Micro-randomized Trials with Categorical Treatments: Causal Effect Estimation and Sample Size Calculation
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908331495391232 |
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| author | Lin, Jeremy Qian, Tianchen |
| author_facet | Lin, Jeremy Qian, Tianchen |
| contents | Micro-randomized trials (MRTs) are widely used to assess the marginal and moderated effect of mobile health (mHealth) treatments delivered via mobile devices. In many applications, the mHealth treatments are categorical with multiple levels such as different types of message contents, but existing analysis and sample size calculation methods for MRTs only focus on binary treatment options (i.e., prompt vs. no prompt). We extended the causal excursion effect definition and the weighted and centered least squares estimator to MRTs with categorical treatments. Furthermore, we developed a sample size formula for comparing categorical treatment levels, and proved the type I error and power guarantee under working assumptions. We conducted extensive simulations to assess type I error and power under assumption violations, and we provided practical guidelines for using the sample size formula to ensure adequate power in most real-world scenarios. We illustrated the proposed estimator and sample size formula using the HeartSteps MRT. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_15484 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Micro-randomized Trials with Categorical Treatments: Causal Effect Estimation and Sample Size Calculation Lin, Jeremy Qian, Tianchen Methodology Micro-randomized trials (MRTs) are widely used to assess the marginal and moderated effect of mobile health (mHealth) treatments delivered via mobile devices. In many applications, the mHealth treatments are categorical with multiple levels such as different types of message contents, but existing analysis and sample size calculation methods for MRTs only focus on binary treatment options (i.e., prompt vs. no prompt). We extended the causal excursion effect definition and the weighted and centered least squares estimator to MRTs with categorical treatments. Furthermore, we developed a sample size formula for comparing categorical treatment levels, and proved the type I error and power guarantee under working assumptions. We conducted extensive simulations to assess type I error and power under assumption violations, and we provided practical guidelines for using the sample size formula to ensure adequate power in most real-world scenarios. We illustrated the proposed estimator and sample size formula using the HeartSteps MRT. |
| title | Micro-randomized Trials with Categorical Treatments: Causal Effect Estimation and Sample Size Calculation |
| topic | Methodology |
| url | https://arxiv.org/abs/2504.15484 |