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04_ancova/exercises-ancova1.md

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+Exercise: Analysis and power simulation for baseline/follow-up
+measurements
+================
+
+## Shoulder pain and acupuncture
+
+1.  Reanalyze the original data
+    - Re-estimate the ANCOVA model for the Kleinhenz et al. (1999)
+      [data](../data/kleinhenz.txt)
+2.  Run a power simulation for a replication study
+    1.  Draw plausible pre-CMS values
+    2.  Specify the minimum relevant average treatment effect (ATE)
+    3.  Set the remaining parameters to plausible values
+    4.  What is the sample size required for the test to detect the
+        effect with 80% power?
+    5.  How robust is the power simulation when you repeat it with a new
+        set of pre-CMS values? Try it!
+    6.  Recover the parameters of the ANCOVA model
+
+### References
+
+<div id="refs" class="references csl-bib-body hanging-indent">
+
+<div id="ref-KleinhenzStreitberger99" class="csl-entry">
+
+Kleinhenz, J., K. Streitberger, J. Windeler, A. Güßbacher, G. Mavridis,
+and E. Martin. 1999. “Randomised Clinical Trial Comparing the Effects of
+Acupuncture and a Newly Designed Placebo Needle in Rotator Cuff
+Tendinitis.” *Pain* 83 (2): 235–41.
+
+</div>
+
+</div>

04_ancova/exercises-ancova2.md

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+Exercise: Analysis and power simulation for baseline/follow-up
+measurements
+================
+
+## MASS anorexia data
+
+1.  Analyze the original data:
+    - In R, see ?MASS::anorexia
+    - 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)
+```
+
+- 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.
+
+### Reference