21] 0.6
## Roe CM 0.05 [-0.04; 0.15] 0.6
## Roe CM 0.09 [ 0.00; 0.19] 0.6
## Roe CM 0.07 [-0.03; 0.16] 0.6
## Rosenberg PB -0.15 [-0.62; 0.39] 0.3
## Rosenberg PB 0.16 [-0.38; 0.62] 0.3
## Sanabria A NA 0.0
## Schindler SE 0.07 [-0.06; 0.20] 0.5
## Schindler SE 0.08 [-0.05; 0.20] 0.5
## Schindler SE -0.03 [-0.16; 0.10] 0.5
## Schindler SE 0.00 [-0.13; 0.13] 0.5
## Schindler SE -0.10 [-0.22; 0.03] 0.5
## Schindler SE -0.07 [-0.20; 0.06] 0.5
## Schindler SE 0.04 [-0.09; 0.17] 0.5
## Schindler SE 0.01 [-0.12; 0.14] 0.5
## Schindler SE -0.02 [-0.15; 0.11] 0.5
## Schindler SE -0.02 [-0.15; 0.11] 0.5
## Schindler SE -0.07 [-0.20; 0.06] 0.5
## Schindler SE -0.02 [-0.15; 0.11] 0.5
## Sala-Llonch R -0.05 [-0.26; 0.16] 0.5
## Sala-Llonch R -0.05 [-0.25; 0.16] 0.5
## Sala-Llonch R 0.11 [-0.10; 0.31] 0.5
## Sierra-Rio A 0.05 [-0.22; 0.31] 0.5
## Song Z 0.13 [-0.09; 0.34] 0.5
## Song Z 0.12 [-0.10; 0.33] 0.5
## Song Z 0.00 [-0.28; 0.27] 0.5
## Song Z -0.02 [-0.30; 0.26] 0.5
## Stomrud E 0.08 [-0.25; 0.39] 0.4
## Stomrud E -0.03 [-0.35; 0.30] 0.4
## Stomrud E 0.06 [-0.27; 0.38] 0.4
## Stomrud E -0.23 [-0.52; 0.10] 0.4
## Stomrud E -0.13 [-0.44; 0.20] 0.4
## Stomrud E -0.27 [-0.54; 0.06] 0.4
## Timmers T -0.16 [-0.34; 0.03] 0.5
## Timmers T -0.06 [-0.24; 0.13] 0.5
## Timmers T -0.03 [-0.22; 0.16] 0.5
## Timmers T -0.13 [-0.31; 0.06] 0.5
## Storandt M 0.12 [-0.05; 0.28] 0.5
## Storandt M 0.00 [-0.16; 0.17] 0.5
## Storandt M 0.00 [-0.16; 0.17] 0.5
## Tardif CL -0.11 [-0.39; 0.19] 0.5
## Tardif CL 0.36 [ 0.07; 0.59] 0.5
## Teipel SJ -0.11 [-0.22; 0.00] 0.5
## Teipel SJ -0.11 [-0.22; 0.00] 0.5
## van Bergen JMG -0.04 [-0.23; 0.14] 0.5
## van Harten AC -0.01 [-0.18; 0.16] 0.5
## van Harten AC -0.07 [-0.23; 0.11] 0.5
## van Harten AC -0.04 [-0.21; 0.13] 0.5
## van Harten AC -0.06 [-0.23; 0.11] 0.5
## van Harten AC 0.00 [-0.17; 0.17] 0.5
## van Harten AC -0.14 [-0.30; 0.03] 0.5
## van Harten AC -0.14 [-0.31; 0.03] 0.5
## van Harten AC -0.04 [-0.21; 0.13] 0.5
## van Harten AC -0.01 [-0.18; 0.16] 0.5
## van Harten AC -0.15 [-0.31; 0.02] 0.5
## van Harten AC -0.14 [-0.31; 0.03] 0.5
## van Harten AC -0.06 [-0.23; 0.11] 0.5
## Villemagne VL NA 0.0
## Visser PJ 0.01 [-0.24; 0.27] 0.5
## Visser PJ -0.06 [-0.31; 0.20] 0.5
## Visser PJ 0.04 [-0.22; 0.30] 0.5
## Xiong C -0.11 [-0.24; 0.03] 0.5
## Xiong C -0.15 [-0.28; -0.01] 0.5
## Xiong C -0.33 [-0.45; -0.20] 0.5
## Xiong C -0.27 [-0.39; -0.14] 0.5
## Xiong C -0.02 [-0.16; 0.12] 0.5
## Xiong C 0.22 [ 0.09; 0.35] 0.5
## Huang KL -0.56 [-0.87; 0.06] 0.3
## Insel PS 0.05 [-0.06; 0.15] 0.5
## Janelidze S 0.12 [ 0.03; 0.20] 0.6
## Jansen WJ 0.07 [ 0.03; 0.11] 0.6
## Jansen WJ 0.09 [ 0.05; 0.12] 0.6
