Evidence map›Paper›PMID 32466758›Full record

ReviewBMC medicine2020

Evidence of unexplained discrepancies between planned and conducted statistical analyses: a review of randomised trials.

Suzie Cro, Gordon Forbes, Nicholas A Johnson, Brennan C Kahan

Abstract readReview
In one paragraph

Review in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Observational
  6. How can we make sound replication decisions?Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  7. Review
  8. A template for the authoring of statistical analysis plans.Contemporary clinical trials communications · 2023
    Article
  9. Eliminating Ambiguous Treatment Effects Using Estimands.American journal of epidemiology · 2023
    Article
  10. Article
  11. Article
  12. 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.

Suzie CroImperial Clinical Trials Unit, School of Public Health, Imperial College London, 1st Floor, Stadium House, London, W12 7RH, UK. s.cro@imperial.ac.uk.
Gordon ForbesDepartment of Biostatistics and Health Informatics, Kings College London, London, UK.
Nicholas A JohnsonImperial Clinical Trials Unit, School of Public Health, Imperial College London, 1st Floor, Stadium House, London, W12 7RH, UK.
Brennan C KahanMRC Clinical Trials Unit at UCL, London, UK.

Funding

Medical Research Council MC_UU_12023/21
6 · The paper itself

Abstract

backgroundChoosing or altering the planned statistical analysis approach after examination of trial data (often referred to as 'p-hacking') can bias the results of randomised trials. However, the extent of this issue in practice is currently unclear. We conducted a review of published randomised trials to evaluate how often a pre-specified analysis approach is publicly available, and how often the planned analysis is changed.

methodsA review of randomised trials published between January and April 2018 in six leading general medical journals. For each trial, we established whether a pre-specified analysis approach was publicly available in a protocol or statistical analysis plan and compared this to the trial publication.

resultsOverall, 89 of 101 eligible trials (88%) had a publicly available pre-specified analysis approach. Only 22/89 trials (25%) had no unexplained discrepancies between the pre-specified and conducted analysis. Fifty-four trials (61%) had one or more unexplained discrepancies, and in 13 trials (15%), it was impossible to ascertain whether any unexplained discrepancies occurred due to incomplete reporting of the statistical methods. Unexplained discrepancies were most common for the analysis model (n = 31, 35%) and analysis population (n = 28, 31%), followed by the use of covariates (n = 23, 26%) and the approach for handling missing data (n = 16, 18%). Many protocols or statistical analysis plans were dated after the trial had begun, so earlier discrepancies may have been missed.

conclusionsUnexplained discrepancies in the statistical methods of randomised trials are common. Increased transparency is required for proper evaluation of results.

Indexed as

Data Interpretation, StatisticalHumansRandomized Controlled Trials as TopicP-hackingRandomised controlled trialsStatistical analysisStatistical analysis planTransparency

Identifiers

PMID32466758
PMCPMC7257229

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.