data_binary: A synthetic data set of an MRT with binary proximal outcomes

In MRTAnalysis: Assessing Proximal, Distal, and Mediated Causal Excursion Effects for Micro-Randomized Trials

A synthetic data set of an MRT with binary proximal outcomes

Description

Baseline model:

\log E\{Y_{t+1} \mid A_t = 0, I_t = 1\} = \alpha_0 + \alpha_1 \cdot \mathrm{time} / \mathrm{total\_T} + \alpha_2 \cdot \mathbf{1}(\mathrm{time} > \mathrm{total\_T}/2).

Treatment effect model:

\log RR_t = \beta_0 + \beta_1 \cdot \mathrm{time} / \mathrm{total\_T}.

Randomization probabilities p_t cycle over 0.3, 0.5, 0.7 (with repetition). Availability is exogenous at 0.8 for all time points.

Usage

data_binary

Format

A data frame with 3000 observations and 10 variables:

userid

Individual id number.

time

Decision point index.

time_var1

Time-varying covariate 1, the \"standardized time in study\", defined as the current decision point index divided by the total number of decision points.

time_var2

Time-varying covariate 2, indicator of \"the second half of the study\", defined as whether the current decision point index is greater than the total number of decision points divided by 2.

Y

Binary proximal outcome.

A

Treatment assignment: whether the intervention is randomized to be delivered (=1) or not (=0) at the current decision point.

rand_prob

Randomization probability P(A=1) for the current decision point.

avail

Availability indicator (=1 available, =0 not available) at the current decision point.

MRTAnalysis documentation built on Sept. 9, 2025, 5:41 p.m.