## Kang JM -0.07 [-0.36; 0.24] 0.5
## Kato M 0.44 [ 0.26; 0.58] 0.5
## Kawas CH 0.31 [-0.29; 0.74] 0.3
## Kawas CH -0.30 [-0.73; 0.30] 0.3
## Kawas CH 0.95 [ 0.71; 0.99] 0.2
## Kawas CH 0.33 [-0.66; 0.90] 0.1
## Kemppainen N 0.21 [-0.08; 0.46] 0.5
## Kemppainen N -0.01 [-0.30; 0.27] 0.5
## Konijnenberg E 0.08 [-0.06; 0.22] 0.5
## Konijnenberg E -0.05 [-0.19; 0.09] 0.5
## Konijnenberg E 0.03 [-0.14; 0.21] 0.5
## Konijnenberg E 0.05 [-0.12; 0.23] 0.5
## Kristofikova Z -0.17 [-0.63; 0.37] 0.3
## Kristofikova Z -0.45 [-0.78; 0.09] 0.3
## Lafirdeen ASM 0.22 [ 0.19; 0.25] 0.6
## Leahey TM -0.34 [-0.60; -0.01] 0.4
## Liguori C 0.72 [ 0.56; 0.83] 0.5
## Lilamand M 0.11 [-0.01; 0.22] 0.5
## Lilamand M -0.69 [-0.75; -0.62] 0.5
## Lilamand M -0.50 [-0.59; -0.41] 0.5
## Lim YY 0.06 [-0.19; 0.30] 0.5
## Lim YY -0.05 [-0.29; 0.20] 0.5
## Lim YY 0.18 [-0.07; 0.41] 0.5
## Llado-Saz S 0.03 [-0.15; 0.21] 0.5
## Lu K -0.32 [-0.40; -0.24] 0.6
## Lu K -0.12 [-0.21; -0.03] 0.6
## Martikainen IK 0.03 [-0.28; 0.34] 0.5
## Martikainen IK -0.13 [-0.43; 0.19] 0.5
## McMillan CT 0.37 [ 0.24; 0.49] 0.5
## McMillan CT -0.35 [-0.47; -0.21] 0.5
## Mecca AP 0.13 [-0.17; 0.41] 0.5
## Aschenbrenner AJ -0.13 [-0.31; 0.06] 0.5
## Aschenbrenner AJ 0.06 [-0.12; 0.25] 0.5
## Aschenbrenner AJ -0.05 [-0.24; 0.13] 0.5
## Berenguer RG 0.38 [ 0.07; 0.62] 0.5
## Berenguer RG -0.34 [-0.59; -0.03] 0.5
## Besson FL -0.13 [-0.39; 0.14] 0.5
## Besson FL -0.11 [-0.37; 0.16] 0.5
## Bilgel M -0.02 [-0.19; 0.16] 0.5
## Bilgel M NA 0.0
## Casaletto KB -0.14 [-0.30; 0.03] 0.5
## Casaletto KB -0.02 [-0.19; 0.15] 0.5
## Chatterjee P -0.12 [-0.31; 0.07] 0.5
## Cosentino SA -0.02 [-0.11; 0.07] 0.6
## Cosentino SA 0.01 [-0.08; 0.10] 0.6
## Cosentino SA -0.02 [-0.11; 0.07] 0.6
## Donohue MC 0.16 [ 0.07; 0.25] 0.6
## Donohue MC 0.25 [ 0.16; 0.34] 0.6
## Donohue MC 0.24 [ 0.15; 0.33] 0.6
## Donohue MC -0.23 [-0.32; -0.14] 0.6
## Doraiswamy PM -0.01 [-0.25; 0.23] 0.5
## Doraiswamy PM 0.23 [ 0.00; 0.45] 0.5
## Doraiswamy PM -0.22 [-0.43; 0.02] 0.5
## Doraiswamy PM 0.83 [ 0.74; 0.89] 0.5
## Dubois B 0.12 [ 0.01; 0.23] 0.5
## Ecay-Torres M 0.00 [-0.13; 0.13] 0.5
## Farrell ME -0.12 [-0.29; 0.06] 0.5
## Farrell ME -0.09 [-0.27; 0.09] 0.5
## Farrell ME -0.01 [-0.19; 0.16] 0.5
## Franzmeier N 0.26 [-0.02; 0.51] 0.5
## Franzmeier N 0.12 [-0.17; 0.39] 0.5
## Gangishetti U 0.10 [-0.20; 0.39] 0.5
## Haapalinna F -0.29 [-0.51; -0.03] 0.5
## Hamelin L 0.17 [-0.34; 0.60] 0.3
## Hamelin L 0.14 [-0.37; 0.58] 0.3
## Hamelin L 0.28 [-0.24; 0.67] 0.3
## Zhao Y 0.08 [-0.07; 0.23] 0.5
## Yaffe K -0.03 [-0.11; 0.04] 0.6
## Yaffe K -0.11 [-0.19; -0.04] 0.6
## Yaffe K 0.02 [-0.06; 0.10] 0.6
## Yaffe