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Introduction to the pimeta package

Kengo Nagashima

2019-09-17

The pimeta package is easy. Load your data and then pass it the pima function!

library("pimeta")
library("ggplot2")

## Warning: package 'ggplot2' was built under R version 3.6.1

data(sbp, package = "pimeta")

# a parametric bootstrap prediction interval
piboot <- pima(
  y        = sbp$y,      # effect size estimates
  se       = sbp$sigmak, # within studies standard errors
  B        = 25000,      # number of bootstrap samples
  seed     = 14142135,   # random number seed
  parallel = 4           # multi-threading     
)
piboot

## 
## Prediction & Confidence Intervals for Random-Effects Meta-Analysis
## 
## A parametric bootstrap prediction and confidence intervals
##  Heterogeneity variance: DerSimonian-Laird
##  Variance for average treatment effect: Hartung (Hartung-Knapp)
## 
## No. of studies: 10
## 
## Average treatment effect [95% prediction interval]:
##  -0.3341 [-0.8807, 0.2240]
##  d.f.: 9
## 
## Average treatment effect [95% confidence interval]:
##  -0.3341 [-0.5613, -0.0985]
##  d.f.: 9
## 
## Heterogeneity measure
##  tau-squared: 0.0282
##  I-squared:  70.5%

plot(piboot, base_size = 10, studylabel = sbp$label)

Several type of methods ("HTS", "HK", "SJ", …) are supported.

# Higgins-Thompson-Spiegelhalter prediction interval
pima(sbp$y, sbp$sigmak, method = "HTS")

## 
## Prediction & Confidence Intervals for Random-Effects Meta-Analysis
## 
## Higgins-Thompson-Spiegelhalter prediction and confidence intervals
##  Heterogeneity variance: DerSimonian-Laird
##  Variance for average treatment effect: approximate
## 
## No. of studies: 10
## 
## Average treatment effect [95% prediction interval]:
##  -0.3341 [-0.7598, 0.0917]
##  d.f.: 8
## 
## Average treatment effect [95% confidence interval]:
##  -0.3341 [-0.5068, -0.1613]
##  d.f.: 9
## 
## Heterogeneity measure
##  tau-squared: 0.0282
##  I-squared:  70.5%

The convert_bin() function converts binary outcome data to effect size estimates and within studies standard errors vectors. A data set of 13 placebo-controlled trials with cisapride that was reported by Hartung and Knapp (Stat Med., 2001, doi:10.1002/sim.1009) was analyzed below. Estimated values on the logarithmic scale can be back-transformed to the original scale with the trans option (in print and plot).

m1 <- c(15,12,29,42,14,44,14,29,10,17,38,19,21)
n1 <- c(16,16,34,56,22,54,17,58,14,26,44,29,38)
m2 <- c( 9, 1,18,31, 6,17, 7,23, 3, 6,12,22,19)
n2 <- c(16,16,34,56,22,55,15,58,15,27,45,30,38)
dat <- convert_bin(m1, n1, m2, n2, type = "logOR")
head(dat, n = 3)

##          y        se
## 1 2.098986 0.9847737
## 2 3.357026 1.0165653
## 3 1.565232 0.5747840

pibin <- pima(dat$y, dat$se, seed = 2236067, parallel = 4)
print(pibin, digits = 3, trans = "exp")

## 
## Prediction & Confidence Intervals for Random-Effects Meta-Analysis
## 
## A parametric bootstrap prediction and confidence intervals
##  Heterogeneity variance: DerSimonian-Laird
##  Variance for average treatment effect: Hartung (Hartung-Knapp)
## 
## No. of studies: 13
## 
## Average treatment effect [95% prediction interval]:
##  4.141 [0.533, 33.692]
##  d.f.: 12
##  Scale: exponential transformed
## 
## Average treatment effect [95% confidence interval]:
##  4.141 [2.224, 7.820]
##  d.f.: 12
##  Scale: exponential transformed
## 
## Heterogeneity measure
##  tau-squared: 0.718
##  I-squared:  69.9%

binlabel <- c(
   "Creytens", "Milo", "Francois and De Nutte", "Deruyttere et al.",
   "Hannon", "Roesch", "De Nutte et al.", "Hausken and Bestad",
   "Chung", "Van Outryve et al.", "Al-Quorain et al.", "Kellow et al.",
   "Yeoh et al.")
plot(pibin, digits = 2, base_size = 10, studylabel = binlabel, trans = "exp")

Save a plot to PNG:

png("forestplot.png", width = 500, height = 300, family = "Arial")
plot(piboot, digits = 2, base_size = 18, studylabel = sbp$label)
dev.off()

or

p <- plot(piboot, digits = 2, base_size = 10, studylabel = sbp$label)
ggsave("forestplot.png", p, width = 5, height = 3, dpi = 150)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.