meta <- metacont(N.HC, Mean.HC, SD.HC, N.MCI_AD, Mean.MCI_AD, SD.MCI_AD,

sm=“SMD”, data=data, studlab=paste(Author, Year))

## Warning in metacont(N.HC, Mean.HC, SD.HC, N.MCI_AD, Mean.MCI_AD, SD.MCI_AD, :

## Note, studies with non-positive values for n.e and / or n.c get no weight in

## meta-analysis.

meta$label.e <- “HC”

meta$label.c <- “MCI_AD”

print(meta, digits=2)

## SMD 95%-CI %W(fixed) %W(random)

## Bjerke 2009 NA 0.0 0.0

## Mattsson 2009 -1.13 [-1.31; -0.96] 55.9 55.9

## Hertze 2010 -0.99 [-1.43; -0.55] 8.9 8.9

## Palmqvist 2012 NA 0.0 0.0

## Spencer 2019 NA 0.0 0.0

## Hansson 2006 NA 0.0 0.0

## Hansson 2007 NA 0.0 0.0

## Hampel 2004 NA 0.0 0.0

## Herukka 2007 -1.14 [-1.59; -0.68] 8.4 8.4

## Lanari 2009 NA 0.0 0.0

## Papaliagkas 2009 NA 0.0 0.0

## Eckerstrom 2010 NA 0.0 0.0

## Kester 2011 NA 0.0 0.0

## Seppala 2011 -0.47 [-1.15; 0.22] 3.7 3.7

## Buchhave 2012 -1.04 [-1.46; -0.63] 10.2 10.2

## Parnetti 2012 NA 0.0 0.0

## Prestia 2013 NA 0.0 0.0

## Leuzy 2015 NA 0.0 0.0

## Baldeiras 2018 -1.21 [-1.57; -0.84] 13.0 13.0

## Khoonsari 2019 NA 0.0 0.0

## Santangelo 2020 NA 0.0 0.0

## Eckerstrom 2020 NA 0.0 0.0

## Brys 2009 NA 0.0 0.0

##

## Number of studies combined: k = 6

## Number of observations: o = 1749

##

## SMD 95%-CI z p-value

## Fixed effect model -1.10 [-1.23; -0.96] -16.29 < 0.0001

## Random effects model -1.10 [-1.23; -0.96] -16.29 < 0.0001

##

## Quantifying heterogeneity:

## tau^2 = 0; tau = 0; I^2 = 0.0% [0.0%; 74.6%]; H = 1.00 [1.00; 1.99]

##

## Test of heterogeneity:

## Q d.f. p-value

## 4.06 5 0.5414

##

## Details on meta-analytical method:

## - Inverse variance method

## - DerSimonian-Laird estimator for tau^2

## - Hedges’ g (bias corrected standardised mean difference)