K -0.08 [-0.16; 0.00] 0.6
## Meng Y 0.03 [-0.31; 0.36] 0.4
## Merrill DA NA 0.0
## Merrill DA NA 0.0
## Mielke MM 0.01 [-0.18; 0.19] 0.5
## Mielke MM 0.01 [-0.17; 0.19] 0.5
## Mielke MM -0.08 [-0.17; 0.01] 0.6
## Mielke MM -0.05 [-0.14; 0.04] 0.6
## Mok VC 0.14 [-0.09; 0.36] 0.5
## Mok VC 0.07 [-0.20; 0.34] 0.5
## Mok VC 0.19 [-0.09; 0.43] 0.5
## Mok VC 0.32 [ 0.06; 0.54] 0.5
## Molinuevo JL 0.17 [-0.16; 0.47] 0.4
## Molinuevo JL 0.07 [-0.25; 0.38] 0.4
## Moon YS -0.25 [-0.41; -0.08] 0.5
## Moon YS 0.99 [ 0.99; 0.99] 0.5
## Mueller SG 0.15 [-0.13; 0.41] 0.5
## Mueller SG 0.06 [-0.22; 0.33] 0.5
## Mukaetova-Ladinska EB NA 0.0
## Nakamura A -0.02 [-0.34; 0.30] 0.4
## Nakamura A -0.01 [-0.33; 0.31] 0.4
## Nebes RD 0.13 [-0.11; 0.35] 0.5
## Ossenkoppele R 0.06 [-0.16; 0.27] 0.5
## Ossenkoppele R 0.08 [-0.14; 0.30] 0.5
## Ossenkoppele R -0.23 [-0.43; -0.01] 0.5
## Palmqvist S -0.32 [-0.47; -0.15] 0.5
## Palmqvist S -0.28 [-0.44; -0.10] 0.5
## Hammers DB 0.73 [ 0.49; 0.87] 0.4
## Hammers DB 0.73 [ 0.48; 0.87] 0.4
## Hanseeuw BJ 0.18 [ 0.06; 0.29] 0.5
## Hanseeuw BJ -0.04 [-0.16; 0.08] 0.5
## Hanseeuw BJ 0.13 [ 0.01; 0.25] 0.5
## Hanseeuw BJ 0.03 [-0.08; 0.15] 0.5
## Harrington KD 0.08 [-0.01; 0.17] 0.6
## Harrington KD -0.06 [-0.14; 0.03] 0.6
## Harrington MG -0.07 [-0.35; 0.23] 0.5
## Jacobs HIL 0.08 [-0.05; 0.20] 0.5
## Jacobs HIL 0.00 [-0.12; 0.12] 0.5
## Tolboom -0.75 [-0.92; -0.34] 0.3
## Tolboom -0.39 [-0.77; 0.21] 0.3
## Schott JM 0.13 [-0.06; 0.32] 0.5
## Schott JM -0.03 [-0.22; 0.16] 0.5
## Lim YY 0.21 [ 0.12; 0.30] 0.6
## Lim YY 0.15 [ 0.06; 0.24] 0.6
## byvar
## Gu Memory NA Ab42
## Horn MM Memory NA PiB
## Hoscheidt SM Memory NA ab42/ab40/ptau/ttau
## Hosokawa C Global NA PiB
## Adamczuk K Global NA Flutemetamol
## Adamczuk K Memory NA Flutemetamol
## Aizenstein HJ Global NA PiB
## Aizenstein HJ Memory NA PiB
## Pomara N Global NA Ab42
## Radanovic M Global NA ab42/ptau
## Radanovic M Global NA Ab42
## Radanovic M Global NA ptau
## Rentz DM Global NA PiB
## Rentz DM Global NA T807
## Rentz DM Global NA PiB, T807
## Rentz DM Global NA PiB, T807
## Rentz DM Memory NA PiB
## Resnick SM Memory NA PiB
## Resnick SM Global NA PiB
## Roberts RO Global NA PiB
## Roberts RO Memory NA PiB
## Roberts RO Global NA PiB
## Roberts RO Memory NA PiB
## Roe CM Global NA Florbetapir, ptau
## Roe CM Global NA Florbetapir, ab42/ptau
## Roe CM Global NA Florbetapir, ab42
## Rosenberg PB Memory NA Florbetapir
## Rosenberg PB Global NA Florbetapir
## Sanabria A Memory NA PiB
## Schindler SE Memory NA Ab42
## Schindler SE Global NA Ab42
## Schindler SE Memory NA ptau
## Schindler SE Global NA ptau
## Schindler SE Memory NA ab42/ptau
