A flexible micro-randomized trial design and sample size considerations

Abstract

Technological advancements have made it possible to deliver mobile health interventions to individuals. A novel framework that has emerged from such advancements is the just-in-time adaptive intervention, which aims to suggest the right support to the individuals when their needs arise. The micro-randomized trial design has been proposed recently to test the proximal effects of the components of these just-in-time adaptive interventions. However, the extant micro-randomized trial framework only considers components with a fixed number of categories added at the beginning of the study. We propose a more flexible micro-randomized trial design which allows addition of more categories to the components during the study. Note that the number and timing of the categories added during the study need to be fixed initially. The proposed design is motivated by collaboration on the Diabetes and Mental Health Adaptive Notification Tracking and Evaluation study, which learns to deliver effective text messages to encourage physical activity among patients with diabetes and depression. We developed a new test statistic and the corresponding sample size calculator for the flexible micro-randomized trial using an approach similar to the generalized estimating equation for longitudinal data. Simulation studies were conducted to evaluate the sample size calculators and an R shiny application for the calculators was developed.

Keywords

mHealth just-in-time adaptive intervention micro-randomized trial generalized estimating equation longitudinaldata

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References

Adibi

. Mobile Health: A Technology Road Map. Cham: Springer, 2015.

Lewis

Uhrig

Bann

et al. Tailored text messaging intervention for HIV adherence: a proof-of-concept study. Health Psychol 2013; 32: 248–253.

King

Castro

Buman

et al. Behavioral impacts of sequentially versus simultaneously delivered dietary plus physical activity interventions: the CALM trial. Ann Behav Med 2013; 46: 157–168.

Alessi

Petry

. A randomized study of cellphone technology to reinforce alcohol abstinence in the natural environment. Addiction 2013; 108: 900–909.

Free

Phillips

Galli

et al. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med 2013; 10: e1001362.

Intille

. Ubiquitous computing technology for just-in-time motivation of behavior change. Stud Health Technol Inform 2004; 107: 1434–1437.

Patrick

Griswold

Raab

et al. Health and the mobile phone. Am J Prev Med 2008; 35: 177–181.

Nahum-Shani

Smith

Spring

et al. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med 2018; 52: 446–462.

Klasnja

Hekler

Shiffman

et al. Micro-randomized trials: an experimental design for developing just-in-time adaptive interventions. Health Psychol 2015; 34: 1220–1228.

10.

Liao

Klasnja

Tewari

et al. Sample size calculations for micro-randomized trials in mHealth. Stat Med 2016; 35: 1944–1971.

11.

Klasnja

Smith

Seewald

et al. Efficacy of contextually tailored suggestions for physical activity: a micro-randomized optimization trial of HeartSteps. Ann Behav Med 2019; 53: 573–582.

12.

Liao

Dempsey

Sarker

et al. Just-in-time but not too much: determining treatment timing in mobile health. Proc ACM Interact Mob Wearable Ubiquitous Technol 2018; 2: 179.

13.

Aguilera

Figueroa

Hernandez-Ramos

et al. An mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE study. BMJ Open 2020; 10: e034723.

14.

Figueroa

Hernandez-Ramos

Boone

et al. A text messaging intervention for coping with social distancing during COVID-19 (StayWell at home): protocol for a randomized controlled trial. JMIR Res Protoc 2021; 10: e23592.

15.

Ventz

Cellamare

Parmigiani

et al. Adding experimental arms to platform clinical trials: randomization procedures and interim analyses. Biostatistics 2017; 19: 199–215.

16.

Lee

Wason

Stallard

. To add or not to add a new treatment arm to a multiarm study: a decision-theoretic framework. Stat Med 2019; 38: 3305–3321.

17.

Mei

May

Heap

et al. Rapid development studio: an intensive, iterative approach to designing online learning. Sage Open 2021; 11: 1–9.

18.

DeVries

Truong

Kwint

et al. Crowd-designed motivation: motivational messages for exercise adherence based on behavior change theory. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems 2016, pp. 297–308.

19.

Williams

Kim

Rafferty

et al. AXIS: generating Explanations at scale with learnersourcing and machine learning. In Proceedings of the 3rd (2016) ACM Conference on Learning @ Scale 2016; 379–388.

20.

Koning

Hasan

Chatterji

. Experimentation and start-up performance: evidence from A/B testing. Manage Sci 2022; 68: 6434–6453.

21.

Fabijan

Dmitriev

McFarland

et al. Experimentation growth: evolving trustworthy A/B testing capabilities in online software companies. J Softw: Evol Process 2018; 30: e2113.

22.

Figueroa

Deliu

Chakraborty

et al. Daily motivational text-messages to promote physical activity in university students: results from a micro-randomized trial. Ann Behav Med 2022; 56: 212–218.

23.

Boruvka

Almirall

Witkiewitz

et al. Assessing time-varying causal effect moderation in mobile health. J Am Stat Assoc 2018; 113: 1112–1121.

24.

Seewald

Smith

Lee

et al. Practical considerations for data collection and management in mobile health micro-randomized trials. Stat Biosci 2019; 11: 355–370.

25.

Kowalski

Zhang

et al. Power analyses for longitudinal trials and other clustered designs. Stat Med 2004; 23: 2799–2815.

26.

Mancl

DeRouen

. A covariance estimator for GEE with improved small-sample properties. Biometrics 2001; 57: 126–134.

27.

Hotelling

. The generalization of student’s ratio. Ann Math Stat 1931; 2: 360–378.

28.

Redden

. Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes. Stat Med 2015; 34: 281–296.

29.

Almirall

Compton

Gunlicks-Stoessel

et al. Designing a pilot sequential multiple assignment randomized trial for developing an adaptive treatment strategy. Stat Med 2012; 31: 1887–1902.

30.

Kelley

Maxwell

Rausch

. obtaining power or obtaining precision: delineating methods of sample-size planning. Eval Health Prof 2003; 26: 258–287.

31.

Maxwell

Kelley

Rausch

. Sample size planning for statistical power and accuracy in parameter estimation. Annu Rev Psychol 2008; 59: 537–563.

32.

Yan

Ghosh

Chakraborty

. Sample size calculation based on precision for pilot sequential multiple assignment randomized trial (SMART). Biometrical J 2021; 63: 247–271.

33.

Buuren

Groothuis-Oudshoorn

. Mice: multivariate imputation by chained equations in R. J Stat Softw 2011; 45: 1–67.

34.

Qian

Klasnja

Murphy

. Linear mixed models with endogenous covariates: modeling sequential treatments with application to a mobile health study. Stat Sci (With Discussion) 2020; 35: 375–390.

35.

Bidargaddi

Almirall

Murphy

et al. To prompt or not to prompt? A microrandomized trial of time-varying push notifications to increase proximal engagement with a mobile health app. JMIR Mhealth Uhealth 2018; 6: e10123.

36.

Magirr

Jaki

Whitehead

. A generalized Dunnett test for multi-arm multi-stage clinical studies with treatment selection. Biometrika 2012; 99: 494–501.

37.

Wason

Trippa

. A comparison of bayesian adaptive randomization and multi-stage designs for multi-arm clinical trials a comparison of Bayesian adaptive randomization and multi-stage designs for multi-arm clinical trials. Stat Med 2014; 33: 2206–2221.

38.

Dempsey

Liao

Kumar

et al. The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments. Ann App Stat 2020; 28: 2687–2708.

39.

Qian

Yoo

Klasnja

et al. Estimating time-varying causal excursion effect in mobile health with binary outcomes. Biometrika 2021; 108: 507–527.

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