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/data_feb.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	0.2500000
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	0.7628017	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
Aizenstein HJ	2008	PITT	PiB	C	No	No	38	-0.1215034	NA
Aizenstein HJ	2008	PITT	PiB	C	No	No	34	NA	-0.3791941
Pomara N	2005	Nathan Kline	Ab42	L	No	No	34	0.4100000	NA
Radanovic M	2019	Brazil	ab42/ptau181	C	No	Yes	54	0.2070000	NA
Radanovic M	2019	Brazil	Ab42	C	No	Yes	54	0.2110000	NA
Radanovic M	2019	Brazil	ptau181	C	No	Yes	54	0.2840000	NA
Rentz DM	2010	HABS	PiB	C	No	Yes	66	NA	0.0000000
Resnick SM	2010	BLSA	PiB	L	Yes	Yes	51	NA	0.0163589
Resnick SM	2010	BLSA	PiB	L	Yes	Yes	51	0.0135846	NA
Roberts RO	2018	MCSA	PiB	L	No	Yes	1492	0.0367629	NA
Roberts RO	2018	MCSA	PiB	L	No	Yes	1492	NA	0.0385758
Roberts RO	2018	MCSA	PiB	C	No	No	1492	0.2466143	NA
Roberts RO	2018	MCSA	PiB	C	No	No	1492	NA	0.1638525
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
Roe CM	2013	Knight	ab42	L	No	Yes	430	0.0678207	NA
Rosenberg PB	2013	John Hopkns	Florbetapir	C	No	No	15	NA	0.1500000
Rosenberg PB	2013	John Hopkns	Florbetapir	C	No	No	15	0.1650000	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	0.0755000	NA
Schindler SE	2017	Knight	ptau	C	Yes	Yes	233	NA	0.0282500
Schindler SE	2017	Knight	ptau	C	Yes	Yes	233	0.0025000	NA
Schindler SE	2017	Knight	ptau/ab42	C	Yes	Yes	233	NA	0.0950833
Schindler SE	2017	Knight	ptau/ab42	C	Yes	Yes	233	0.0695000	NA
Schindler SE	2017	Knight	Ab42	L	Yes	Yes	233	0.0405000	NA
Schindler SE	2017	Knight	Ab42	L	Yes	Yes	233	NA	0.0110000
Schindler SE	2017	Knight	ptau	L	Yes	Yes	233	0.0135000	NA
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	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	-0.0467196	NA
Sala-Llonch R	2017	Norway	Ab42	L	No	No	89	0.1102283	NA
Sierra-Rio A	2015	Barcelona	ab42/ptau	C	No	No	55	0.0475204	NA
Song Z	2016	DLBS	Florbetapir	C	No	No	82	NA	0.1287696
Song Z	2016	DLBS	Florbetapir	C	No	No	82	0.1225236	NA
Song Z	2015	UCSF	Florbetapir	C	No	Yes	50	NA	0.0040000
Song Z	2015	UCSF	Florbetapir	C	No	Yes	50	0.0200000	NA
Stomrud E	2010	CMRU	Ab42	C	No	Yes	37	-0.0005000	NA
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	Ab42	L	No	Yes	37	0.2300000	NA
Stomrud E	2010	CMRU	ptau 181	C	No	Yes	37	NA	-0.1280000
Stomrud E	2010	CMRU	Ab42	C	No	Yes	37	NA	0.1340000
Stomrud E	2010	CMRU	ptau 181	L	No	Yes	37	NA	-0.0940000
Stomrud E	2010	CMRU	Ab42	L	No	Yes	37	NA	0.4370000
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	0.4860464	NA
Timmers T	2019	SCIENCe	Florbetapir	L	No	Yes	107	NA	0.3085927
Storandt M	2009	Knight	PiB	C	No	No	135	0.1209122	NA
Storandt M	2009	Knight	PiB	L	Yes	Yes	135	0.0041069	NA
Storandt M	2009	Knight	PiB	L	Yes	Yes	135	NA	0.0041069
Tardif CL	2017	INTREPAD	Ab42	C	No	No	46	-0.1107440	NA
Tardif CL	2017	INTREPAD	ttau	C	No	No	46	0.3570145	NA
Teipel SJ	2017	INSIGHT	Florbetapir	C	No	Yes	318	0.1100000	NA
Teipel SJ	2017	INSIGHT	Florbetapir	C	No	Yes	318	NA	0.1220000
van Bergen JMG	2018	zurich	Flutemetamol	C	No	Yes	116	-0.0447848	NA
van Harten AC	2013	Amsterdam	Ab42	C	No	No	132	0.1643990	NA
van Harten AC	2013	Amsterdam	ptau	C	No	No	132	NA	0.6000000
van Harten AC	2013	Amsterdam	ptau	C	No	No	132	0.4472136	NA
van Harten AC	2013	Amsterdam	ab42/ttau	C	No	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	C	No	No	132	NA	0.8516583
van Harten AC	2013	Amsterdam	Ab42	L	Yes	No	132	0.8574929	NA
van Harten AC	2013	Amsterdam	ptau	L	Yes	No	132	NA	0.4472136
van Harten AC	2013	Amsterdam	ptau	L	Yes	No	132	0.1643990	NA
van Harten AC	2013	Amsterdam	ab42/ttau	L	Yes	No	132	NA	0.8682431
van Harten AC	2013	Amsterdam	ab42/ttau	L	Yes	No	132	0.8574929	NA
van Harten AC	2013	Amsterdam	Ab42	L	Yes	No	132	NA	0.5547002
Villemagne VL	2014	AIBL	18F-THK523	C	No	No	10	NA	0.0000000
Visser PJ	2009	DESCRIPA	ab42/ttau	L	No	Yes	58	0.0142401	NA
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
Xiong C	2016	ACS	ptau	C	No	Yes	209	0.0600000	NA
Xiong C	2016	ACS	PiB	C	No	Yes	209	0.1500000	NA
Xiong C	2016	ACS	ptau	L	No	Yes	209	0.3300000	NA
Xiong C	2016	ACS	PiB	L	No	Yes	209	0.2700000	NA
Xiong C	2016	ACS	Ab42	C	No	Yes	209	0.0200000	NA
Xiong C	2016	ACS	Ab42	L	No	Yes	209	0.2200000	NA
Huang KL	2018	Taiwan	Florbetapir	C	No	Yes	11	NA	-0.5600000
Insel PS	2019	BioFINDER	ab42/ab40	C	No	Yes	329	0.0458154	NA
Janelidze S	2018	Mälmo Diet Cancer Study +BioFINDER	ab42/ab40	C	No	Yes	508	0.1161625	NA
Jansen WJ	2017	ABS	PiB	C	No	Yes	2908	0.0709225	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	0.4369210	NA
