Evidence map›Paper›PMID 39472775›Full record

ArticleBMC medical research methodology2024

Interpretation of statistical findings in randomised trials: a survey of statisticians using thematic analysis of open-ended questions.

Karla Hemming, Laura Kudrna, Sam Watson, Monica Taljaard, Sheila Greenfield, Beatriz Goulao, Richard Lilford

Abstract read
In one paragraph

Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. 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

7 authors.

Karla HemmingInstitute of Applied Health Research, University of Birmingham, Birmingham, UK. K.hemming@bham.ac.uk.
Laura KudrnaInstitute of Applied Health Research, University of Birmingham, Birmingham, UK.
Sam WatsonInstitute of Applied Health Research, University of Birmingham, Birmingham, UK.
Monica TaljaardClinical Epidemiology Program, Ottawa Hospital Research Institute, 1053 Carling Avenue, Ottawa, Ontario, Canada.
Sheila GreenfieldInstitute of Applied Health Research, University of Birmingham, Birmingham, UK.
Beatriz GoulaoHealth Services Research Unit, University of Aberdeen, Aberdeen, UK.
Richard LilfordInstitute of Applied Health Research, University of Birmingham, Birmingham, UK.

Funding

the National Institute for Health Research (NIHR)
6 · The paper itself

Abstract

backgroundDichotomisation of statistical significance, rather than interpretation of effect sizes supported by confidence intervals, is a long-standing problem.

methodsWe distributed an online survey to clinical trial statisticians across the UK, Australia and Canada asking about their experiences, perspectives and practices with respect to interpretation of statistical findings from randomised trials. We report a descriptive analysis of the closed-ended questions and a thematic analysis of the open-ended questions.

resultsWe obtained 101 responses across a broad range of career stages (24% professors; 51% senior lecturers; 22% junior statisticians) and areas of work (28% early phase trials; 44% drug trials; 38% health service trials). The majority (93%) believed that statistical findings should be interpreted by considering (minimal) clinical importance of treatment effects, but many (61%) said quantifying clinically important effect sizes was difficult, and fewer (54%) followed this approach in practice. Thematic analysis identified several barriers to forming a consensus on the statistical interpretation of the study findings, including: the dynamics within teams, lack of knowledge or difficulties in communicating that knowledge, as well as external pressures. External pressures included the pressure to publish definitive findings and statistical review which can sometimes be unhelpful but can at times be a saving grace. However, the concept of the minimally important difference was identified as a particularly poorly defined, even nebulous, construct which lies at the heart of much disagreement and confusion in the field.

conclusionThe majority of participating statisticians believed that it is important to interpret statistical findings based on the clinically important effect size, but report this is difficult to operationalise. Reaching a consensus on the interpretation of a study is a social process involving disparate members of the research team along with editors and reviewers, as well as patients who likely have a role in the elicitation of minimally important differences.

Indexed as

Data Interpretation, StatisticalMinimal Clinically Important DifferenceRandomized Controlled Trials as TopicAustraliaCanadaHumansSurveys and QuestionnairesUnited KingdomConfidence intervalsMisinterpretationP-valuesRCTs

Identifiers

PMID39472775
PMCPMC11520448

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.