## Schindler SE Global NA ab42/ptau
## Schindler SE Global NA Ab42
## Schindler SE Memory NA Ab42
## Schindler SE Global NA ptau
## Schindler SE Memory NA ptau
## Schindler SE Global NA ab42/ptau
## Schindler SE Memory NA ab42/ptau
## Sala-Llonch R Memory NA Ab42
## Sala-Llonch R Global NA Ab42
## Sala-Llonch R Global NA Ab42
## Sierra-Rio A Global NA ab42/ptau
## Song Z Memory NA Florbetapir
## Song Z Global NA Florbetapir
## Song Z Memory NA Florbetapir
## Song Z Global NA Florbetapir
## Stomrud E Global NA Ab42
## Stomrud E Global NA ptau
## Stomrud E Global NA ptau
## Stomrud E Global NA Ab42
## Stomrud E Memory NA ptau
## Stomrud E Memory NA ptau
## Timmers T Global NA Florbetapir
## Timmers T Memory NA Florbetapir
## Timmers T Global NA Florbetapir
## Timmers T Memory NA Florbetapir
## Storandt M Global NA PiB
## Storandt M Global NA PiB
## Storandt M Memory NA PiB
## Tardif CL Global NA Ab42
## Tardif CL Global NA ttau
## Teipel SJ Global NA Florbetapir
## Teipel SJ Memory NA Florbetapir
## van Bergen JMG Global NA Flutemetamol
## van Harten AC Global NA Ab42
## van Harten AC Memory NA ptau
## van Harten AC Global NA ptau
## van Harten AC Memory NA ab42/ttau
## van Harten AC Global NA ab42/ttau
## van Harten AC Memory NA Ab42
## van Harten AC Global NA Ab42
## van Harten AC Memory NA ptau
## van Harten AC Global NA ptau
## van Harten AC Memory NA ab42/ttau
## van Harten AC Global NA ab42/ttau
## van Harten AC Memory NA Ab42
## Villemagne VL Memory NA 18F-THK523
## Visser PJ Global NA ab42/ttau
## Visser PJ Global NA ab42/ttau
## Visser PJ Memory NA ab42
## Xiong C Global NA ptau
## Xiong C Global NA PiB
## Xiong C Global NA ptau
## Xiong C Global NA PiB
## Xiong C Global NA Ab42
## Xiong C Global NA Ab42
## Huang KL Memory NA Florbetapir
## Insel PS Global NA ab42/ab40
## Janelidze S Global NA ab42/ab40
## Jansen WJ Global NA PiB
## Jansen WJ Memory NA PiB
## Kang JM Memory NA THK5351
## Kato M Global NA Florbetapir
## Kawas CH Global NA Florbetapir
## Kawas CH Memory NA Florbetapir
## Kawas CH Global NA Florbetapir
## Kawas CH Memory NA Florbetapir
## Kemppainen N Global NA PiB
## Kemppainen N Memory NA PiB
## Konijnenberg E Memory NA Flutemetamol
## Konijnenberg E Global NA Flutemetamol
## Konijnenberg E Memory NA ab42/ab40
## Konijnenberg E Global NA ab42/ab40
## Kristofikova Z Global NA Ab42
## Kristofikova Z Global NA ptau
## Lafirdeen ASM Global NA Ab42
## Leahey TM Global NA Ab40
## Liguori C Memory NA ab40/ptau/ttau
## Lilamand M Global NA Florbetapir
## Lilamand M Global NA Florbetapir
## Lilamand M Memory NA Florbetapir
## Lim YY Global NA Florbetapir
## Lim YY Global NA Florbetapir
## Lim YY