Evidence map›Paper›PMID 34191978›Full record

ArticleStatistics in biopharmaceutical research2020

Assessing via Simulation the Operating Characteristics of the WHO Scale for COVID-19 Endpoints.

Michael O'Kelly, Siying Li

Abstract read
In one paragraph

Article in Statistics in biopharmaceutical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  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

2 authors.

Michael O'KellyIQVIA, East Point Business Park, Dublin 3, Ireland.
Siying LiIQVIA, Durham, NC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many clinical trials of treatments for patients hospitalised for COVID-19 use an ordinal scale recommended by the World Heath Organisation. The scale represents intensity of medical intervention, with higher scores for interventions more burdensome for the patient, and highest score for death. There is uncertainty about use of this ordinal scale in testing hypotheses. With the objective of assessing the power and Type I error of potential endpoints and analyses based on the ordinal scale, trajectories of the score over 28 days were simulated for scenarios based closely on results of two trials recently published. The simulation used transition probabilities for the ordinal scale over time. No one endpoint was optimal across scenarios, but a ranked measure of trajectory fared moderately well in all scenarios. Type I error was controlled at close to the nominal level for all endpoints. Because not tied to a particular population with regard to baseline severity, the use of transition probabilities allows plausible assessment of endpoints in populations with configurations of baseline score for which data is not yet published, provided some data on the relevant transition probabilities are available. The results could support experts in the choice of endpoint based on the ordinal scale.

Indexed as

health trajectorymodelling and simulationordinal responsepowertransition probabilityType I error

Identifiers

PMID34191978
PMCPMC8011484

What OpenQuestion holds

Textmetadata
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.