A Data Analysis Method for Using Longitudinal Binary Outcome Data from a SMART to Compare Adaptive Interventions

Dziak, John J.; Yap, Jamie; Almirall, Daniel; Lynch, Kevin G.; Nahum‐Shani, Inbal

doi:10.1080/00273171.2018.1558042

Cited by 9 publications

(9 citation statements)

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“… Δ Q) focuses on the EDTR. In the context of sample size estimation for SMARTs, some authors 13 have also required elicitation of parameters at the ETS level. However, to our knowledge, none have made the explicit connection between eliciting parameters at the ETS level to interpretability and meaningfulness of parameters while accommodating realistic features of SMARTs, as in this manuscript.…”

Section: Discussionmentioning

confidence: 99%

“…The comparison of EDTRs based on differences in end-of-study means is a common goal of SMARTs. However, EDTRs may also be compared based on differences in other quantities, 4,13 for example, AUC. AUC is defined as the total area under T ( a 1 , a 2 N R ) ( X ) between t 1 and t T and can be approximated using the trapezoidal rule as In contrast to end-of-study means the AUC accounts for how mean trajectories of EDTRs evolve over time (see Web Figure 13).…”

Section: Hypothesis Tests For Comparing Edtrs In a Smartmentioning

confidence: 99%

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Sample size estimation for comparing dynamic treatment regimens in a SMART: A Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes

Yap

Dziak

Maiti

et al. 2023

Stat Methods Med Res

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Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by delivering the type of treatment needed, when needed, while minimizing unnecessary treatment. Practically, a DTR is a sequence of decision rules that specify, for each of several points in time, how available information about the individual's status and progress should be used in practice to decide which treatment (e.g. type or intensity) to deliver. The sequential multiple assignment randomized trial (SMART) is an experimental design widely used to empirically inform the development of DTRs. Sample size planning resources for SMARTs have been developed for continuous, binary, and survival outcomes. However, an important gap exists in sample size estimation methodology for SMARTs with longitudinal count outcomes. Furthermore, in many health domains, count data are overdispersed —having variance greater than their mean. We propose a Monte Carlo-based approach to sample size estimation applicable to many types of longitudinal outcomes and provide a case study with longitudinal overdispersed count outcomes. A SMART for engaging alcohol and cocaine-dependent patients in treatment is used as motivation.

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Section: Discussionmentioning

confidence: 99%

Section: Hypothesis Tests For Comparing Edtrs In a Smartmentioning

confidence: 99%

Sample size estimation for comparing dynamic treatment regimens in a SMART: A Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes

Yap

Dziak

Maiti

et al. 2023

Stat Methods Med Res

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“…Readers may refer to recent papers that focus on analytic considerations for SMART studies for more detailed information. For analysis of repeated continuous outcome measures, please refer to Lu et al (), and for analyses of binary outcome measures, please refer to Dziak et al ().…”

Section: Methodsmentioning

confidence: 99%

Sequential multiple assignment randomization trial designs for nursing research

Doorenbos

Haozous

Jang

et al. 2019

Research in Nursing & Health

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With mounting pressure to move toward precision health care and implementation science, nurse researchers are faced with the challenge of producing evidence that their interventions are applicable in real‐world clinical settings. Pragmatic clinical trials are critically important to generate evidence that is relevant to clinical practice. Pragmatic designs closely replicate true‐to‐life settings thereby expediting research translation and improving health outcomes. The Sequential Multiple Assignment Randomized Trial (SMART) is a valuable a pragmatic trial design that is receiving greater attention in nursing. SMART designs can be used to provide relevant clinical evidence by comparative evaluation of two or more alternative interventions. The objectives of this article are to provide: (a) A description of the main considerations and key components of SMART designs, and (b) a summary of three different nursing studies using SMART designs. Information provided by pragmatic nursing trials using SMART designs, which more closely mirror clinical practice, will facilitate evidence‐based clinical practice.

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“…In recent years, various types of SMART designs and analytic methods have been developed to enable investigators to address specific scientific questions about the selection and adaptation of components. Examples include questions about the differences between ADIs that are embedded in a SMART, and the selection of the best embedded ADI [e.g., ( 22 , 32 , 33 )], how well components that are offered at different time points work together [e.g., ( 25 , 34 )], and what type of information should be used to decide which component to offer [e.g., ( 35 – 38 )]. These methods enable investigators to leverage the SMART to inform the development of a wide variety of ADIs.…”

Section: The Mcmtc Pragmatic Frameworkmentioning

confidence: 99%

MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions

Nahum‐Shani

Dziak

Wetter

2022

Front. Digit. Health

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Advances in digital technologies have created unprecedented opportunities to deliver effective and scalable behavior change interventions. Many digital interventions include multiple components, namely several aspects of the intervention that can be differentiated for systematic investigation. Various types of experimental approaches have been developed in recent years to enable researchers to obtain the empirical evidence necessary for the development of effective multiple-component interventions. These include factorial designs, Sequential Multiple Assignment Randomized Trials (SMARTs), and Micro-Randomized Trials (MRTs). An important challenge facing researchers concerns selecting the right type of design to match their scientific questions. Here, we propose MCMTC – a pragmatic framework that can be used to guide investigators interested in developing digital interventions in deciding which experimental approach to select. This framework includes five questions that investigators are encouraged to answer in the process of selecting the most suitable design: (1) Multiple-component intervention: Is the goal to develop an intervention that includes multiple components; (2) Component selection: Are there open scientific questions about the selection of specific components for inclusion in the intervention; (3) More than a single component: Are there open scientific questions about the inclusion of more than a single component in the intervention; (4) Timing: Are there open scientific questions about the timing of component delivery, that is when to deliver specific components; and (5) Change: Are the components in question designed to address conditions that change relatively slowly (e.g., over months or weeks) or rapidly (e.g., every day, hours, minutes). Throughout we use examples of tobacco cessation digital interventions to illustrate the process of selecting a design by answering these questions. For simplicity we focus exclusively on four experimental approaches—standard two- or multi-arm randomized trials, classic factorial designs, SMARTs, and MRTs—acknowledging that the array of possible experimental approaches for developing digital interventions is not limited to these designs.

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A Data Analysis Method for Using Longitudinal Binary Outcome Data from a SMART to Compare Adaptive Interventions

Cited by 9 publications

References 53 publications

Sample size estimation for comparing dynamic treatment regimens in a SMART: A Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes

Sample size estimation for comparing dynamic treatment regimens in a SMART: A Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes

Sequential multiple assignment randomization trial designs for nursing research

MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions

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