Memory NA Florbetapir
## Llado-Saz S Memory NA Florbetapir
## Lu K Global NA Florbetapir
## Lu K Memory NA Florbetapir
## Martikainen IK Global NA PiB
## Martikainen IK Memory NA PiB
## McMillan CT Global NA ab42/ptau/ttau
## McMillan CT Memory NA Ab42
## Mecca AP Memory NA PiB
## Aschenbrenner AJ Memory NA ptau
## Aschenbrenner AJ Memory NA Ab42
## Aschenbrenner AJ Memory NA PiB
## Berenguer RG Memory NA Ab42
## Berenguer RG Memory NA ab42/ptau
## Besson FL Global NA 0
## Besson FL Memory NA 0
## Bilgel M Memory NA PiB
## Bilgel M Global NA PiB
## Casaletto KB Memory NA ptau
## Casaletto KB Memory NA Ab42
## Chatterjee P Global NA Florbetaben
## Cosentino SA Global NA Ab42
## Cosentino SA Global NA ptau
## Cosentino SA Memory NA Ab42
## Donohue MC Global NA Florbetapir, PiB
## Donohue MC Global NA Florbetapir, PiB
## Donohue MC Memory NA Florbetapir, PiB
## Donohue MC Global NA Florbetapir, PiB
## Doraiswamy PM Global NA Florbetapir
## Doraiswamy PM Memory NA Florbetapir
## Doraiswamy PM Global NA Florbetapir
## Doraiswamy PM Memory NA Florbetapir
## Dubois B Global NA Florbetapir
## Ecay-Torres M Global NA Ab42
## Farrell ME Memory NA Florbetapir
## Farrell ME Global NA Florbetapir
## Farrell ME Global NA Florbetapir
## Franzmeier N Global NA Florbetapir
## Franzmeier N Memory NA Florbetapir
## Gangishetti U Global NA Florbetapir
## Haapalinna F Memory NA Ab42
## Hamelin L Global NA PiB
## Hamelin L Global NA PiB
## Hamelin L Memory NA PiB
## Zhao Y Memory NA PiB
## Yaffe K Global NA Ab42
## Yaffe K Global NA Ab42
## Yaffe K Global NA ab42/ab40
## Yaffe K Global NA ab42/ab40
## Meng Y Global NA APL1b28
## Merrill DA Global NA FDDNP
## Merrill DA Memory NA FDDNP
## Mielke MM Memory NA PiB
## Mielke MM Global NA PiB
## Mielke MM Global NA PiB
## Mielke MM Memory NA PiB
## Mok VC Global NA PiB
## Mok VC Global NA PiB
## Mok VC Global NA PiB
## Mok VC Global NA PiB
## Molinuevo JL Global NA ab42/ptau/ttau
## Molinuevo JL Memory NA ab42/ptau/ttau
## Moon YS Global NA ab42
## Moon YS Global NA Ab42
## Mueller SG Global NA Florbetapir
## Mueller SG Memory NA Florbetapir
## Mukaetova-Ladinska EB Global NA ptau-181
## Nakamura A Global NA PiB
## Nakamura A Memory NA PiB
## Nebes RD Memory NA PiB
## Ossenkoppele R Global NA PiB
## Ossenkoppele R Memory NA PiB
## Ossenkoppele R Global NA PiB
## Palmqvist S Global NA Flutemetamol
## Palmqvist S Memory NA Flutemetamol
## Hammers DB Memory NA Flutemetamol
## Hammers DB Global NA Flutemetamol
## Hanseeuw BJ Global NA PiB
## Hanseeuw BJ Memory NA PiB
## Hanseeuw BJ Global NA PiB
## Hanseeuw BJ Memory NA PiB
## Harrington KD Global NA Florbetapir, PiB, FDDNP
## Harrington KD Memory