Evidence map›Paper›PMID 33241607›Full record

Trial reportStatistics in medicine2021

Generalizing randomized trial findings to a target population using complex survey population data.

Benjamin Ackerman, Catherine R Lesko, Juned Siddique, Ryoko Susukida, Elizabeth A Stuart

Open access · greenAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 36% of its field
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 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
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  9. Review
  10. Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review.Statistical science : a review journal of the Institute of Mathematical Statistics · 2024
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Generalizing trial evidence to target populations in non-nested designs: Applications to AIDS clinical trials.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022
    Article
  16. Universal adaptability: Target-independent inference that competes with propensity scoring.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  17. Article
  18. Article
  19. Article
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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

5 authors at 2 institutions in 1 country.

Benjamin AckermanDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.ORCID 0000-0003-2522-6623
Catherine R LeskoDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Juned SiddiqueDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Ryoko SusukidaDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Elizabeth A StuartDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Johns Hopkins University · USNorthwestern University · US

Funding

Using an innovative quality improvement process to increase delivery of evidenced-based CVD risk factor care in community mental health organizationsP50MH115842 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Gail L. Daumit · 2018 to 2026
$16.2M
Statistical methods to correct for measurement error in self-reported dietary data from lifestyle intervention trialsR01HL127491 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI SIDDIQUE, JUNED · 2015 to 2017
$1.1M
Unhealthy alcohol use, other drug use, and mental health disorders: comorbidity burden in people with HIV, gaps in treatment, and impact on the HIV care continuumK01AA028193 · NIAAA · JOHNS HOPKINS UNIVERSITY · PI LESKO, CATHERINE · 2019 to 2023
$900k
Statistical methods for estimating relative intensity physical activity and its association with cardiometabolic diseaseR01HL131606 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI SIDDIQUE, JUNED · 2017 to 2018
$288k
NHLBI NIH HHS R01 HL127491NHLBI NIH HHS R01 HL131606NIAAA NIH HHS K01 AA028193NIMH NIH HHS P50 MH115842
6 · The paper itself

Abstract

Randomized trials are considered the gold standard for estimating causal effects. Trial findings are often used to inform policy and programming efforts, yet their results may not generalize well to a relevant target population due to potential differences in effect moderators between the trial and population. Statistical methods have been developed to improve generalizability by combining trials and population data, and weighting the trial to resemble the population on baseline covariates. Large-scale surveys in fields such as health and education with complex survey designs are a logical source for population data; however, there is currently no best practice for incorporating survey weights when generalizing trial findings to a complex survey. We propose and investigate ways to incorporate survey weights in this context. We examine the performance of our proposed estimator through simulations in comparison to estimators that ignore the complex survey design. We then apply the methods to generalize findings from two trials-a lifestyle intervention for blood pressure reduction and a web-based intervention to treat substance use disorders-to their respective target populations using population data from complex surveys. The work highlights the importance in properly accounting for the complex survey design when generalizing trial findings to a population represented by a complex survey sample.

Indexed as

Health Services Needs and DemandSubstance-Related DisordersCausalityHumansSurveys and Questionnairescausal inferencecomplex survey datageneralizabilitypropensity scorestransportability

Identifiers

PMID33241607
PMCPMC8034867
OpenAlexW3011261748

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.