Causal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia

[1]  V. Jha,et al.  Heterogeneous treatment effects of dexamethasone 12 mg versus 6 mg in patients with COVID‐19 and severe hypoxaemia—Post hoc exploratory analyses of the COVID STEROID 2 trial , 2022, Acta anaesthesiologica Scandinavica.

[2]  J. Williams,et al.  Coronavirus disease 2019 subphenotypes and differential treatment response to convalescent plasma in critically ill adults: secondary analyses of a randomized clinical trial , 2022, Intensive Care Medicine.

[3]  L. Poulsen,et al.  Changes over time in characteristics, resource use and outcomes among ICU patients with COVID‐19—A nationwide, observational study in Denmark , 2022, Acta anaesthesiologica Scandinavica.

[4]  J. Perry,et al.  Effect of 12 mg vs 6 mg of dexamethasone on the number of days alive without life support in adults with COVID-19 and severe hypoxemia: the COVID STEROID 2 randomized trial , 2022, Canadian Journal of Emergency Medicine.

[5]  A. Perner,et al.  Dexamethasone and Number of Days Alive Without Life Support in Adults With COVID-19 and Severe Hypoxemia-Reply. , 2022, JAMA.

[6]  M. Netea,et al.  A guide to immunotherapy for COVID-19 , 2022, Nature Medicine.

[7]  F. Kapadia,et al.  Dexamethasone 12 mg versus 6 mg for patients with COVID-19 and severe hypoxaemia: a pre-planned, secondary Bayesian analysis of the COVID STEROID 2 trial , 2021, Intensive Care Medicine.

[8]  F. Kapadia,et al.  Effect of 12 mg vs 6 mg of Dexamethasone on the Number of Days Alive Without Life Support in Adults With COVID-19 and Severe Hypoxemia: The COVID STEROID 2 Randomized Trial. , 2021, JAMA.

[9]  Peter J. Godolphin,et al.  Association Between Administration of IL-6 Antagonists and Mortality Among Patients Hospitalized for COVID-19: A Meta-analysis. , 2021, JAMA.

[10]  V. Jha,et al.  Low‐dose hydrocortisone in patients with COVID‐19 and severe hypoxia: The COVID STEROID randomised, placebo‐controlled trial , 2021, Acta anaesthesiologica Scandinavica.

[11]  R. Sparapani,et al.  Nonparametric machine learning for precision medicine with longitudinal clinical trials and Bayesian additive regression trees with mixed models , 2021, Statistics in medicine.

[12]  F. Kapadia,et al.  Higher vs lower doses of dexamethasone in patients with COVID‐19 and severe hypoxia (COVID STEROID 2) trial: Protocol and statistical analysis plan , 2021, Acta anaesthesiologica Scandinavica.

[13]  Robert E. McCulloch,et al.  Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package , 2021, J. Stat. Softw..

[14]  Helio T. Navarro,et al.  Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia , 2020, Nature.

[15]  Fan Li,et al.  Estimating heterogeneous survival treatment effect in observational data using machine learning , 2020, Statistics in medicine.

[16]  Jared S. Murray,et al.  Model Interpretation Through Lower-Dimensional Posterior Summarization , 2019, J. Comput. Graph. Stat..

[17]  W. Lim,et al.  Dexamethasone in Hospitalized Patients with Covid-19 , 2021 .

[18]  G. Guyatt,et al.  A living WHO guideline on drugs for covid-19 , 2020, BMJ.

[19]  Arthur S Slutsky,et al.  Association Between Administration of Systemic Corticosteroids and Mortality Among Critically Ill Patients With COVID-19: A Meta-analysis. , 2020, JAMA.

[20]  J. Mason,et al.  A dynamic COVID-19 immune signature includes associations with poor prognosis , 2020, Nature Medicine.

[21]  Eric Song,et al.  Longitudinal analyses reveal immunological misfiring in severe COVID-19 , 2020, Nature.

[22]  Sasikanth Manne,et al.  Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications , 2020, Science.

[23]  Edward H. Kennedy.  Towards optimal doubly robust estimation of heterogeneous causal effects , 2020, 2004.14497.

[24]  Jared S. Murray,et al.  Bayesian Additive Regression Trees: A Review and Look Forward , 2020, Annual Review of Statistics and Its Application.

[25]  Purushottam W. Laud,et al.  Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees , 2019, Statistical methods in medical research.

[26]  Jennifer Hill,et al.  Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition , 2017, Statistical Science.

[27]  R. McCulloch,et al.  The BART R package , 2019 .

[28]  Reynold A Panettieri,et al.  Non-genomic Effects of Glucocorticoids: An Updated View. , 2019, Trends in pharmacological sciences.

[29]  K Jung,et al.  Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases , 2018, Statistics in medicine.

[30]  Hemant Ishwaran,et al.  Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods , 2017, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[31]  James F Burke,et al.  Three simple rules to ensure reasonably credible subgroup analyses , 2015, BMJ : British Medical Journal.

[32]  B. Ripley.  Classification and Regression Trees , 2015 .

[33]  B. Ripley,et al.  Recursive Partitioning and Regression Trees , 2015 .

[34]  S. Pocock,et al.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. , 2007, Preventive medicine.

[35]  S. Pocock,et al.  Guidelines for Reporting Observational Studies , 2007 .

[36]  David Schoenfeld,et al.  Survival methods, including those using competing risk analysis, are not appropriate for intensive care unit outcome studies , 2005, Critical care.

[37]  Jill Hayden,et al.  Seven items were identified for inclusion when reporting a Bayesian analysis of a clinical study. , 2005, Journal of clinical epidemiology.

[38]  T. Peters,et al.  Subgroup analyses in randomised controlled trials: quantifying the risks of false-positives and false-negatives. , 2001, Health technology assessment.

[39]  John Geweke,et al.  Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments , 1991 .