In softloud/metasim: Simulate Meta-Analytic Data

knitr::opts_chunk$set(
  eval = TRUE,
  message = FALSE,
  echo = TRUE,
  results = 'asis',
  fig.height = 3.5,
  fig.width = 4.5,
  out.width = "100%",
  warning = FALSE,
  fig.align = 'center',
  message = FALSE,
  dev = 'cairo_pdf'
)

batpigday

\begin{description} \item[batpigday]{\emph{noun} The coding equivalent of groundhogday.} \end{description}

the problem

---

\begin{center} Simulating data is a bitch. \end{center}

---

Debugging frequently dominates the time of students in mathematical science. These students know how to solve equations, and next to nothing about code.

---

New tools are emerging daily to enable researchers to avoid these timesink pitfalls.

These tools have lowered the programmatic barrier for researchers, but it still a learning curve.

---

We consider a case study in meta-analysis.

\begin{description} \item[meta-analysis] Statistical methodology for combining the results of several studies.

\end{description}

meta-analysis of medians

Conventional meta-analytic tools, such as metafor::rma, require an effect and a variance of that effect.

---

But what if the reported statistics are median and interquartile range?

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To test our proposed estimator for the variance of the sample median, I found myself repeating tasks and checks in the algorithms.

---

I tried to find a better way of debugging and writing simulations.

This lead to:

1. a packaged analysis, varameta::*, which is built on
2. the simulation package for meta-analysis data, metasim::*.

(*in development)

\vfill\null \columnbreak

escaping batpigday

\begin{center} \textbf{coding is the easiest part of coding} \end{center}

---

• Modular code, break the code into chunks.
• Reproducibility is more than set.seed(): accessibility, refactoring, integratability, versioning,

---

library(tidyverse)
library(metasim)

library(kableExtra)

# table styling
output_table <- function(df) {
  df %>% 
    janitor::adorn_rounding(skip_first_col = FALSE) %>% 
  kable(align = "c", caption = "", booktabs = TRUE) %>% 
  kable_styling(latex_options = c("striped","HOLD_position"),full_width = TRUE, font_size = 25)
}

---

Generate sample sizes for $k$ studies.

# simulate 2 studies where most have at most 25
sim_n(k = 2, min_n = 10, max_n = 25) %>% output_table()

# generate simulation dataframe
sim_df() %>% head(2) %>% select(-n) %>% output_table()

Each row of this dataframe represents a set of simulation parameters.

---

Each simulation runs a trial function.

metatrial() %>% output_table()

Each simulation runs the trial function |trials| times.

metasim() %>%
  pluck("results") %>%
  select(-coverage_count) %>%
  output_table()

For all simulations, run metasim over each row of the dataframe.

metasims() %>% output_table()

\vfill\null \columnbreak

the maths

references

This poster was created with posterdown::.

\small\printbibliography

softloud/metasim documentation built on July 15, 2019, 8:02 p.m.