Evidence map›Paper›PMID 29843614›Full record

ArticleBMC medical research methodology2018

Analysis of randomised trials with long-term follow-up.

Robert D Herbert, Jessica Kasza, Kari Bø

Abstract read
In one paragraph

Article in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 5 pooled it
–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

36 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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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

3 authors.

Robert D HerbertNeuroscience Research Australia (NeuRA), Sydney, Australia. r.herbert@neura.edu.au.ORCID 0000-0003-4188-9195
Jessica KaszaMonash University, Melbourne, Australia.
Kari BøNorwegian School of Sport Sciences, Oslo, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Randomised trials with long-term follow-up can provide estimates of the long-term effects of health interventions. However, analysis of long-term outcomes in randomised trials may be complicated by problems with the administration of treatment such as non-adherence, treatment switching and co-intervention, and problems obtaining outcome measurements arising from loss to follow-up and death of participants. Methods for dealing with these issues that involve conditioning on post-randomisation variables are unsatisfactory because they may involve the comparison of non-exchangeable groups and generate estimates that do not have a valid causal interpretation. We describe approaches to analysis that potentially provide estimates of causal effects when such issues arise. Brief descriptions are provided of the use of instrumental variable and propensity score methods in trials with imperfect adherence, marginal structural models and g-estimation in trials with treatment switching, mixed longitudinal models and multiple imputation in trials with loss to follow-up, and a sensitivity analysis that can be used when trial follow-up is truncated by death or other events. Clinical trialists might consider these methods both at the design and analysis stages of randomised trials with long-term follow-up.

Indexed as

Drug MonitoringFollow-Up StudiesHumansOutcome Assessment, Health CareRandomized Controlled Trials as TopicTime FactorsClinical trialsCo-interventionLong-term follow-upLoss to follow-upNon-complianceRandomized controlled trialsTreatment switching

Identifiers

PMID29843614
PMCPMC5975460

What OpenQuestion holds

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LicenceCC BY
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.