Evidence map›Paper›PMID 36468153›Full record

ArticleExceptional children2021

Multitiered Systems of Support, Adaptive Interventions, and SMART Designs.

Greg Roberts, Nathan Clemens, Christian T Doabler, Sharon Vaughn, Daniel Almirall, Inbal Nahum-Shani

Abstract read
In one paragraph

Article in Exceptional children, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Approaches to Statistical Efficiency When Comparing the Embedded Adaptive Interventions in a SMART.Journal of educational and behavioral statistics : a quarterly publication sponsored by the American Educational Research Association and the American Statistical Association · 2025
    Article
  3. Hybrid Experimental Designs for Intervention Development: What, Why, and How.Advances in methods and practices in psychological science
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Greg RobertsUniversity of Texas.
Nathan ClemensUniversity of Texas.
Christian T DoablerUniversity of Texas.
Sharon VaughnUniversity of Texas.
Daniel AlmirallUniversity of Michigan.
Inbal Nahum-ShaniUniversity of Michigan.

Funding

Texas Center for Learning DisabilitiesP50HD052117 · NICHD · UNIVERSITY OF HOUSTON · PI FLETCHER, JACK M · 2006 to 2023
$26.9M
Novel Methods for Intensive Longitudinal Data in SMART Studies of Drug Abuse and HIVR01DA039901 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALMIRALL, DANIEL, NAHUM-SHANI, INBAL BILLIE · 2015 to 2024
$5.4M
NICHD NIH HHS P50 HD052117NIDA NIH HHS R01 DA039901
6 · The paper itself

Abstract

This article introduces the special section on adaptive interventions and sequential multiple-assignment randomized trial (SMART) research designs. In addition to describing the two accompanying articles, we discuss features of adaptive interventions (AIs) and describe the use of SMART design to optimize AIs in the context of multitiered systems of support (MTSS) and integrated MTSS. AI is a treatment delivery model that explicitly specifies how information about individuals should be used to decide which treatment to provide in practice. Principles that apply to the design of AIs may help to more clearly operationalize MTSS-based programs, improve their implementation in school settings, and increase their efficacy when used according to evidence-based decision rules. A SMART is a research design for developing and optimizing MTSS-based programs. We provide a running example of a SMART design to optimize an MTSS-aligned AI that integrates academic and behavioral interventions.

Identifiers

PMID36468153
PMCPMC9718557

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.