Kawas CH	2012	90 + study	Florbetapir	C	No	No	13	0.3097950	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	6	NA	0.3272785
Kemppainen N	2017	FINGER	PiB	C	No	Yes	48	0.1034905	NA
Kemppainen N	2017	FINGER	PiB	C	No	Yes	48	NA	0.0313408
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	0.5812382	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	0.0533761	NA
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
Lafirdeen ASM	2019	Multicenter France	Ab42	C	No	Yes	3562	0.2218789	NA
Leahey TM	2008	Kent USA	Ab40	C	No	Yes	35	0.3400000	NA
Liguori C	2017	Rome	Ab42	C	No	No	50	NA	0.7250000
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
Lim YY	2015	Rhode Island	Florbetapir	L	No	No	63	0.0606041	NA
Lim YY	2015	Rhode Island	Florbetapir	C	No	No	63	-0.0452078	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	0.2050000	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	0.0304178	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	0.3727037	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	0.0250000
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	-0.0504891	NA
Besson FL	2015	IMAP	0	C	No	No	54	NA	-0.1110823
Bilgel M	2018	BLSA	PiB	L	Yes	Yes	127	NA	0.3807769
Bilgel M	2018	BLSA	PiB	L	Yes	No	127	0.6417183	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	-0.1245904	NA
Cosentino SA	2010	WHICAP	Ab42	C	No	Yes	481	0.0200000	NA
Cosentino SA	2010	WHICAP	Ab43	L	No	Yes	481	0.0100000	NA
Cosentino SA	2010	WHICAP	Ab42	C	Yes	Yes	481	NA	0.0233333
Cosentino SA	2010	WHICAP	Ab42	L	Yes	Yes	481	NA	0.0200000
Donohue MC	2017	ADNI	Florbetapir, PiB	C	No	No	445	0.0398969	NA
Donohue MC	2017	ADNI	Florbetapir, PiB	L	Yes	Yes	445	0.0100000	NA
Donohue MC	2017	ADNI	Florbetapir, PiB	L	Yes	Yes	445	NA	0.2400000
Doraiswamy PM	2014	cohort	Florbetapir	C	No	No	67	0.0693774	NA
Doraiswamy PM	2014	cohort	Florbetapir	C	No	No	67	NA	0.2334509
Doraiswamy PM	2014	cohort	Florbetapir	L	Yes	Yes	67	0.0941613	NA
Doraiswamy PM	2014	cohort	Florbetapir	L	Yes	Yes	67	NA	0.8284213
Dubois B	2018	INSIGHT	Florbetapir	C	No	Yes	318	0.1213773	NA
Ecay-Torres M	2018	GAP	Ab42	C	No	Yes	238	0.0010000	NA
Farrell ME	2017	DLBS	Florbetapir	L	Yes	No	123	NA	0.8046629
Farrell ME	2017	DLBS	Florbetapir	L	Yes	No	123	0.7180338	NA
Farrell ME	2017	DLBS	Florbetapir	L	No	No	123	0.1468287	NA
Franzmeier N	2018	DELCODE	Florbetapir	C	No	No	49	0.2613050	NA
Franzmeier N	2018	DELCODE	Florbetapir	C	No	Yes	49	NA	0.1203751
Gangishetti U	2018	Emory, Penn, Washu	Florbetapir	C	No	No	44	0.1008711	NA
Haapalinna F	2018	Finland	Ab42	C	No	No	57	NA	0.2860000
Hamelin L	2018	IMABio3	PiB	L	No	No	17	0.1672821	NA
Hamelin L	2018	IMABio3	PiB	C	No	No	17	0.1396219	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	-0.0323689	NA
Yaffe K	2011	Healthy ABC	Ab42	L	No	No	658	-0.1112537	NA
Yaffe K	2011	Healthy ABC	ab42/ab40	C	No	No	659	0.0198521	NA
Yaffe K	2011	Healthy ABC	ab42/ab40	L	No	No	659	-0.0800631	NA
Meng Y	2015	Peking	APL1b28	C	No	No	35	0.0250556	NA
Merrill DA	2013	UCLA	FDDNP	C	No	Yes	75	0.7240669	NA
Merrill DA	2013	UCLA	FDDNP	C	No	Yes	75	NA	0.6506007
Mielke MM	2017	MCSA	PiB	L	Yes	Yes	115	NA	0.0058281
Mielke MM	2017	MCSA	PiB	L	Yes	Yes	115	0.0116555	NA
Mielke MM	2012	MCSA	PiB	C	No	Yes	483	-0.0270166	NA
Mielke MM	2012	MCSA	PiB	C	No	Yes	483	NA	-0.0220700
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
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
Moon YS	2011	Korean	ab42	C	No	Yes	123	0.2500000	NA
Mueller SG	2018	UCSF	Florbetapir	C	No	No	51	0.1524001	NA
Mueller SG	2018	UCSF	Florbetapir	C	No	No	51	NA	0.0639220
Mukaetova-Ladinska EB	2018	Leicester	ptau-181	C	No	Yes	26	0.0000000	NA
Nakamura A	2018	MULNIAD	PiB	C	No	Yes	38	-0.0189523	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	0.0572249	NA
Ossenkoppele R	2013	BACS	PiB	C	No	Yes	81	NA	0.0833677
Ossenkoppele R	2013	BACS	PiB	C	No	Yes	81	-0.2292357	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
Hammers DB	2017	Utah	Flutemetamol	C	No	Yes	27	NA	0.3002590
Hammers DB	2017	Utah	Flutemetamol	C	No	Yes	27	0.1912685	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	0.1317225	NA
Hanseeuw BJ	2017	HABS	PiB	C	No	No	277	NA	0.0337453
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
Harrington MG	2013	Pasadena	ab42/ttau	C	No	No	46	NA	-0.0663486
Jacobs HIL	2018	HABS	PiB	C	No	Yes	256	0.0759581	NA
Jacobs HIL	2018	HABS	PiB	C	No	Yes	256	NA	-0.0011623
Tolboom	2009	Amsterdam	PiB	C	No	Yes	13	0.7500000	NA
Tolboom	2009	Amsterdam	FDDNP	C	No	Yes	13	0.3900000	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
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

