Developing adaptive interventions for adolescent substance use treatment settings: Protocol of an observational, mixed-methods project

Sean Grant, Denis Agniel, Daniel Almirall, Q. Burkhart, Sarah B. Hunter, Daniel F. McCaffrey, Eric R. Pedersen, Rajeev Ramchand, Beth Ann Griffin

Research output: Contribution to journalArticle

Abstract

Background: Over 1.6 million adolescents in the United States meet criteria for substance use disorders (SUDs). While there are promising treatments for SUDs, adolescents respond to these treatments differentially in part based on the setting in which treatments are delivered. One way to address such individualized response to treatment is through the development of adaptive interventions (AIs): sequences of decision rules for altering treatment based on an individual's needs. This protocol describes a project with the overarching goal of beginning the development of AIs that provide recommendations for altering the setting of an adolescent's substance use treatment. This project has three discrete aims: (1) explore the views of various stakeholders (parents, providers, policymakers, and researchers) on deciding the setting of substance use treatment for an adolescent based on individualized need, (2) generate hypotheses concerning candidate AIs, and (3) compare the relative effectiveness among candidate AIs and non-adaptive interventions commonly used in everyday practice. Methods: This project uses a mixed-methods approach. First, we will conduct an iterative stakeholder engagement process, using RAND's ExpertLens online system, to assess the importance of considering specific individual needs and clinical outcomes when deciding the setting for an adolescent's substance use treatment. Second, we will use results from the stakeholder engagement process to analyze an observational longitudinal data set of 15,656 adolescents in substance use treatment, supported by the Substance Abuse and Mental Health Services Administration, using the Global Appraisal of Individual Needs questionnaire. We will utilize methods based on Q-learning regression to generate hypotheses about candidate AIs. Third, we will use robust statistical methods that aim to appropriately handle casemix adjustment on a large number of covariates (marginal structural modeling and inverse probability of treatment weights) to compare the relative effectiveness among candidate AIs and non-adaptive decision rules that are commonly used in everyday practice. Discussion: This project begins filling a major gap in clinical and research efforts for adolescents in substance use treatment. Findings could be used to inform the further development and revision of influential multi-dimensional assessment and treatment planning tools, or lay the foundation for subsequent experiments to further develop or test AIs for treatment planning.

Original languageEnglish (US)
Article number35
JournalAddiction Science and Clinical Practice
Volume12
Issue number1
DOIs
StatePublished - Dec 19 2017
Externally publishedYes

Fingerprint

Clinical Protocols
Therapeutics
Substance-Related Disorders
Health Services Misuse
Health Services Administration
Online Systems
Social Adjustment
Mental Health Services
Parents
Research Personnel
Learning

Keywords

  • Adaptive interventions
  • Adolescents
  • Alcohol
  • Clinical decision-making
  • Drugs
  • Substance use treatment

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Developing adaptive interventions for adolescent substance use treatment settings : Protocol of an observational, mixed-methods project. / Grant, Sean; Agniel, Denis; Almirall, Daniel; Burkhart, Q.; Hunter, Sarah B.; McCaffrey, Daniel F.; Pedersen, Eric R.; Ramchand, Rajeev; Griffin, Beth Ann.

In: Addiction Science and Clinical Practice, Vol. 12, No. 1, 35, 19.12.2017.

Research output: Contribution to journalArticle

Grant, Sean ; Agniel, Denis ; Almirall, Daniel ; Burkhart, Q. ; Hunter, Sarah B. ; McCaffrey, Daniel F. ; Pedersen, Eric R. ; Ramchand, Rajeev ; Griffin, Beth Ann. / Developing adaptive interventions for adolescent substance use treatment settings : Protocol of an observational, mixed-methods project. In: Addiction Science and Clinical Practice. 2017 ; Vol. 12, No. 1.
@article{c03fb7a196684daa87b087c91adc5e80,
title = "Developing adaptive interventions for adolescent substance use treatment settings: Protocol of an observational, mixed-methods project",
abstract = "Background: Over 1.6 million adolescents in the United States meet criteria for substance use disorders (SUDs). While there are promising treatments for SUDs, adolescents respond to these treatments differentially in part based on the setting in which treatments are delivered. One way to address such individualized response to treatment is through the development of adaptive interventions (AIs): sequences of decision rules for altering treatment based on an individual's needs. This protocol describes a project with the overarching goal of beginning the development of AIs that provide recommendations for altering the setting of an adolescent's substance use treatment. This project has three discrete aims: (1) explore the views of various stakeholders (parents, providers, policymakers, and researchers) on deciding the setting of substance use treatment for an adolescent based on individualized need, (2) generate hypotheses concerning candidate AIs, and (3) compare the relative effectiveness among candidate AIs and non-adaptive interventions commonly used in everyday practice. Methods: This project uses a mixed-methods approach. First, we will conduct an iterative stakeholder engagement process, using RAND's ExpertLens online system, to assess the importance of considering specific individual needs and clinical outcomes when deciding the setting for an adolescent's substance use treatment. Second, we will use results from the stakeholder engagement process to analyze an observational longitudinal data set of 15,656 adolescents in substance use treatment, supported by the Substance Abuse and Mental Health Services Administration, using the Global Appraisal of Individual Needs questionnaire. We will utilize methods based on Q-learning regression to generate hypotheses about candidate AIs. Third, we will use robust statistical methods that aim to appropriately handle casemix adjustment on a large number of covariates (marginal structural modeling and inverse probability of treatment weights) to compare the relative effectiveness among candidate AIs and non-adaptive decision rules that are commonly used in everyday practice. Discussion: This project begins filling a major gap in clinical and research efforts for adolescents in substance use treatment. Findings could be used to inform the further development and revision of influential multi-dimensional assessment and treatment planning tools, or lay the foundation for subsequent experiments to further develop or test AIs for treatment planning.",
keywords = "Adaptive interventions, Adolescents, Alcohol, Clinical decision-making, Drugs, Substance use treatment",
author = "Sean Grant and Denis Agniel and Daniel Almirall and Q. Burkhart and Hunter, {Sarah B.} and McCaffrey, {Daniel F.} and Pedersen, {Eric R.} and Rajeev Ramchand and Griffin, {Beth Ann}",
year = "2017",
month = "12",
day = "19",
doi = "10.1186/s13722-017-0099-4",
language = "English (US)",
volume = "12",
journal = "Addiction science & clinical practice",
issn = "1940-0632",
publisher = "BioMed Central",
number = "1",

