2023
Causal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia
Blette B, Granholm A, Li F, Shankar-Hari M, Lange T, Munch M, Møller M, Perner A, Harhay M. Causal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia. Scientific Reports 2023, 13: 6570. PMID: 37085591, PMCID: PMC10120498, DOI: 10.1038/s41598-023-33425-3.Peer-Reviewed Original ResearchConceptsCritical COVID-19Better long-term outcomesCOVID-19Entire trial populationStandardized dosing protocolsMultiple patient characteristicsDose of dexamethasoneLong-term outcomesIL-6 inhibitorsDexamethasone doseSevere hypoxemiaMost patientsPatient characteristicsRespiratory supportDiabetes mellitusClinical outcomesDosing protocolTrial populationTreatment effect heterogeneityPatient featuresPatientsAdditional studiesDexamethasoneDoseMore evidence
2020
An evidence mapping and analysis of registered COVID-19 clinical trials in China
Lu L, Li F, Wen H, Ge S, Zeng J, Luo W, Wang L, Tang C, Xu N. An evidence mapping and analysis of registered COVID-19 clinical trials in China. BMC Medicine 2020, 18: 167. PMID: 32493331, PMCID: PMC7268588, DOI: 10.1186/s12916-020-01612-y.Peer-Reviewed Original ResearchConceptsCOVID-19 clinical trialsCurrent Control TrialsClinical Trials RegistryNational Research RegisterTraditional Chinese medicinePrimary outcomeTrials RegistryClinical trialsAntiviral drugsCOVID-19 nucleic acid testInternational Clinical Trials Registry PlatformAustralian Clinical Trials RegistryClinical Trials Registry PlatformChinese Clinical Trial RegistryEvidence mappingPilot studyPhase 4 trialTrials Registry PlatformNucleic acid testCOVID-19 trialsData monitoring committeePublic Health DivisionMedian sample sizeBiological agentsType of intervention