  # 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 = 205
## 
##                         COR            95%-CI    t  p-value
## Random effects model 0.1844 [ 0.1430; 0.2251] 8.65 < 0.0001
## Prediction interval         [-0.3689; 0.6413]              
## 
## Quantifying heterogeneity:
##  tau^2 = 0.0842 [0.0645; 0.1032]; tau = 0.2902 [0.2539; 0.3212];
##  I^2 = 90.6% [89.5%; 91.5%]; H = 3.25 [3.09; 3.42]
## 
## Quantifying residual heterogeneity:
##  I^2 = 90.6% [89.5%; 91.5%]; H = 3.25 [3.09; 3.43]
## 
## Test of heterogeneity:
##        Q d.f.  p-value
##  2105.94  199 < 0.0001
## 
## Results for subgroups (random effects model):
##                                         k    COR            95%-CI  tau^2
## byvar = Memory  Baseline Amyloid       48 0.1403 [ 0.0543; 0.2242] 0.0780
## byvar = Memory  Baseline Amyloid  Tau   9 0.1332 [-0.0867; 0.3407] 0.0667
## byvar =  Global Baseline Amyloid       61 0.1543 [ 0.0908; 0.2164] 0.0525
## byvar =  Global Followup Amyloid       28 0.2570 [ 0.1162; 0.3877] 0.1369
## byvar =  Global Baseline Amyloid  Tau  10 0.1526 [ 0.0530; 0.2493] 0.0116
## byvar =  Global Baseline  Tau           8 0.2075 [ 0.0294; 0.3728] 0.0337
## byvar = Memory  Followup Amyloid       15 0.3069 [ 0.1007; 0.4878] 0.1350
## byvar =  Global Followup  Tau           5 0.1308 [-0.0408; 0.2949] 0.0128
## byvar =  Global Followup Amyloid  Tau   5 0.2812 [-0.3828; 0.7536] 0.3010
## byvar = Memory  Baseline  Tau           7 0.0982 [-0.1849; 0.3663] 0.0844
## byvar = Memory  Followup  Tau           3 0.2205 [-0.3714; 0.6850] 0.0472
## byvar = Memory  Followup Amyloid  Tau   4 0.3371 [-0.5961; 0.8829] 0.4132
## byvar =   Followup Amyloid              2 0.2080 [-0.9892; 0.9953] 0.0766
##                                          tau      Q   I^2
## byvar = Memory  Baseline Amyloid      0.2792 354.31 87.3%
## byvar = Memory  Baseline Amyloid  Tau 0.2583  77.68 89.7%
## byvar =  Global Baseline Amyloid      0.2291 391.05 84.9%
## byvar =  Global Followup Amyloid      0.3699 472.72 94.3%
## byvar =  Global Baseline Amyloid  Tau 0.1075  22.87 60.6%
## byvar =  Global Baseline  Tau         0.1836  26.38 77.3%
## byvar = Memory  Followup Amyloid      0.3674 259.23 94.6%
## byvar =  Global Followup  Tau         0.1133  15.03 73.4%
## byvar =  Global Followup Amyloid  Tau 0.5486 167.60 97.6%
## byvar = Memory  Baseline  Tau         0.2905  56.29 91.1%
## byvar = Memory  Followup  Tau         0.2172  17.77 88.7%
## byvar = Memory  Followup Amyloid  Tau 0.6428 166.09 98.2%
## byvar =   Followup Amyloid            0.2768   6.39 84.3%
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   6.47   12  0.8905
## 
## 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

