Clean up RCT adjustment section #277
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LucyMcGowan
approved these changes
Oct 16, 2024
| @@ -399,8 +399,9 @@ When you have no confounders and there is a linear relationship between the expo | |||
| Even in these cases, using the methods you will learn in this book can help. | |||
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| 1. Adjusting for baseline covariates can make an estimate *more efficient* | |||
| 2. Propensity score weighting is *more efficient* than direct adjustment | |||
| 2. Propensity score weighting is *as efficient* as direct adjustment | |||
| @@ -523,7 +543,7 @@ d <- tibble( | |||
| weight = rnorm(n), | |||
| # generate the treatment from a binomial distribution | |||
| # with the probability of treatment dependent on the age and weight | |||
| treatment = rbinom(n, 1, 1 / (1 + exp(-0.01 * age - weight))), | |||
| treatment = rbinom(n, 1, plogis(-0.01 * age - weight)), | |||
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I love this and have adopted it since seeing you do it last semester!
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To match this section to the claims in the paper cited. I also modified the tables to figures.
These are mostly corrections instead of edits; I still plan to overhaul this chapter and decided to do some of the rewording and expansion of this section as part of that effort.