Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 2;81(2):ujaf002.
doi: 10.1093/biomtc/ujaf002.

Data integration methods for micro-randomized trials

E Huch  1   2 , I Nahum-Shani  2 , L Potter  3 , C Lam  3 , D W Wetter  3 , W Dempsey  2   4
Affiliations

Data integration methods for micro-randomized trials

E Huch et al. Biometrics. .

Abstract

Existing statistical methods for the analysis of micro-randomized trials (MRTs) are designed to estimate causal excursion effects using data from a single MRT. In practice, however, researchers can often find previous MRTs that employ similar interventions. In this paper, we develop data integration methods that capitalize on this additional information, leading to statistical efficiency gains. To further increase efficiency, we demonstrate how to combine these approaches according to a generalization of multivariate precision weighting that allows for correlation between estimates, and we show that the resulting meta-estimator possesses an asymptotic optimality property. We illustrate our methods in simulation and in a case study involving 2 MRTs in the area of smoking cessation, finding that the proposed methods can reduce standard errors by over 30% without sacrificing asymptotic unbiasedness or calibrated statistical inference.

Keywords: causal excursion effect; meta-analysis; micro-randomized trial; mobile health.

PubMed Disclaimer

References

    1. Bang H and Robins JM (2005). Doubly robust estimation in missing data and causal inference models. Biometrics 61, 962–973. - PubMed
    1. Boruvka A, Almirall D, Witkiewitz K, and Murphy SA (2018). Assessing time-varying causal effect moderation in mobile health. Journal of the American Statistical Association 113, 1112–1121. - PMC - PubMed
    1. Carroll RJ, Ruppert D, Stefanski LA, and Crainiceanu CM (2006). Measurement error in nonlinear models: a modern perspective. Chapman and Hall/CRC.
    1. Dempsey W, Liao P, Kumar S, and Murphy SA (2020). The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments. The Annals of Applied Statistics 14, 661. - PMC - PubMed
    1. Graham BS, de Xavier Pinto CC, and Egel D (2012). Inverse probability tilting for moment condition models with missing data. The Review of Economic Studies 79, 1053–1079.

MeSH terms

LinkOut - more resources