4.2 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_rainforest(memory_amyloid, type = "summary_only", method = "DL")
# 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$total_nc),
#     
#     data = data_memory_amyloid
#   ) #
# 
# # meta-analysis of the transformed correlations
# res <- rma(yi, vi, data = dat, method = "DL", slab=paste(dat$author, dat$year, sep=", "))
# print(res)
# 
# # 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,
# 
#   
#   label = 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$total_nc),
#     data = data_memory_tau
#   ) #
# 
# # meta-analysis of the transformed correlations
# res <- rma(yi, vi, data = dat, method = "DL")
# print(res)
# 
# # Standard funnel plot (Plot A)
# 
# 
# funnel(
#   res,
#   yaxis = "sei",
#   level = c(90, 95, 99),
#   shade = c("white", "gray55", "gray75"),
#   refline = 0,
#   legend = TRUE,
#   slab=paste(dat$author, dat$year, sep=", "),
#   label = TRUE
# )
# funnel(
#   res,
#   yaxis = "seinv",
#   level = c(90, 95, 99),
#   shade = c("white", "gray55", "gray75"),
#   refline = 0,
#   legend = TRUE,
#   label = "all"
# )
# funnel(
#   res,
#   yaxis = "ni",
#   level = c(90, 95, 99),
#   shade = c("white", "gray55", "gray75"),
#   refline = 0,
#   legend = TRUE,
#   label = "all"
# )
# 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,
#   studlab = 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)
# 
# #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)
# 
# # 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)

