Evidence map›Paper›PMID 24440050›Full record

ArticleDrug and alcohol dependence2014

Estimating the causal effects of cumulative treatment episodes for adolescents using marginal structural models and inverse probability of treatment weighting.

Beth Ann Griffin, Rajeev Ramchand, Daniel Almirall, Mary E Slaughter, Lane F Burgette, Daniel F McCaffery

Abstract read
In one paragraph

Article in Drug and alcohol dependence, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
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  3. Assessing Covariate Balance with Small Sample Sizes.medRxiv : the preprint server for health sciences · 2025
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  19. Daily stressors as antecedents, correlates, and consequences of alcohol and drug use and cravings in community-based offenders.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2017
    Article
  20. 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.

Beth Ann GriffinRAND Corporation, United States. Electronic address: bethg@rand.org.
Rajeev RamchandRAND Corporation, United States.
Daniel AlmirallInstitute for Social Research, United States.
Mary E SlaughterRAND Corporation, United States.
Lane F BurgetteRAND Corporation, United States.
Daniel F McCafferyRAND Corporation, United States.

Funding

THE REFINEMENT &APPLICATION OF MEDICAL DECISION MAKING IN PREVENTION SCIENCESP50DA010075 · NIDA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI LANZA, STEPHANIE T · 1996 to 2014
$24.5M
The Causal Effect of Community-Based Treatment for YouthsR01DA015697 · NIDA · RAND CORPORATION · PI GRIFFIN, BETH ANN · 2003 to 2018
$2.8M
CSAT SAMHSA HHS TI-11317CSAT SAMHSA HHS TI-11321CSAT SAMHSA HHS TI-11323CSAT SAMHSA HHS TI-11324CSAT SAMHSA HHS TI-11422CSAT SAMHSA HHS TI-11423CSAT SAMHSA HHS TI-11424CSAT SAMHSA HHS TI-11432CSAT SAMHSA HHS TI-11892CSAT SAMHSA HHS TI-11894CSAT SAMHSA HHS TI-13344CSAT SAMHSA HHS TI-13354CSAT SAMHSA HHS TI-13356CSAT SAMHSA HHS TI-16400NIDA NIH HHS 1R01DA015697NIDA NIH HHS P50 DA010075NIDA NIH HHS R01 DA015697PHS HHS 270-00-6500PHS HHS 270-07-0191PHS HHS 270-2003-00006PHS HHS 270-2007-00004CPHS HHS 270-97-7011PHS HHS 270-98-7047PHS HHS 277-00-6500
6 · The paper itself

Abstract

backgroundSubstance use treatment is rarely a one-time event for individuals with substance use disorders. Sustained reductions in substance use and its related symptoms may result from multiple treatment episodes.

methodsWe use a marginal structural model with inverse-probability-of-treatment weighting to estimate the causal effects of cumulative treatment experiences over a period of 9 months on drug use at the end of 1-year among 2870 adolescents receiving care in community-based treatment settings. During the 9 months, adolescents move in and out of outpatient and residential treatment with periods where they only receive biological drug screening (BDS) or no treatment at all. The use of inverse-probability-of-treatment weighting reduces confounding bias due to observed baseline and time-varying measures over the course of follow-up; weights were estimated using generalized boosted models.

resultsEach additional period of treatment (representing at least one day, 1 session, or 1 BDS during the 90 day period between follow-up visits) yielded reductions in average substance use frequency at 1-year relative to no treatment during the 90-day period. For residential treatment it was a 16% decrease (95% CI=-27%, -7%), for outpatient treatment it was a 9% decrease (95% CI=-18%, -0%), and for BDS (with no additional outpatient or residential treatment) it was an 11% decrease (95% CI=-20%, -3%).

conclusionsUsing robust statistical methods, we find promising (albeit preliminary) evidence that additional periods of outpatient and residential treatment, as well as biological drug screening, lead to reductions in substance use outcomes at one year.

Indexed as

AdolescentAlgorithmsAmbulatory CareData Interpretation, StatisticalFemaleHumansMaleModels, StatisticalResidential TreatmentSocioeconomic FactorsSubstance Abuse DetectionSubstance Abuse Treatment CentersSubstance-Related DisordersTreatment OutcomeUnited StatesAdolescent treatmentCumulative treatment effectsGeneralized boosted modelInverse-probability-of-treatment weightsMarginal structural modelTime-varying confounding

Identifiers

PMID24440050
PMCPMC3969884

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Registered trials

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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.