}

TY - JOUR

T1 - Developing adaptive interventions for adolescent substance use treatment settings

T2 - Protocol of an observational, mixed-methods project

AU - Grant, Sean

AU - Agniel, Denis

AU - Almirall, Daniel

AU - Burkhart, Q.

AU - Hunter, Sarah B.

AU - McCaffrey, Daniel F.

AU - Pedersen, Eric R.

AU - Ramchand, Rajeev

AU - Griffin, Beth Ann

PY - 2017/12/19

Y1 - 2017/12/19

N2 - Background: Over 1.6 million adolescents in the United States meet criteria for substance use disorders (SUDs). While there are promising treatments for SUDs, adolescents respond to these treatments differentially in part based on the setting in which treatments are delivered. One way to address such individualized response to treatment is through the development of adaptive interventions (AIs): sequences of decision rules for altering treatment based on an individual's needs. This protocol describes a project with the overarching goal of beginning the development of AIs that provide recommendations for altering the setting of an adolescent's substance use treatment. This project has three discrete aims: (1) explore the views of various stakeholders (parents, providers, policymakers, and researchers) on deciding the setting of substance use treatment for an adolescent based on individualized need, (2) generate hypotheses concerning candidate AIs, and (3) compare the relative effectiveness among candidate AIs and non-adaptive interventions commonly used in everyday practice. Methods: This project uses a mixed-methods approach. First, we will conduct an iterative stakeholder engagement process, using RAND's ExpertLens online system, to assess the importance of considering specific individual needs and clinical outcomes when deciding the setting for an adolescent's substance use treatment. Second, we will use results from the stakeholder engagement process to analyze an observational longitudinal data set of 15,656 adolescents in substance use treatment, supported by the Substance Abuse and Mental Health Services Administration, using the Global Appraisal of Individual Needs questionnaire. We will utilize methods based on Q-learning regression to generate hypotheses about candidate AIs. Third, we will use robust statistical methods that aim to appropriately handle casemix adjustment on a large number of covariates (marginal structural modeling and inverse probability of treatment weights) to compare the relative effectiveness among candidate AIs and non-adaptive decision rules that are commonly used in everyday practice. Discussion: This project begins filling a major gap in clinical and research efforts for adolescents in substance use treatment. Findings could be used to inform the further development and revision of influential multi-dimensional assessment and treatment planning tools, or lay the foundation for subsequent experiments to further develop or test AIs for treatment planning.

AB - Background: Over 1.6 million adolescents in the United States meet criteria for substance use disorders (SUDs). While there are promising treatments for SUDs, adolescents respond to these treatments differentially in part based on the setting in which treatments are delivered. One way to address such individualized response to treatment is through the development of adaptive interventions (AIs): sequences of decision rules for altering treatment based on an individual's needs. This protocol describes a project with the overarching goal of beginning the development of AIs that provide recommendations for altering the setting of an adolescent's substance use treatment. This project has three discrete aims: (1) explore the views of various stakeholders (parents, providers, policymakers, and researchers) on deciding the setting of substance use treatment for an adolescent based on individualized need, (2) generate hypotheses concerning candidate AIs, and (3) compare the relative effectiveness among candidate AIs and non-adaptive interventions commonly used in everyday practice. Methods: This project uses a mixed-methods approach. First, we will conduct an iterative stakeholder engagement process, using RAND's ExpertLens online system, to assess the importance of considering specific individual needs and clinical outcomes when deciding the setting for an adolescent's substance use treatment. Second, we will use results from the stakeholder engagement process to analyze an observational longitudinal data set of 15,656 adolescents in substance use treatment, supported by the Substance Abuse and Mental Health Services Administration, using the Global Appraisal of Individual Needs questionnaire. We will utilize methods based on Q-learning regression to generate hypotheses about candidate AIs. Third, we will use robust statistical methods that aim to appropriately handle casemix adjustment on a large number of covariates (marginal structural modeling and inverse probability of treatment weights) to compare the relative effectiveness among candidate AIs and non-adaptive decision rules that are commonly used in everyday practice. Discussion: This project begins filling a major gap in clinical and research efforts for adolescents in substance use treatment. Findings could be used to inform the further development and revision of influential multi-dimensional assessment and treatment planning tools, or lay the foundation for subsequent experiments to further develop or test AIs for treatment planning.

KW - Adaptive interventions

KW - Adolescents

KW - Alcohol

KW - Clinical decision-making

KW - Drugs

KW - Substance use treatment

UR - http://www.scopus.com/inward/record.url?scp=85057227521&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85057227521&partnerID=8YFLogxK

U2 - 10.1186/s13722-017-0099-4

DO - 10.1186/s13722-017-0099-4

M3 - Article

C2 - 29254500

AN - SCOPUS:85057227521

VL - 12

JO - Addiction science & clinical practice

JF - Addiction science & clinical practice

SN - 1940-0632

IS - 1

M1 - 35

ER -