7 Memory amyloid baseline

data <- subset(data_full, table1 == 1, exclude != 0)
data_memory_amyloid <-
  subset(data, table1== "1" & data$memory_composite == "1" & data$amyloid == "1" & data$tau != "1" & data$cross_long == "C" )
MA <- 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_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"
 
  #The summary measure we want to calculate- fisher z
  
  #grouping variable
  
) #print grouping variable

summary(MA, digits = 2, pval=TRUE)

## Number of studies combined: k = 48
## 
##                         COR            95%-CI    t p-value
## Random effects model 0.1403 [ 0.0543; 0.2242] 3.27  0.0020
## Prediction interval         [-0.4033; 0.6107]             
## 
## Quantifying heterogeneity:
##  tau^2 = 0.0780 [0.0462; 0.1295]; tau = 0.2792 [0.2149; 0.3599];
##  I^2 = 87.3% [83.9%; 90.0%]; H = 2.81 [2.49; 3.16]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  354.31   45 < 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(
 MA,
  yaxis= "se",
 legend = TRUE,
 main = "Standard Error",
 studlab = TRUE,
 level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour

## [1] "#808080" "#B3B3B3" "#E6E6E6"

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

8 Memory amyloid longitudinal

data <- subset(data_full, table1 == 1, exclude != 0)
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"
 
