Exercise: Analysis and power simulation for baseline/follow-up
measurements
================

## MASS anorexia data

Data preparation

``` r
data(anorexia, package = "MASS")
dat <-
  subset(anorexia, Treat != "Cont") |>  # exclude control group
  droplevels()                          # drop empty factor levels
lbs2kg <- 0.4535924
dat$Prewt  <- lbs2kg * dat$Prewt        # to kg
dat$Postwt <- lbs2kg * dat$Postwt
lattice::xyplot(Postwt ~ Prewt, dat, groups = Treat,
                type = c("g", "r", "p"), auto.key = TRUE)
```

1.  Analyze the original data:
    - In R, see ?MASS::anorexia
    - Estimate the average treatment effect (ATE) for FT relative to
      CBT.
    - What is the 95% CI for the ATE?
    - What are the pre- and post-weight means for the two groups?
    - What are the baseline-adjusted means for the two groups?
2.  Run a power simulation for a replication study:
    - Draw plausible pre-weights.
    - Specify the minimum relevant effect.
    - Set the remaining parameters to plausible values.
    - What is the sample size required for the test to detect the effect
      with 80% power?
    - How robust is the power simulation when you repeat it with a new
      set of pre-weights? Try it!
    - Recover the parameters of the ANCOVA model.
3.  Create a renderable R script or an R Markdown file that includes
    - a header with title, author, date
    - at least one section head line
    - the homework questions and your answers
    - the R code, output, and plots (if any)

    Render the R or Rmd file to HTML.
