Non-binary Treatment Specification

In propertee: Standardization-Based Effect Estimation with Optional Prior Covariance Adjustment

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(propertee)

Binary Treatment

When creating a StudySpecification, handling binary treatment variables is straightforward. If the treatment variable is either numeric with only values 0/1, or is logical, then lmitt() will estimate a treatment effect of the difference between the outcome in the treated group (1 or TRUE) versus the control group (0 or FALSE).

Missing treatment status

In all cases (binary and non-binary), missing values are allowed and any units of assignment with missing treatment values are excluded from models fit via lmitt().

Non-binary Treatment

However, the _spec() functions can take in any (reasonable) form of treatment assignment.

If the treatment variable is a numeric with non-binary values, it is treated as a continuous treatment effect and lmitt(y ~ 1, ... will estimate a single coefficient on treatment.

If the treatment variable is a character, it is treated as a multi-level treatment variable and lmitt(y ~ 1, ... will estimate treatment effects against a reference category. The reference category is the first level defined according to R's comparison of characters.

factor and ordered objects are tricky to deal with, so while a StudySpecification can be created with factor or ordered treatment variables, lmitt() will refuse to estimate a model unless it is also provided a dichotomy (see below).

Dichotomzing a Non-binary Treatment

Studies may offer treatment to units at different times or provide treatment to units in varying intensities. Researchers may be interested in estimating treatment effects at different times or given a certain threshold of provided treatment, however. propertee accommodates these wishes by storing the time or intensity of treatment for treated units in the StudySpecification, then offering a dichotomy= argument to the weights calculation functions ett()/ate() and the assginment creation function assigned() A dichotomy is presented as a formula, where the left-hand side is a logical statement defining inclusion in the treatment group, and the right-hand side is a logical statement defining inclusion in the control group. For example, if dose represents the intensity of a given treatment, we could set a threshold of 200, say, mg:

dose > 200 ~ dose <= 200

All units of assignment with dose above 200 are treated units, and all units of assignment with dose of 200 or below are control units.

A . can be used to define either group as the inverse of the other. For example, the above dichotomy could be defined as either of

dose > 200 ~ .
. ~ dose <= 200

Any units of assignment not assigned to either treatment or control are assumed to have NA for a treatment status and will be ignored in the estimation of treatment effects.

dose >= 300 ~ dose <= 100

In this dichotomy, units of assignment in the range (100,300) are ignored.

An Example

data(simdata)
table(simdata$dose)
spec1 <- rct_spec(dose ~ uoa(uoa1, uoa2), data = simdata)
summary(spec1)

head(ate(spec1, data = simdata, dichotomy = dose >= 300 ~ dose <= 100))

head(assigned(spec1, data = simdata, dichotomy = dose >= 300 ~ dose <= 100))

propertee documentation built on Aug. 22, 2025, 1:09 a.m.