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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Missing data in microrandomized trials: Challenges and opportunities.

Shiyu Zhang1, John J Dziak2, Lizbeth Benson2

  • 1Institute for Social Research, University of Michigan, 426 Thompson St, Ann Arbor, Michigan, 48104, USA. zsy@umich.edu.

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Summary
This summary is machine-generated.

Missing data in microrandomized trials (MRTs) challenge the development of just-in-time adaptive interventions (JITAIs). This study provides a framework to manage missing data, improving JITAI design and implementation for digital health interventions.

Keywords:
Just-in time adaptive interventionsMicrorandomized trialsMissing data

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Area of Science:

  • Digital Health
  • Behavioral Science
  • Clinical Trials

Background:

  • Just-in-time adaptive interventions (JITAIs) leverage digital technologies for real-time psychological support.
  • Microrandomized trials (MRTs) are crucial for optimizing JITAI design and behavior change promotion.
  • Missing data pose significant challenges to the validity and utility of MRTs for JITAI development.

Purpose of the Study:

  • To identify sources of missing data in MRT studies.
  • To analyze the impact of missing data on bias, variance, and JITAI implementation.
  • To propose strategies for minimizing and handling missing data in MRTs.

Main Methods:

  • Conceptual framework development.
  • Analysis of missing data sources and their impact.
  • Illustration using data from the Mobile Assistance for Regulating Smoking (MARS) study.

Main Results:

  • Missing data can arise from various sources in MRTs.
  • Missing data can introduce bias and increase variance in trial results.
  • Effective strategies are needed to mitigate and manage missing data.

Conclusions:

  • Addressing missing data is essential for robust JITAI research.
  • A proactive framework for managing missing data will guide future MRT investigations.
  • This work aims to enhance the development and implementation of digital psychological interventions.