Evidence map›Paper›PMID 30297036›Full record

ArticleJournal of clinical epidemiology2018

Design analysis indicates Potential overestimation of treatment effects in randomized controlled trials supporting Food and Drug Administration cancer drug approvals.

Emily M Lord, Isabelle R Weir, Ludovic Trinquart

Abstract read
In one paragraph

Article in Journal of clinical epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Decreased risk of non-infectious anterior uveitis with statin therapy in a large healthcare claims database.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2021
    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

3 authors.

Emily M LordBoston University School of Public Health, Department of Biostatistics, 801 Massachusetts Avenue, Boston, MA 02118, USA.
Isabelle R WeirBoston University School of Public Health, Department of Biostatistics, 801 Massachusetts Avenue, Boston, MA 02118, USA.
Ludovic TrinquartBoston University School of Public Health, Department of Biostatistics, 801 Massachusetts Avenue, Boston, MA 02118, USA. Electronic address: ludovic@bu.edu.

Funding

Interdisciplinary Training for BiostatisticiansT32GM074905 · NIGMS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI WHITE, LAURA FORSBERG · 2005 to 2019
$3.0M
NIGMS NIH HHS T32 GM074905
6 · The paper itself

Abstract

objectiveStatistical significance drives interpretation of randomized controlled trials (RCTs). We examined the type S error risk-claiming a new drug is falsely beneficial-and exaggeration ratio-how estimated effects differ from true effects-to re-emphasize direction and magnitude of treatment effects. STUDY DESIGN AND

settingWe systematically reviewed RCTs supporting Food and Drug Administration (FDA) approval of cancer drugs between 2007 and 2016. We extracted data for overall survival (OS), progression-free survival (PFS), and response outcomes from FDA reviews. We estimated type S error risks and exaggeration ratios by considering replicated RCTs of equal size and a range of true effects.

resultsWe analyzed 42 RCTs for 39 approved drugs. Across 38 RCTs reporting OS, the median type S error risk was 0.00% (Q1-Q3, 0.00-0.01%) and 3.56% (0.40-6.74%), for true hazard ratios of 0.7 and 0.9, respectively, indicating confidence in effect direction. The corresponding exaggeration ratios were 1.09 (1.01-1.11) and 1.30 (1.13-1.42), indicating median overestimations of 9% and 30%. Similar results held for PFS and response outcomes.

conclusionsThe type S error risk and exaggeration ratio provide additional insights into the replicability of RCTs. Our analyses also quantify the winner's curse, in which pivotal RCTs tend toward overoptimism.

Indexed as

Outcome Assessment, Health CareRandomized Controlled Trials as TopicAntineoplastic AgentsBiasData Interpretation, StatisticalDrug ApprovalHumansNeoplasmsProportional Hazards ModelsReproducibility of ResultsUnited StatesUnited States Food and Drug AdministrationAntineoplastic AgentsBiasDisease-free survivalDrug approvalRandomized controlled trialsReproducibility of resultsStatistical data interpretation

Identifiers

PMID30297036
PMCPMC8978539

What OpenQuestion holds

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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.