NA Florbetapir, PiB, FDDNP
## Harrington MG Memory NA ab42/ttau
## Jacobs HIL Global NA PiB
## Jacobs HIL Memory NA PiB
## Tolboom Global NA PiB
## Tolboom Global NA FDDNP
## Schott JM Global NA Ab42
## Schott JM Memory NA Ab42
## Lim YY Global NA Florbetapir, PiB, FDDNP
## Lim YY Memory NA Florbetapir, PiB, FDDNP
##
## Number of studies combined: k = 208
##
## COR 95%-CI t p-value
## Random effects model 0.04 [-0.01; 0.08] 1.45 0.1497
## Prediction interval [-0.56; 0.60]
##
## Quantifying heterogeneity:
## tau^2 = 0.1118 [0.0870; 0.1376]; tau = 0.3343 [0.2950; 0.3709];
## I^2 = 93.3% [92.7%; 93.9%]; H = 3.88 [3.70; 4.06]
##
## Quantifying residual heterogeneity:
## I^2 = 93.6% [92.9%; 94.2%]; H = 3.95 [3.76; 4.15]
##
## Test of heterogeneity:
## Q d.f. p-value
## 3004.34 200 0
##
## Results for subgroups (random effects model):
## k COR 95%-CI tau^2
## byvar = Memory NA Ab42 13 -0.03 [-0.13; 0.07] 0.0225
## byvar = Memory NA PiB 24 0.10 [-0.08; 0.28] 0.1770
## byvar = Memory NA ab42/ab40/ptau/ttau 1 0.10 [-0.14; 0.32] --
## byvar = Global NA PiB 29 0.05 [-0.05; 0.16] 0.0676
## byvar = Global NA Flutemetamol 5 0.12 [-0.43; 0.60] 0.1976
## byvar = Memory NA Flutemetamol 4 0.21 [-0.53; 0.77] 0.2300
## byvar = Global NA Ab42 21 0.13 [-0.16; 0.39] 0.3842
## byvar = Global NA ab42/ptau 4 -0.01 [-0.19; 0.18] 0.0087
## byvar = Global NA ptau 11 -0.09 [-0.18; 0.01] 0.0155
## byvar = Global NA T807 1 -0.15 [-0.31; 0.02] --
## byvar = Global NA PiB, T807 2 -0.07 [-0.59; 0.49] 0.0011
## byvar = Global NA Florbetapir, ptau 1 0.05 [-0.04; 0.15] --
## byvar = Global NA Florbetapir, ab42/ptau 1 0.09 [-0.00; 0.19] --
## byvar = Global NA Florbetapir, ab42 1 0.07 [-0.03; 0.16] --
## byvar = Memory NA Florbetapir 18 0.01 [-0.17; 0.20] 0.1203
## byvar = Global NA Florbetapir 22 0.02 [-0.14; 0.19] 0.1414
## byvar = Memory NA ptau 8 -0.07 [-0.13; -0.02] 0.0020
## byvar = Memory NA ab42/ptau 3 -0.11 [-0.45; 0.25] 0.0166
## byvar = Global NA ttau 1 0.36 [ 0.07; 0.59] --
## byvar = Memory NA ab42/ttau 3 -0.10 [-0.23; 0.04] 0.0005
## byvar = Global NA ab42/ttau 4 -0.06 [-0.18; 0.07] 0.0015
## byvar = Memory NA 18F-THK523 1 NA --
## byvar = Memory NA ab42 1 0.04 [-0.22; 0.30] --
## byvar = Global NA ab42/ab40 5 0.03 [-0.07; 0.12] 0.0036
## byvar = Memory NA THK5351 1 -0.07 [-0.36; 0.24] --
## byvar = Memory NA ab42/ab40 1 0.03 [-0.14; 0.21] --
## byvar = Global NA Ab40 1 -0.34 [-0.60; -0.01] --
## byvar = Memory NA ab40/ptau/ttau 1 0.72 [ 0.56; 0.83] --
## byvar = Global NA ab42/ptau/ttau 2 0.32 [-0.73; 0.92] 0.0096
## byvar = Global NA 0 1 -0.13 [-0.39; 0.14] --
## byvar = Memory NA 0 1 -0.11 [-0.37; 0.16] --
## byvar = Global NA Florbetaben 