triplesmatch (version 1.1.0)

sentrip: Sensitivity analysis for a triples match in an observational study

Description

This function parallels [sensitivityfull::senfm()] for full matches. However, this function does not force the scores used for the test to be Huber's M-statistic. Instead, scores should be calculated ahead of time and then entered here. Performs either a randomization test or the corresponding Rosenbaum sensitivity analysis.

Usage

sentrip(scores, treated1, gamma = 1, alternative = "greater")

Value

Named list with 5 elements: `pval` is the upper bound on the one or two-sided P-value depending on `alternative`, `deviate` is the deviate that was compared to the Normal distribution to produce pval, `statistic` is the value of the statistic that is the sum of scores among treated units, `expectation` is the maximum expectation of this statistic for the given \(\Gamma\), and `variance` is the maximum variance of this statistic among treatment assignments that achieve the maximum expectation

Arguments

scores

A matrix of scores. Rows correspond to matched triples and the three columns correspond to the three units in the match. The first unit is the one treated unit if `treated1 == TRUE` or the one control unit if `treated1 == FALSE`. The other two columns contain the remaining two units in the match. These are control units if `treated1 == TRUE` or treated units if `treated1 == FALSE`. This can easily be created from the triples match using the [formattrip()] function with `type == "wide"`

treated1

A logical vector with length equal to the number of triples. `TRUE` if there is only one treated unit in the matched triple; `FALSE` if there are two treated units and only one control unit. This can easily be created from the triples match using the [formattrip()] function with `type == "wide"`

gamma

The sensitivity parameter \(\Gamma \geq 1\). Setting \(\Gamma = 1\) performs a randomization test that assumes ignorable treatment assignment given the matched triples

alternative

One of `greater`, `less` or `both`. `greater` implies the alternative hypothesis is that the treatment has a positive effect on the scores, `less` implies the alternative hypothesis is that the treatment has a negative effect on the scores, and `both` conducts a two-sided hypothesis test

See Also

sensitivityfull::senfm for more details, especially for the interpretation of the `expectation` and `variance` components of the output and relevant references.

formattrip for easily creating inputs to this function.

Examples

# Generate some data
set.seed(246)
n <- 30
x <- rnorm(n, 0, 3)
nt <- floor(n * 0.5)
nc <- n - nt
z <- c(rep(1, nt), rep(0, nc))
# Create a distance matrix, everything in one stratum
dist <- dist_mahal(data.frame(x = x), z, rep(1, n))[[1]]
# Create the triples match
triplesm <- triples_st(cost = dist, z = z, solver = "rlemon")
# Create an outcome
y <- 1:40
# Give the outcome some random unit names
names(y) <- sample(1:40)
# Reformat the triples match
ywide <- formattrip(m = triplesm, y = y, type = "wide")
# Turn the outcome into scores, in this case aberrant ranks
ab <- aberrantscores(ywide$ymat, 15, cutoff_dir = "less", tau = 0, treated1 = NULL)
# Conduct a one-sided hypothesis test with a bias of gamma = 1.25
sentrip(scores = ab, treated1 = ywide$treated1, gamma = 1.25, alternative = "greater")