Inverse Probability Weights From User-Supplied Probabilities

Computes inverse probability weights from probabilities (or densities) of the observed exposure that you estimated yourself, for example with a machine-learning model or SuperLearner. Point treatment weights are num / den; with id and timevar, the ratios are multiplied cumulatively over time within each subject, as in ipwtm.

Usage

ipwprob(den, num = NULL, id = NULL, timevar = NULL, trunc = NULL)

Arguments

den: Numeric vector: for each row, the probability (or density) of the observed exposure given the covariates of the denominator model.
num: Optional numeric vector of the same length: the probability (or density) of the observed exposure given the covariates of the numerator model. If NULL, unstabilized weights (numerator = 1) are computed.
id: Optional vector of subject identifiers, same length as den. When given, weights are cumulated over time within each subject.
timevar: Numeric vector of follow-up times, same length as den; required when id is given. It sets the order in which the ratios are multiplied within a subject.
trunc: Optional truncation percentile between 0 and 0.5 (e.g., 0.01 for 1st/99th percentiles).

Value

A list of class "ipwprob" containing:

ipw.weights: Numeric vector of inverse probability weights, in the order of the input.
weights.trunc: Numeric vector of truncated weights (if trunc is specified).
call: The original function call.

Details

For time-varying weights of type = "first" or "cens" (see ipwtm), set the probabilities to 1 for the rows after the first switch, so that those rows do not change the cumulative weight.

Author

Hung Thai Tran hungtt@oucru.org

Examples

# Point treatment: probabilities from any model, here a logistic regression
set.seed(1)
d <- data.frame(l = rnorm(200))
d$a <- rbinom(200, 1, plogis(d$l))
p <- fitted(glm(a ~ l, family = binomial, data = d))
w <- ipwprob(den = ifelse(d$a == 1, p, 1 - p),
             num = ifelse(d$a == 1, mean(d$a), 1 - mean(d$a)))
summary(w$ipw.weights)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.5892  0.7849  0.9089  0.9981  1.1440  2.6291 

# Time-varying: the ratios are multiplied within each subject over time
long <- data.frame(id = rep(1:3, each = 2), time = rep(1:2, 3),
                   p = c(0.5, 0.8, 0.4, 0.9, 0.6, 0.7))
ipwprob(den = long$p, id = long$id, timevar = long$time)$ipw.weights
#> [1] 2.000000 2.500000 2.500000 2.777778 1.666667 2.380952