data_example_for_standardized_effect

A synthetic data set in long format, one row per participant-by-decision point,
suitable for illustrating `calculate_mrt_effect_size()`.

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_example_for_standardized_effect function

A data frame with 5000 observations and 10 variables:<dl>
 <dt>id</dt>
<dd>Individual id number.</dd> <dt>decision_point</dt>
<dd>Decision point index.</dd> <dt>availability</dt>
<dd>Availability indicator (=1 available, =0 not available).</dd> <dt>prob_treatment</dt>
<dd>Randomization probability for treatment.</dd> <dt>treatment</dt>
<dd>Treatment assignment (=1 treated, =0 not treated).</dd> <dt>covariate1</dt>
<dd>Time-varying covariate 1.</dd> <dt>covariate2</dt>
<dd>Time-varying covariate 2.</dd> <dt>treatment_effect</dt>
<dd>Time-varying treatment effect used to generate outcome.</dd> <dt>sigma</dt>
<dd>Outcome noise scale.</dd> <dt>outcome</dt>
<dd>Continuous proximal outcome.</dd>
</dl>

Format

Example micro-randomized trial (MRT) data for standardized effect size — data_example_for_standardized_effect

A data frame with 5000 observations and 10 variables:<dl>
 <dt>id</dt>
<dd>Individual id number.</dd>

 <dt>decision_point</dt>
<dd>Decision point index.</dd>

 <dt>availability</dt>
<dd>Availability indicator (=1 available, =0 not available).</dd>

 <dt>prob_treatment</dt>
<dd>Randomization probability for treatment.</dd>

 <dt>treatment</dt>
<dd>Treatment assignment (=1 treated, =0 not treated).</dd>

 <dt>covariate1</dt>
<dd>Time-varying covariate 1.</dd>

 <dt>covariate2</dt>
<dd>Time-varying covariate 2.</dd>

 <dt>treatment_effect</dt>
<dd>Time-varying treatment effect used to generate outcome.</dd>

 <dt>sigma</dt>
<dd>Outcome noise scale.</dd>

 <dt>outcome</dt>
<dd>Continuous proximal outcome.</dd>


</dl>

data_example_for_standardized_effect: Example micro-randomized trial (MRT) data for standardized effect size

Description

Usage

Arguments

Format