  #The summary measure we want to calculate- fisher z
  
  #grouping variable
  
) #print grouping variable

summary(MAL, digits = 2, pval=TRUE)

## Number of studies combined: k = 15
## 
##                         COR            95%-CI    t p-value
## Random effects model 0.3069 [ 0.1007; 0.4878] 3.15  0.0071
## Prediction interval         [-0.4668; 0.8145]             
## 
## Quantifying heterogeneity:
##  tau^2 = 0.1350 [0.0676; 0.3510]; tau = 0.3674 [0.2600; 0.5924];
##  I^2 = 94.6% [92.5%; 96.1%]; H = 4.30 [3.66; 5.06]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  259.23   14 < 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(
 MAL,
  yaxis= "se",
 legend = TRUE,
 main = "Standard Error",
 studlab = TRUE,
 level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour

## [1] "#808080" "#B3B3B3" "#E6E6E6"

Doraiswamy Florbetapir F, 69 NC 18 Farrel Timmers T doi: 10.1016/j.pdpdt.2019.04.022 , Amsterdam, 107 NC , Florbetepan - checked van Harten Bilgen

9 Memory Tau baseline

data <- subset(data_full, table1 == 1, exclude != 0)
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"
 
  #The summary measure we want to calculate- fisher z
  
  #grouping variable
  
) #print grouping variable

summary(MT, digits = 2, pval=TRUE)

## Number of studies combined: k = 7
## 
##                         COR            95%-CI    t p-value
## Random effects model 0.0982 [-0.1849; 0.3663] 0.84  0.4310
## Prediction interval         [-0.6084; 0.7179]             
## 
## Quantifying heterogeneity:
##  tau^2 = 0.0844 [0.0276; 0.5597]; tau = 0.2905 [0.1662; 0.7481];
##  I^2 = 91.1% [83.4%; 95.2%]; H = 3.36 [2.46; 4.58]
## 
## Test of heterogeneity:
##      Q d.f.  p-value
##  56.29    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(
 MT,
  yaxis= "se",
 legend = TRUE,
 main = "Standard Error",
 studlab = TRUE,
 level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour

## [1] "#808080" "#B3B3B3" "#E6E6E6"

10 Memory Tau longitudinal

data <- subset(data_full, table1 == 1, exclude != 0)
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"
 
  #The summary measure we want to calculate- fisher z
  
  #grouping variable
  
) #print grouping variable

summary(MTL, digits = 2, pval=TRUE)

## Number of studies combined: k = 3
## 
##                         COR            95%-CI    t p-value
## Random effects model 0.2205 [-0.3714; 0.6850] 1.57  0.2569
## Prediction interval         [-0.9958; 0.9983]             
## 
## Quantifying heterogeneity:
##  tau^2 = 0.0472 [0.0086; 2.1927]; tau = 0.2172 [0.0929; 1.4808];
##  I^2 = 88.7% [69.0%; 95.9%]; H = 2.98 [1.80; 4.95]
## 
## Test of heterogeneity:
##      Q d.f. p-value
##  17.77    2  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(
 MTL,
  yaxis= "se",
 legend = TRUE,
 main = "Standard Error",
 studlab = TRUE,
 level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour

## [1] "#808080" "#B3B3B3" "#E6E6E6"

11 Global amyloid baseline

data <- subset(data_full, table1 == 1, exclude != 0)
data_global_amyloid <-
  subset(data, table1== "1" & data$global_composite == "1" & data$amyloid == "1" & data$tau != "1" & data$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"
  #The summary measure we want to calculate- fisher z
  
  #grouping variable
  
) #print grouping variable

summary(GA, digits = 2, pval=TRUE)

