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Estimate the ATE of the mean difference in one multilevel subgroup defined by the proportions

Source: R/ATE_continuous.R
estmean.multilevel.subgroup.Rd

Scores are adjusted to the opposite sign if higher.y == FALSE; scores stay the same if higher.y == TRUE; this is because subgroups defined in estmean.multilevel.subgroup() start from the lowest to the highest adjusted scores, and higher adjusted scores should always represent high responders of trt=1

Usage

estmean.multilevel.subgroup(
  y,
  x.cate,
  x.ps,
  trt,
  score,
  higher.y,
  prop,
  ps.method = "glm",
  minPS = 0.01,
  maxPS = 0.99
)

Arguments

y

Observed outcome; vector of size n (observations)

x.cate

Matrix of p.cate baseline covariates; dimension n by p.cate (covariates in the outcome model)

x.ps

Matrix of p.ps baseline covariates (plus a leading column of 1 for the intercept); dimension n by p.ps + 1 (covariates in the propensity score model plus intercept)

trt

Treatment received; vector of size n units with treatment coded as 0/1

score

Estimated CATE scores for all n observations from one of the six methods (boosting, linear regression, two regressions, contrast regression, random forest, generalized additive model); vector of size n

higher.y

A logical value indicating whether higher (TRUE) or lower (FALSE) values of the outcome are more desirable. Default is TRUE.

prop

Proportions corresponding to percentiles in the estimated CATE scores that define subgroups to calculate ATE for; vector of floats in `[0, 1]` always starting with 0 and ending with 1: Each element of prop represents inclusive cutoffs in the multilevel subgroup and the length of prop is number of categories in the multilevel subgroup

ps.method

A character value for the method to estimate the propensity score. Allowed values include one of: 'glm' for logistic regression with main effects only (default), or 'lasso' for a logistic regression with main effects and LASSO penalization on two-way interactions (added to the model if interactions are not specified in ps.model). Relevant only when ps.model has more than one variable.

minPS

A numerical value (in `[0, 1]`) below which estimated propensity scores should be truncated. Default is 0.01.

maxPS

A numerical value (in `(0, 1]`) above which estimated propensity scores should be truncated. Must be strictly greater than minPS. Default is 0.99.

Value

estimated ATEs of all categories in the one multilevel subgroup; vector of size equal to the length of categories in the multilevel subgroup