1 -0.12 [-0.31; 0.07] --
## byvar = Global NA Florbetapir, PiB 3 0.06 [-0.53; 0.61] 0.0641
## byvar = Memory NA Florbetapir, PiB 1 0.24 [ 0.15; 0.33] --
## byvar = Global NA APL1b28 1 0.03 [-0.31; 0.36] --
## byvar = Global NA FDDNP 2 -0.39 [-0.39; -0.39] --
## byvar = Memory NA FDDNP 1 NA --
## byvar = Memory NA ab42/ptau/ttau 1 0.07 [-0.25; 0.38] --
## byvar = Global NA ab42 1 -0.25 [-0.41; -0.08] --
## byvar = Global NA ptau-181 1 NA --
## byvar = Global NA Florbetapir, PiB, FDDNP 2 0.15 [-0.59; 0.75] 0.0055
## byvar = Memory NA Florbetapir, PiB, FDDNP 2 0.05 [-0.86; 0.88] 0.0183
## tau Q I^2
## byvar = Memory NA Ab42 0.1499 41.20 70.9%
## byvar = Memory NA PiB 0.4207 693.19 97.0%
## byvar = Memory NA ab42/ab40/ptau/ttau -- 0.00 --
## byvar = Global NA PiB 0.2600 208.95 87.1%
## byvar = Global NA Flutemetamol 0.4445 36.11 88.9%
## byvar = Memory NA Flutemetamol 0.4796 33.59 91.1%
## byvar = Global NA Ab42 0.6199 986.46 98.0%
## byvar = Global NA ab42/ptau 0.0932 3.98 24.7%
## byvar = Global NA ptau 0.1244 25.91 61.4%
## byvar = Global NA T807 -- 0.00 --
## byvar = Global NA PiB, T807 0.0327 0.60 0.0%
## byvar = Global NA Florbetapir, ptau -- 0.00 --
## byvar = Global NA Florbetapir, ab42/ptau -- 0.00 --
## byvar = Global NA Florbetapir, ab42 -- 0.00 --
## byvar = Memory NA Florbetapir 0.3469 188.40 91.0%
## byvar = Global NA Florbetapir 0.3760 273.15 92.3%
## byvar = Memory NA ptau 0.0449 3.70 0.0%
## byvar = Memory NA ab42/ptau 0.1287 3.65 45.2%
## byvar = Global NA ttau -- 0.00 --
## byvar = Memory NA ab42/ttau 0.0214 0.62 0.0%
## byvar = Global NA ab42/ttau 0.0389 1.69 0.0%
## byvar = Memory NA 18F-THK523 -- -- --
## byvar = Memory NA ab42 -- 0.00 --
## byvar = Global NA ab42/ab40 0.0598 11.66 65.7%
## byvar = Memory NA THK5351 -- 0.00 --
## byvar = Memory NA ab42/ab40 -- 0.00 --
## byvar = Global NA Ab40 -- 0.00 --
## byvar = Memory NA ab40/ptau/ttau -- 0.00 --
## byvar = Global NA ab42/ptau/ttau 0.0980 1.37 27.1%
## byvar = Global NA 0 -- 0.00 --
## byvar = Memory NA 0 -- 0.00 --
## byvar = Global NA Florbetaben -- 0.00 --
## byvar = Global NA Florbetapir, PiB 0.2531 59.48 96.6%
## byvar = Memory NA Florbetapir, PiB -- 0.00 --
## byvar = Global NA APL1b28 -- 0.00 --
## byvar = Global NA FDDNP -- 0.00 --
## byvar = Memory NA FDDNP -- -- --
## byvar = Memory NA ab42/ptau/ttau -- 0.00 --
## byvar = Global NA ab42 -- 0.00 --
## byvar = Global NA ptau-181 -- -- --
## byvar = Global NA Florbetapir, PiB, FDDNP 0.0744 3.90 74.4%
## byvar = Memory NA Florbetapir, PiB, FDDNP 0.1351 10.19 90.2%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 149.94 41 < 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