Clinical trial NCT04329832
Conditions: COVID-19
Keywords: SARS-CoV-2 Hydroxychloroquine Azithromycin
Sponsor: Intermountain Health Care, Inc.
Start date: 2020-03-30; Completion date: 2021-01-01
Enrollment: 85 participants; Allocation: RANDOMIZED; Intervention model: PARALLEL

Brief summary:
This study will compare two drugs (hydroxychloroquine and azithromycin) to see if hydroxychloroquine is better than azithromycin in treating hospitalized patients with suspected or confirmed COVID-19.

Detailed description:

Arms: arm 1: None arm 2: None
Interventions: intervention 1: Hydroxychloroquine intervention 2: Azithromycin
Intervention descriptions: intervention 1: Patients in the hydroxychloroquine arm will receive hydroxychloroquine 400 mg by mouth twice daily for 1 day, then 200 mg by mouth twice daily for 4 days (dose reductions for weight \< 45 kg or GFR (glomerular filtration rate)\<50ml/min). For patients \< 45kg, doses will be halved. For patients with GFR\<50ml/min, the final dose of hydroxychloroquine will not be administered. If the patient has already received hydroxychloroquine prior to randomization (no more than 2 days), the prior doses will count toward the 5-day total. intervention 2: Patients in the azithromycin arm will receive azithromycin 500 mg on day 1 plus 250 mg daily on days 2-5 (may be administered intravenously per clinician preference). If the patient has already received azithromycin prior to randomization (no more than 2 days), the prior doses will count toward the 5-day total.

Protocol documents:
Prot_000.pdf: 
 
 
 
Official Title: Hydroxychloroquine 
vs. Azithromycin for Hospitalized 
Patients With Suspected or 
Confirmed COVID-19 (HAHPS): A 
Prospective Pragmatic Trial 
 
 
NCT #: NCT04329832 
 
 
Document Date: Apr 25 2020 


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Protocol version 1.3 
 
 
 
 
CLINICAL STUDY PROTOCOL 
 
Hydroxychloroquine vs. Azithromycin for Hospitalized Patients with Suspected or Confirmed COVID-19 
(HAHPS): A prospective pragmatic trial 
 
 
 
 
Protocol Committee Chairs 
 
Samuel M. Brown, MD MS 
Associate Professor of Medicine 
Pulmonary/Critical Care Medicine 
Intermountain Medical Center/University of Utah 
 
 
 
 
 
 
 
 
Brandon Webb, MD 
 
 
 
 
 
 
 
Assistant Professor of Medicine 
Infectious Diseases 
Intermountain Medical Center/University of Utah 
 
 
 
 
 
 
 
 
Ithan Peltan, MD MSc 
 
 
 
 
 
 
 
Assistant Professor of Medicine 
Pulmonary/Critical Care Medicine 
Intermountain Medical Center/University of Utah 
 
 
 
Protocol Committee 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Tom Greene, PhD  
Colin Grissom, MD 
 
Estelle Harris, MD 
Stacey Johnson, MD  
 
 
 
 
 
 
 
Raj Srivastava, MD 
 
 
 
 
 
 
 
Whitney Buckel, PharmD 
 
 
 
 
 
 
 
Nathan Starr, DO 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 


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STATEMENT OF COMPLIANCE 
 
 
This study will be carried out in accordance with Good Clinical Practice (GCP) and information privacy/security 
as required by the following: 
 
 
United States (US) Code of Federal Regulations (CFR) 45 CFR Part 46 (The Common Rule) 
 
International Council on Harmonization (ICH) E6 (R2) 
 
45 CFR Part 160 and Part 164, Subparts A, C, and E  
 
 
All key personnel (all individuals responsible for the design and conduct of this study) have completed Human 
Subjects Protection Training. 
 
 
 
 


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KEY ROLES 
 
 
Individuals:  
 
 
 
Principal Investigator: SM Brown 
 
 
 
 
 
 
Medical Monitor: J Bledsoe 
 
 
Institutions:   
Data Management Core:  
Intermountain Medical Center 
 
Clinical Coordinating Center:  Intermountain Medical Center 
 
Biostatistics Core: 
 
University of Utah 
 
 
 
 
 
 
 
 
 
 


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Protocol Committee Chair Declaration 
TITLE: Hydroxychloroquine vs. Azithromycin for Hospitalized Patients with Suspected or Confirmed COVID-19 
(HAHPS) 
This study protocol has been critically reviewed by protocol committee members including myself. The protocol 
is consistent with current best knowledge of risks and benefits of study drug and with the ethical, moral, and 
scientific principles for clinical research as contained in ICH E6 standards and relevant US regulations. 
 
 
 
 
___________________________________________                                ________________ 
Samuel M. Brown, MD MS 
 
 
 
 
 
 
Date of signature 
 
 
 
 


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Site Principal Investigator Declaration 
TITLE: Hydroxychloroquine vs. Azithromycin for Hospitalized Patients with Suspected or Confirmed COVID-19 
(HAHPS) 
I have reviewed this study protocol and relevant associated training documents. I agree to abide the protocol 
requirements and follow Good Clinical Practice. The study will not commence without approval of the relevant 
Institutional Review Board (IRB). No changes will be made to the study without prior written approval from the 
protocol committee and the IRB unless it is necessary to control or eliminate an immediate risk to study 
subject(s). 
 
