Evidence map›Paper›PMID 29724115›Full record

Trial reportEvaluation & the health professions2018

An Algorithm for Creating Virtual Controls Using Integrated and Harmonized Longitudinal Data.

William B Hansen, Shyh-Huei Chen, Santiago Saldana, Edward H Ip

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Evaluation & the health professions, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Heterogeneity in Association Between Cognitive Function and Gait Speed Among Older Adults: An Integrative Data Analysis Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2021
    Article
  4. Review
  5. 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

4 authors.

William B Hansen1 Prevention Strategies, Greensboro, NC, USA.ORCID 0000-0002-0970-8000
Shyh-Huei Chen2 Department of Biostatistical Sciences, Division of Public Health Sciences, School of Medicine, Wake Forest University, Winston-Salem, NC, USA.
Santiago Saldana2 Department of Biostatistical Sciences, Division of Public Health Sciences, School of Medicine, Wake Forest University, Winston-Salem, NC, USA.
Edward H Ip2 Department of Biostatistical Sciences, Division of Public Health Sciences, School of Medicine, Wake Forest University, Winston-Salem, NC, USA.

Funding

Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
An Integrative Approach to Control Group Creation for Prevention ResearchR44AA024657 · NIAAA · PREVENTION STRATEGIES, LLC · PI HANSEN, WILLIAM B. · 2018 to 2019
$1.0M
An Integrative Approach to Control Group Creation for Prevention ResearchR43AA024657 · NIAAA · PREVENTION STRATEGIES, LLC · PI HANSEN, WILLIAM B. · 2016 to 2016
$270k
NCATS NIH HHS UL1 TR001420NIAAA NIH HHS R43 AA024657NIAAA NIH HHS R44 AA024657
6 · The paper itself

Abstract

We introduce a strategy for creating virtual control groups-cases generated through computer algorithms that, when aggregated, may serve as experimental comparators where live controls are difficult to recruit, such as when programs are widely disseminated and randomization is not feasible. We integrated and harmonized data from eight archived longitudinal adolescent-focused data sets spanning the decades from 1980 to 2010. Collectively, these studies examined numerous psychosocial variables and assessed past 30-day alcohol, cigarette, and marijuana use. Additional treatment and control group data from two archived randomized control trials were used to test the virtual control algorithm. Both randomized controlled trials (RCTs) assessed intentions, normative beliefs, and values as well as past 30-day alcohol, cigarette, and marijuana use. We developed an algorithm that used percentile scores from the integrated data set to create age- and gender-specific latent psychosocial scores. The algorithm matched treatment case observed psychosocial scores at pretest to create a virtual control case that figuratively "matured" based on age-related changes, holding the virtual case's percentile constant. Virtual controls matched treatment case occurrence, eliminating differential attrition as a threat to validity. Virtual case substance use was estimated from the virtual case's latent psychosocial score using logistic regression coefficients derived from analyzing the treatment group. Averaging across virtual cases created group estimates of prevalence. Two criteria were established to evaluate the adequacy of virtual control cases: (1) virtual control group pretest drug prevalence rates should match those of the treatment group and (2) virtual control group patterns of drug prevalence over time should match live controls. The algorithm successfully matched pretest prevalence for both RCTs. Increases in prevalence were observed, although there were discrepancies between live and virtual control outcomes. This study provides an initial framework for creating virtual controls using a step-by-step procedure that can now be revised and validated using other prevention trial data.

Indexed as

AlgorithmsComputer SimulationResearch DesignAdolescentAlcoholismChildData Interpretation, StatisticalFemaleHumansLongitudinal StudiesMaleMarijuana AbuseSubstance-Related DisordersTobacco Use Disorderadolescentsalcoholcigarettescontrol groupsharmonizationintegrated data analysismarijuanamissing data imputationpsychosocial mediators

Identifiers

PMID29724115
PMCPMC7050185

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.