# Missing data in microrandomized trials: Challenges and opportunities

**Authors:** Shiyu Zhang, John J. Dziak, Lizbeth Benson, Jamie R. T. Yap, Dusti R. Jones, Cho Y. Lam, Lindsey N. Potter, David W. Wetter, Inbal Nahum-Shani

PMC · DOI: 10.3758/s13428-025-02885-y · Behavior Research Methods · 2025-11-14

## TL;DR

This paper discusses how missing data in microrandomized trials can affect the development of real-time psychological interventions and offers strategies to manage them.

## Contribution

The paper introduces a conceptual framework for anticipating and managing missing data in microrandomized trials.

## Key findings

- Missing data in MRTs can lead to bias and increased variance in results.
- Strategies for minimizing and handling missing data are proposed to improve JITAI development.
- The MARS study illustrates the practical implications of missing data in MRTs.

## Abstract

The vision of leveraging digital technologies to deliver real-time psychological interventions in everyday settings is realized via just-in-time adaptive interventions (JITAI) – an intervention design that guides the use of rapidly changing information about a person’s internal states and contexts to decide whether and how to intervene in daily life. Microrandomized trials (MRTs) were developed as an experimental design to address scientific questions about how to best construct JITAIs, enabling scientists to investigate whether, what type, and under what conditions, intervention delivery can promote behavior change. However, missing data present challenges to the ability of MRTs to inform the development of JITAIs. This article articulates the multiple sources of missing data that can manifest in MRT studies, discusses how such missing data can impact (1) bias, (2) variance, and (3) the future implementation of JITAIs, and discusses strategies for both minimizing missing data in an MRT design and handling missing data when they occur. The overarching goal is to provide a conceptual framework that will guide future investigators in anticipating missing data and making informed decisions to manage them. Throughout, we illustrate concepts using existing data from the Mobile Assistance for Regulating Smoking (MARS) study. MARS (n = 99) involved a 10-day MRT that included up to six randomizations per person per day.

The online version contains supplementary material available at 10.3758/s13428-025-02885-y.

## Full-text entities

- **Diseases:** pain (MESH:D010146), MARS (MESH:D015208), depressive symptoms (MESH:D003866), JITAI (MESH:D018489), mental health problems (MESH:D000076082), anxiety disorders (MESH:D001008), Cancer (MESH:D009369)
- **Chemicals:** water (MESH:D014867), alcohol (MESH:D000438), nicotine (MESH:D009538)
- **Species:** Homo sapiens (human, species) [taxon 9606], Nicotiana tabacum (American tobacco, species) [taxon 4097]

## Full text

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## Figures

3 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12618347/full.md

## References

5 references — full list in the complete paper: https://tomesphere.com/paper/PMC12618347/full.md

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Source: https://tomesphere.com/paper/PMC12618347