Trial reportBMC medical research methodology2025
Visualizing a marker's degrees of necessity and of sufficiency in the predictiveness curve.
Trial report in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04351724 (A Multicenter, Randomized, Active Controlled, Open Label, Platform Trial on the Efficacy and Safety of Experimental Therapeutics for Patients With COVID-19), which is not on this map. Not yet cited in PubMed.
What it found
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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.
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.
A Multicenter, Randomized, Active Controlled, Open Label, Platform Trial on the Efficacy and Safety of Experimental Therapeutics for Patients With COVID-19 (Caused by Infection With Severe Acute Respiratory Syndrome Coronavirus-2)
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe degrees to which a factor is necessary or sufficient for an event have been proposed as generalizations of attributable risk based on simple functions of unconditional and conditional event probabilities. Predictiveness curves show the risk for an event, as derived by a model with one or more predictors, depending on risk percentiles that represent the predictors' distribution in the underlying population.
methodsConnections between the degrees of necessity and of sufficiency and explained variation on the one hand and the predictiveness curve on the other hand are mathematically proved and exemplified using data of in-hospital death of Covid- 19 patients.
resultsWe show that the degrees of necessity and of sufficiency can be represented as proportions of areas easily identifiable in the plot of the predictiveness curve. In addition, we show that the proportion of explained variation, a common measure of predictiveness and relative importance of prognostic factors, is also closely connected to these areas.
conclusionOur investigations demonstrate that the predictiveness curve extended by these new interpretations of areas provides a comprehensive evaluation of markers or sets of markers for prediction.
trial registrationAustrian Coronavirus Adaptive Clinical Trial (ACOVACT); ClinicalTrials.gov, identifier NCT04351724.
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