Evidence map›Paper›PMID 30944022›Full record

ArticleTrials2019

Estimating Site Performance (ESP): can trial managers predict recruitment success at trial sites? An exploratory study.

Hanne Bruhn, Shaun Treweek, Anne Duncan, Kirsty Shearer, Sarah Cameron, Karen Campbell, Karen Innes, Dawn McRae, Seonaidh C Cotton

Abstract read
In one paragraph

Article in Trials, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

9 authors.

Hanne BruhnHealth Services Research Unit, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen, UK. hanne.bruhn@abdn.ac.uk.ORCID http://orcid.org/0000-0002-9309-9809
Shaun TreweekHealth Services Research Unit, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen, UK.
Anne DuncanCHaRT, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen, UK.
Kirsty ShearerNHS Grampian, Aberdeen Royal Infirmary, Foresterhill, Aberdeen, UK.
Sarah CameronCHaRT, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen, UK.
Karen CampbellCHaRT, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen, UK.
Karen InnesCHaRT, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen, UK.
Dawn McRaeCHaRT, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen, UK.
Seonaidh C CottonCHaRT, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen, UK.

Funding

Chief Scientist Office HSRU1
6 · The paper itself

Abstract

backgroundMulticentre randomised trials provide some of the key evidence underpinning healthcare practice around the world. They are also hard work and generally expensive. Some of this work and expense are devoted to sites that fail to recruit as many participants as expected. Methods to identify sites that will recruit to target would be helpful.

methodsWe asked trial managers at the Centre for Healthcare Randomised Trials (CHaRT), University of Aberdeen to predict whether a site would recruit to target. Predictions were made after a site initiation visit and were collected on a form comprising a simple 'Yes/No' prediction and a reason for the prediction. We did not provide guidance as to what trial managers might want to think about when making predictions. After a minimum of eight months of recruitment at each site for which a prediction had been made, all trial mangers in CHaRT were invited to a group discussion where predictions were presented together with sites' actual recruitment performance over that period. Individual trial managers reflected on their predictions and there was a general discussion about predicting site recruitment. The prediction reasons from the forms and the content of the group discussion were used to identify features linked to correct predictions of recruitment failure.

resultsTen trial managers made predictions for 56 site visits recruiting to eight trials. Trial managers' sensitivity was 82% and their specificity was 32%, correctly identifying 65% of sites that would hit their recruitment target and 54% of those that did not. Eight 'red flags' for recruitment failure were identified: previous poor site performance; slow approvals process; strong staff/patient preferences; the site recruitment target; the trial protocol and its implementation at the site; lack of staff engagement; lack of research experience among site staff; and busy site staff. We used these red flags to develop a guided prediction form.

conclusionsTrial managers' unguided recruitment predictions were not bad but were not good enough for decision-making. We have developed a modified prediction form that includes eight flags to consider before making a prediction. We encourage anyone interested in contributing to its evaluation to contact us.

Indexed as

Decision Support TechniquesPatient SelectionAttitude of Health PersonnelHealth Knowledge, Attitudes, PracticeHumansMulticenter Studies as TopicRandomized Controlled Trials as TopicResearch PersonnelSample SizeClinical trialsRecruitmentRecruitment performanceRecruitment sitesTrial managers

Identifiers

PMID30944022
PMCPMC6448211

What OpenQuestion holds

Textmetadata
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.