inst/doc/introduction.R

In metaLong: Longitudinal Meta-Analysis with Robust Variance Estimation and Sensitivity Analysis

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
  collapse  = TRUE,
  comment   = "#>",
  fig.width = 6,
  fig.height = 4
)

## ----simulate-----------------------------------------------------------------
library(metaLong)

dat <- sim_longitudinal_meta(
  k    = 10,
  times = c(0, 6, 12, 24),
  mu   = c("0" = 0.30, "6" = 0.50, "12" = 0.42, "24" = 0.20),
  tau  = 0.20,
  seed = 42
)

head(dat, 6)

## ----ml_meta------------------------------------------------------------------
meta <- ml_meta(dat, yi = "yi", vi = "vi", study = "study", time = "time")
print(meta)

## ----plot_meta----------------------------------------------------------------
plot(meta, main = "Pooled Effects Across Follow-Up")

## ----ml_sens------------------------------------------------------------------
sens <- ml_sens(dat, meta, yi = "yi", vi = "vi",
                study = "study", time = "time")
print(sens)

## ----plot_sens----------------------------------------------------------------
plot(sens)

## ----sens_summary-------------------------------------------------------------
cat("Minimum ITCV_alpha:", round(attr(sens, "itcv_min"),  3), "\n")
cat("Mean ITCV_alpha:   ", round(attr(sens, "itcv_mean"), 3), "\n")
cat("Fragile proportion:", round(attr(sens, "fragile_prop"), 3), "\n")

## ----ml_spline----------------------------------------------------------------
spl <- ml_spline(meta, df = 2)
print(spl)

## ----plot_spline--------------------------------------------------------------
plot(spl, main = "Spline Fit: Nonlinear Trajectory")

## ----ml_plot, fig.height = 6--------------------------------------------------
ml_plot(meta, sens_obj = sens, spline_obj = spl,
        main = "Longitudinal Meta-Analysis Profile")

## ----ml_benchmark, eval = TRUE------------------------------------------------
bench <- ml_benchmark(
  dat, meta, sens,
  yi         = "yi", vi = "vi", study = "study", time = "time",
  covariates = c("pub_year", "quality")
)
print(bench)

## ----ml_fragility, eval = TRUE------------------------------------------------
frag <- ml_fragility(dat, meta,
                     yi = "yi", vi = "vi", study = "study", time = "time",
                     max_k = 1L, seed = 1)
print(frag)

## ----fits---------------------------------------------------------------------
f <- fits(meta)
cat("Stored model objects:", sum(!sapply(f, is.null)), "/", length(f), "\n")