sface {TheSFACE}

Subtype Free Average Causal Effect

Description

A function to estimate the Subtype Free Average Causal Effect.

Usage

sface(
  stand_formula,
  iptw_formula,
  exposure,
  outcome,
  df,
  subtype = c(1, 2),
  scale = c("diff", "RR"),
  method = c("stand", "IPTW", "DR"),
  lambda1 = 0,
  lambda2 = 0,
  weight = 1,
  MultPer = 1
)

Arguments

stand_formula

A formula for standartization and DR, y ~ A + X, the outcome as a function of the exposure and covariates

iptw_formula

A formula for IPTW and DR, A ~ X, the exposure as a function of the covariates.

exposure

The treatment/exposure vector pf length n. Must be encoded 1 for treated and 0 for untreated.

outcome

The categorical outcome vector of length n. Must be encoded 0 for disease-free, 1 for the first subtype and 2 for the second subtype.

df

a data frame with columns for the outcome, expousre and covariates.

subtype

Should the SF-ACE be estimated for subtype 1 or subtype 2

scale

Should the SF-ACE be estimated on the difference or risk ratio scale.

method

Which method to use when adjusting for covariates, possibilities include standardization ("stand"), Inverse Probability Treatment Weighting ("IPTW"), and doubly robust estimation ("DR")

lambda1

sensitivity parameter for subtype 1. Can range between 0 (S-Monotonicity for subtype 1) and 1 (D-Monotonicity for subtype 1), Default: 0

lambda2

sensitivity parameter for subtype 2. Can range between 0 (S-Monotonicity for subtype 2) and 1 (D-Monotonicity for subtype 2), Default: 0

weight

A numerical vector of length n, holding weights to adjust for missing subtypes, Default: 1

MultPer

A numeric value indicating per how many people the effect should be calculated on the difference scale, Default: 1

Details

DETALIS

Value

a list of class "sface". In the cell "sface", there is a list holding the estimated SF_ACEs in the chosen methods and scales. In the cell "additional info", there is with additional info regarding the params used.

See Also

multinom

Examples

A <- rbinom(n = 1000, size = 1, prob = 0.5)
X1 <- rbinom(n = 1000, size = 1, prob = 0.5)
X2 <- rnorm(n = 1000, mean = 0, sd = 1)
y <- sample(c(0,1,2), 1000, replace=TRUE, prob=c(0.8, 0.1, 0.1) )
weight <- runif(n = 1000, 0,1)
df <- data.frame(y, A, X1, X2, weight)

sface(stand_formula = y ~ A + X1 + X2,
iptw_formula = A ~ X1 + X2,
exposure = "A",
outcome = "y",
df = df,
scale = c("diff","RR"),
method = c("stand", "IPTW"),
weight = "weight")