Literature DB >> 31571526

Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome.

Nicholas J Seewald1, Kelley M Kidwell2, Inbal Nahum-Shani3, Tianshuang Wu4, James R McKay5, Daniel Almirall1,3.   

Abstract

Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen is a sequence of prespecified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that is tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial is a research tool which allows for the construction of effective dynamic treatment regimens. We derive easy-to-use formulae for computing the total sample size for three common two-stage sequential multiple-assignment randomized trial designs in which the primary aim is to compare mean end-of-study outcomes for two embedded dynamic treatment regimens which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a sequential multiple-assignment randomized trial can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a sequential multiple-assignment randomized trial. The sequential multiple-assignment randomized trial design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for dynamic treatment regimen effect analyses using a longitudinal outcome from a sequential multiple-assignment randomized trial, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a sequential multiple-assignment randomized trial aimed at developing a dynamic treatment regimen for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.

Entities:  

Keywords:  Sample size; dynamic treatment regimens; longitudinal data; sequential multiple-assignment randomized trials

Mesh:

Year:  2019        PMID: 31571526      PMCID: PMC7108977          DOI: 10.1177/0962280219877520

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  31 in total

1.  Dynamic treatment regimes: practical design considerations.

Authors:  Philip W Lavori; Ree Dawson
Journal:  Clin Trials       Date:  2004-02       Impact factor: 2.486

2.  Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research.

Authors:  Daniel Almirall; Inbal Nahum-Shani; Nancy E Sherwood; Susan A Murphy
Journal:  Transl Behav Med       Date:  2014-09       Impact factor: 3.046

3.  Design and Analysis Considerations for Comparing Dynamic Treatment Regimens with Binary Outcomes from Sequential Multiple Assignment Randomized Trials.

Authors:  Kelley M Kidwell; Nicholas J Seewald; Qui Tran; Connie Kasari; Daniel Almirall
Journal:  J Appl Stat       Date:  2017-10-12       Impact factor: 1.404

4.  Introduction to sample size determination and power analysis for clinical trials.

Authors:  J M Lachin
Journal:  Control Clin Trials       Date:  1981-06

5.  Comparison of adaptive treatment strategies based on longitudinal outcomes in sequential multiple assignment randomized trials.

Authors:  Zhiguo Li
Journal:  Stat Med       Date:  2016-09-19       Impact factor: 2.373

6.  Introduction to dynamic treatment strategies and sequential multiple assignment randomization.

Authors:  Philip W Lavori; Ree Dawson
Journal:  Clin Trials       Date:  2014-05-01       Impact factor: 2.486

7.  Program for lung cancer screening and tobacco cessation: Study protocol of a sequential, multiple assignment, randomized trial.

Authors:  Steven S Fu; Alexander J Rothman; David M Vock; Bruce Lindgren; Daniel Almirall; Abbie Begnaud; Anne Melzer; Kelsey Schertz; Susan Glaeser; Patrick Hammett; Anne M Joseph
Journal:  Contemp Clin Trials       Date:  2017-07-04       Impact factor: 2.226

8.  Sequential multiple-assignment randomized trial design of neurobehavioral treatment for patients with metastatic malignant melanoma undergoing high-dose interferon-alpha therapy.

Authors:  S Freda Auyeung; Qi Long; Erica Bruce Royster; Smitha Murthy; Marcia D McNutt; David Lawson; Andrew Miller; Amita Manatunga; Dominique L Musselman
Journal:  Clin Trials       Date:  2009-09-28       Impact factor: 2.486

9.  Effect of patient choice in an adaptive sequential randomization trial of treatment for alcohol and cocaine dependence.

Authors:  James R McKay; Michelle L Drapkin; Deborah H A Van Horn; Kevin G Lynch; David W Oslin; Dominick DePhilippis; Megan Ivey; John S Cacciola
Journal:  J Consult Clin Psychol       Date:  2015-07-27

Review 10.  Hypertrophic Burn Scar Research: From Quantitative Assessment to Designing Clinical Sequential Multiple Assignment Randomized Trials.

Authors:  Paul Diegidio; Steven Hermiz; Jonathan Hibbard; Michael Kosorok; Charles Scott Hultman
Journal:  Clin Plast Surg       Date:  2017-08-01       Impact factor: 2.017

View more
  1 in total

1.  Optimal allocation to treatments in a sequential multiple assignment randomized trial.

Authors:  Andrea Morciano; Mirjam Moerbeek
Journal:  Stat Methods Med Res       Date:  2021-09-23       Impact factor: 3.021

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.