## Number of studies combined: k = 61
## 
##                         COR            95%-CI    t  p-value
## Random effects model 0.1543 [ 0.0908; 0.2164] 4.83 < 0.0001
## Prediction interval         [-0.2981; 0.5500]              
## 
## Quantifying heterogeneity:
##  tau^2 = 0.0525 [0.0315; 0.0807]; tau = 0.2291 [0.1775; 0.2840];
##  I^2 = 84.9% [81.3%; 87.8%]; H = 2.57 [2.31; 2.87]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  391.05   59 < 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(
 GA,
  yaxis= "se",
 legend = TRUE,
 main = "Standard Error",
 studlab = TRUE,
 level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour

## [1] "#808080" "#B3B3B3" "#E6E6E6"

12 Global amyloid longitudinal

data <- subset(data_full, table1 == 1, exclude != 0)
data_global_amyloidL <-
  subset(data, table1== "1" & data$global_composite == "1" & data$amyloid == "1" & data$tau != "1" & data$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 measure we want to calculate- fisher z
  
  #grouping variable
  
) #print grouping variable

summary(GAL, digits = 2, pval=TRUE)

## Number of studies combined: k = 28
## 
##                         COR            95%-CI    t p-value
## Random effects model 0.2570 [ 0.1162; 0.3877] 3.69  0.0010
## Prediction interval         [-0.4711; 0.7768]             
## 
## Quantifying heterogeneity:
##  tau^2 = 0.1369 [0.0691; 0.2721]; tau = 0.3699 [0.2629; 0.5216];
##  I^2 = 94.3% [92.7%; 95.5%]; H = 4.18 [3.71; 4.72]
## 
## Test of heterogeneity:
##       Q d.f.  p-value
##  472.72   27 < 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(
 GAL,
  yaxis= "se",
 legend = TRUE,
 main = "Standard Error",
 studlab = TRUE,
 level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour

## [1] "#808080" "#B3B3B3" "#E6E6E6"

13 Global tau baseline

data <- subset(data_full, table1 == 1, exclude != 0)
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"
 
  #The summary measure we want to calculate- fisher z
  
  #grouping variable
  
) #print grouping variable

summary(GT, digits = 2, pval=TRUE)

## Number of studies combined: k = 8
## 
##                         COR            95%-CI    t p-value
## Random effects model 0.2075 [ 0.0294; 0.3728] 2.75  0.0286
## Prediction interval         [-0.2695; 0.6027]             
## 
## Quantifying heterogeneity:
##  tau^2 = 0.0337 [0.0066; 0.2157]; tau = 0.1836 [0.0811; 0.4644];
##  I^2 = 77.3% [52.6%; 89.1%]; H = 2.10 [1.45; 3.03]
## 
## Test of heterogeneity:
##      Q d.f. p-value
##  26.38    6  0.0002
## 
## 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(
 GT,
  yaxis= "se",
 legend = TRUE,
 main = "Standard Error",
 studlab = TRUE,
 level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour

## [1] "#808080" "#B3B3B3" "#E6E6E6"

14 Global tau longitudinal

data <- subset(data_full, table1 == 1, exclude != 0)
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"
 
  #The summary measure we want to calculate- fisher z
  
  #grouping variable
  
) #print grouping variable

summary(GTL, digits = 2, pval=TRUE)

## Number of studies combined: k = 5
## 
##                         COR            95%-CI    t p-value
## Random effects model 0.1308 [-0.0408; 0.2949] 2.12  0.1015
## Prediction interval         [-0.2725; 0.4950]             
## 
## Quantifying heterogeneity:
##  tau^2 = 0.0128 [0.0015; 0.1398]; tau = 0.1133 [0.0392; 0.3739];
##  I^2 = 73.4% [33.6%; 89.3%]; H = 1.94 [1.23; 3.06]
## 
## Test of heterogeneity:
##      Q d.f. p-value
##  15.03    4  0.0046
## 
## 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(
 GTL,
  yaxis= "se",
 legend = TRUE,
 #main = "Standard Error",
 studlab = TRUE,
 level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour

## [1] "#808080" "#B3B3B3" "#E6E6E6"