Design and Analysis Considerations for a Sequentially Randomized HIV Prevention Trial
- 25 March 2020
- journal article
- research article
- Published by Springer Nature in Statistics in Biosciences
- Vol. 12 (3) , 446-467
Abstract
The TechStep study is an ongoing randomized controlled trial in HIV-negative transgender youths and young adults, which will evaluate the efficacy of mobile health interventions for reducing risk behaviors. Several mobile interventions are available, which complicates the design. To evaluate different combinations of mHealth interventions, TechStep is utilizing a sequentially randomized design. In this work, we discuss the motivation for this design, propose robust methodology for the analysis of the trial, and evaluate the methodology via simulation.Keywords
All Related Versions
Funding Information
- Foundation for the National Institutes of Health (5U19HD089881, U24HD089880)
This publication has 28 references indexed in Scilit:
- Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working ModelsJournal of Causal Inference, 2014
- Experimental design and primary data analysis methods for comparing adaptive interventions.Psychological Methods, 2012
- Screening Experiments for Developing Dynamic Treatment RegimesJournal of the American Statistical Association, 2009
- Analyzing sequentially randomized trials based on causal effect models for realistic individualized treatment rulesStatistics in Medicine, 2008
- Super LearnerStatistical Applications in Genetics and Molecular Biology, 2007
- Targeted Maximum Likelihood LearningThe International Journal of Biostatistics, 2006
- Doubly Robust Estimation in Missing Data and Causal Inference ModelsBiometrics, 2005
- An experimental design for the development of adaptive treatment strategiesStatistics in Medicine, 2004
- A Design for Testing Clinical Strategies: Biased Adaptive Within-Subject RandomizationJournal of the Royal Statistical Society Series A: Statistics in Society, 2000
- Stacked generalizationNeural Networks, 1992