data_distal_continuous

Simulated longitudinal dataset suitable for illustrating the `dcee()` function.
Each row corresponds to one decision point for one subject.
The distal outcome `Y` is constant within subject (because it is measured
at the end of the study, and here we append it to the long format data
as an extra column to conform with the `dcee()` function requirement.

datasets

Provides methods to analyze micro-randomized trials (MRTs) with binary treatment options. Supports four types of analyses: (1) proximal causal excursion effects, including weighted and centered least squares (WCLS) for continuous proximal outcomes by Boruvka et al. (2018) <doi:10.1080/01621459.2017.1305274> and the estimator for marginal excursion effect (EMEE) for binary proximal outcomes by Qian et al. (2021) <doi:10.1093/biomet/asaa070>; (2) distal causal excursion effects (DCEE) for continuous distal outcomes using a two-stage estimator by Qian (2025) <doi:10.1093/biomtc/ujaf134>; (3) mediated causal excursion effects (MCEE) for continuous distal outcomes, estimating natural direct and indirect excursion effects in the presence of time-varying mediators by Qian (2025) <doi:10.48550/arXiv.2506.20027>; and (4) standardized proximal effect size estimation for continuous proximal outcomes, generalizing the approach in Luers et al. (2019) <doi:10.1007/s11121-017-0862-5> to allow adjustment for baseline and time-varying covariates for improved efficiency.

Tianchen Qian

MRTAnalysis

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

Shaolin Xiang

Zhaoxi Cheng

Xinyi Song

John Dziak

Audrey Boruvka

data_distal_continuous function

a data frame with 1500 observations and 11 variables<dl>
 <dt>userid</dt>
<dd>Subject identifier</dd> <dt>dp</dt>
<dd>Decision point (1..T)</dd> <dt>X</dt>
<dd>Endogenous continuous time-varying covariate</dd> <dt>Z</dt>
<dd>Endogenous binary time-varying covariate</dd> <dt>avail</dt>
<dd>Availability indicator (0/1)</dd> <dt>A</dt>
<dd>Treatment (0/1)</dd> <dt>prob_A</dt>
<dd>Randomization probability P(A=1|H_t)</dd> <dt>A_lag1</dt>
<dd>Lagged treatment</dd> <dt>Y</dt>
<dd>Distal continuous outcome (constant per subject)</dd>
</dl>

Format

A synthetic data set of an MRT with continuous distal outcome — data_distal_continuous

a data frame with 1500 observations and 11 variables<dl>
 <dt>userid</dt>
<dd>Subject identifier</dd>

 <dt>dp</dt>
<dd>Decision point (1..T)</dd>

 <dt>X</dt>
<dd>Endogenous continuous time-varying covariate</dd>

 <dt>Z</dt>
<dd>Endogenous binary time-varying covariate</dd>

 <dt>avail</dt>
<dd>Availability indicator (0/1)</dd>

 <dt>A</dt>
<dd>Treatment (0/1)</dd>

 <dt>prob_A</dt>
<dd>Randomization probability P(A=1|H_t)</dd>

 <dt>A_lag1</dt>
<dd>Lagged treatment</dd>

 <dt>Y</dt>
<dd>Distal continuous outcome (constant per subject)</dd>


</dl>

data_distal_continuous: A synthetic data set of an MRT with continuous distal outcome

Description

Usage

Arguments

Format