Frequentist and Bayesian analysis methods for case series data and application to early outpatient COVID-19 treatment case series of high risk patients
Gkioulekas et al.
, Frequentist and Bayesian analysis methods for case series data and application to early outpatient COVID-19..
, Authorea, Inc., doi:10.22541/au.164745391.17821933/v2 (Preprint)
Hybrid statistical framework for evaluating treatment protocols. COVID-19 treatment protocols often use risk stratification, multiple treatments, and customization based on the disease stage and the patient. Authors find strong evidence for the efficacy of the early outpatient treatment protocols considered.
Gkioulekas et al., 16 May 2022, retrospective, preprint, 3 authors.
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Abstract: submitted to Reviews in Cardiovascular Medicine
Frequentist and Bayesian analysis methods for case series data and application to early outpatient
COVID-19 treatment case series of high risk patients
Eleftherios Gkioulekas, Ph.D.
Professor, School of Mathematical and Statistical Sciences,
University of Texas Rio Grande Valley, Edinburg, TX, United States∗
Peter A McCullough, MD, MPH
Chief Medical Advisor, Truth for Health Foundation, Tucson AZ, United States†
Vladimir Zelenko, MD
Affiliate Physician, Columbia University Irving Medical Center, New York City, NY, United States‡
When confronted with a public health emergency, significant innovative treatment protocols can sometimes
be discovered by medical doctors at the front lines based on repurposed medications. We propose a very simple hybrid statistical framework for analyzing the case series of patients treated with such new protocols, that
enables a comparison with our prior knowledge of expected outcomes, in the absence of treatment. The goal
of the proposed methodology is not to provide a precise measurement of treatment efficacy, but to establish
the existence of treatment efficacy, in order to facilitate the binary decision of whether the treatment protocol
should be adopted on an emergency basis. The methodology consists of a frequentist component that compares
a treatment group against the probability of an adverse outcome in the absence of treatment, and calculates an
efficacy threshold that has to be exceeded by this probability, in order to control the corresponding p-value,
and reject the null hypothesis. The efficacy threshold is further adjusted with a Bayesian technique, in order to
also control the false positive rate. A selection bias threshold is then calculated from the efficacy threshold to
control for random selection bias. Exceeding the efficacy threshold establishes efficacy by the preponderance
of evidence, and exceeding the more demanding selection bias threshold establishes efficacy by the clear and
convincing evidentiary standard. The combined techniques are applied to case series of high-risk COVID-19
outpatients, that were treated using the early Zelenko protocol and the more enhanced McCullough protocol.
The resulting efficacy thresholds are then compared against our prior knowledge of mortality and hospitalization
rates of untreated high-risk COVID-19 patients, as reported in the research literature.
Keywords: COVID-19; SARS-CoV-2; ambulatory treatment; early treatment, mortality; hospitalization; epidemiology; biostatistics; drug repurposing.
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