Meta-analysis Report

# ここにメタ解析コードを入れる
library(tidyverse)
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library(metafor)
#> Warning: package 'metafor' was built under R version 4.4.1
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Analysis <- read_csv("Analysis.csv")
#> Rows: 105 Columns: 10
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (9): StudyID, Outcome, ES, Variance, ES_Type, Country, Region, PA_Measur...
#> dbl (1): Year
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dat_use <- Analysis %>%
  filter(
    ES_Type == "SMCC",
    Outcome %in% c("LifeSat", "LifeSatisfaction", "SWLS",
                   "SWB", "SWB_Total", "LifeSat_Self")
  ) %>%
  mutate(
    ES = as.numeric(ES),
    Variance = as.numeric(Variance),
    Region = ifelse(Region == "Asia", "Asia", "NonAsia")
  )

dat_clean <- dat_use %>%
  filter(!is.na(ES), !is.na(Variance), Variance > 0)

res_overall <- rma(
  yi = dat_clean$ES,
  vi = dat_clean$Variance,
  data = dat_clean,
  method = "REML"
)

summary(res_overall)
#> 
#> Random-Effects Model (k = 27; tau^2 estimator: REML)
#> 
#>   logLik  deviance       AIC       BIC      AICc   
#>   3.7450   -7.4899   -3.4899   -0.9737   -2.9682   
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.0389 (SE = 0.0116)
#> tau (square root of estimated tau^2 value):      0.1971
#> I^2 (total heterogeneity / total variability):   94.09%
#> H^2 (total variability / sampling variability):  16.93
#> 
#> Test for Heterogeneity:
#> Q(df = 26) = 423.2473, p-val < .0001
#> 
#> Model Results:
#> 
#> estimate      se    zval    pval   ci.lb   ci.ub     
#>   0.1275  0.0395  3.2250  0.0013  0.0500  0.2049  ** 
#> 
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

forest(res_overall, slab = dat_clean$StudyID)