 
________________________________________                                      
Name (Printed) 
 
________________________________________                                     ______________ 
Signature   
 
 
 
 
 
 
 
 
Date of signature 
 
 


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Table of Contents 
1.0 
ABBREVIATIONS AND DEFINITIONS 
9 
2.0 
STUDY SUMMARY 
10 
2.1 
Study Title 
10 
2.2 
Study Objective 
10 
2.3 
Study Hypothesis 
10 
2.4 
Study design 
10 
2.5 
Inclusion criteria 
10 
2.6 
Exclusion criteria 
10 
2.7 
Sample size 
10 
2.8 
Primary Endpoint 
10 
2.9  
Interim Monitoring 
11 
3.0 STUDY DESCRIPTION 
11 
3.1 
Background 
11 
3.1.1 Evidence supporting possible efficacy for hydroxychloroquine. 
11 
3.1.2 Evidence against efficacy for hydroxychloroquine 
11 
3.1.3. Relevance of azithromycin 
11 
3.1.4. Role for an active comparator arm 
12 
3.2  
Study Design 
12 
3.3  
Study Objective 
12 
3.4 
Study Hypothesis 
12 
3.5  Primary endpoint 
12 
3.6 Secondary endpoints 
13 
4.0  
STUDY POPULATION AND ENROLLMENT 
13 
4.1  
Screening 
13 
4.2  
Inclusion criteria 
13 


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4.3  
Exclusion criteria 
14 
5.0 STUDY PROCEDURES 
14 
5.1 Study commencement 
15 
5.2 Study drug 
15 
5.3 Study Assessments 
15 
5.2.4 Six-month assessment 
15 
5.4 Approach to potential risks 
15 
5.4.1 Potential risks and risk mitigation 
16 
5.4.3 Reporting and Regulatory Compliance 
16 
5.4.4 ClinicalTrials.gov registration 
16 
5.4.5 Records retention 
16 
5.4.6 Protection against loss of privacy 
17 
5.5 Cohort retention techniques 
17 
5.5 Data collection 
17 
5.6 Analysis and interpretation of data 
17 
5.7 Power and sample size analysis 
19 
6.0 HUMAN SUBJECTS CONSIDERATIONS 
19 
6.1 Pandemic considerations 
19 
6.2 Institutional Review Board oversight 
20 
6.3 Selection of subjects 
20 
6.4 Justification for including vulnerable subjects 
20 
6.5 Informed consent 
20 
6.6 Continuing consent 
21 
6.7 Withdrawal of consent 
21 
6.8 Confidentiality 
21 
6.9 Protocol deviations and violations 
21 
APPENDICES 
24 
Appendix 1. Visit schedule 
24 


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Appendix 2: Screening logs 
25 
Appendix 3. Authorship criteria and management plan 
26 
 
Appendix 4. Adverse events reporting 
29 
Appendix 5. Potential medication interactions with hydroxychloroquine 
29 
 
 
 
 


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1.0 
Abbreviations and Definitions 
 
CCC = Clinical Coordinating Center 
DCC = Data Coordinating Center 
DSMB = Data Safety and Monitoring Board 
ER = Emergency Room 
ICU = Intensive Care Unit 
IMC = Intermountain Medical Center 
ITT = Intention to Treat 
LAR = Legally Authorized Representative 
SUSAR = Suspected Unexpected Serious Adverse Reaction 
 
Definitions 
 
Eligible patients: All patients who meet all inclusion criteria and no exclusion criterion.  
 
Enrolled participants: All eligible patients who have completed the consenting process. The time of 
enrollment will be the time informed consent was signed by the patient or legally authorized representative. 
 
Intention-to-treat (ITT): All randomized patients, regardless of whether they received study drug or completed 
study procedures. This is the primary analysis set. 
 
Legally Authorized Representative (LAR): An individual, judicial, or other body authorized under applicable 
law to consent on behalf of a prospective patient to the patient's participation in the clinical study.  
 
Randomized participants: Enrolled participants who undergo randomization, regardless of whether they 
receive any study drug. 
 
Serious adverse event (SAE) or serious suspected adverse reaction or serious unexpected suspected 
adverse event (SUSAR). An adverse event or suspected adverse reaction is considered "serious" if, in the 
view of the investigator, it results in any of the following outcomes: Death, a life-threatening adverse event, 
inpatient hospitalization or prolongation of existing hospitalization, a persistent or significant incapacity or 
substantial disruption of the ability to conduct normal life functions, or a congenital anomaly/birth defect. 
Important medical events that may not result in death, be life-threatening, or require hospitalization may be 
considered serious when, based upon appropriate medical judgment, they may jeopardize the patient or 
subject and may require medical or surgical intervention to prevent one of the outcomes listed in this definition. 
(cf. 21 CFR 312.32(a)). Events that are serious and unexpected and have a reasonable possibility that the 
event was due to a study procedure are considered SUSARs. 
 
Study day: The day of enrollment is study day zero. The next day is study day one, and so on.  
 
Study hospitals: The hospitals where the patient is enrolled.  
 
Study withdrawal: Permanent withdrawal from the study before the completion of study activities. Study 
procedures will cease upon withdrawal from the study. Data recorded up to the time of withdrawal will be 
included in the study analysis, unless consent to use that data has also been withdrawn. If a patient (or LAR) 
requests withdrawal from the study after completion of study procedures, study endpoints will be determined 
unless consent to do so has also been withdrawn. 
 


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2.0 
Study Summary 
 
2.1 
Study Title: Hydroxychloroquine vs. Azithromycin for Hospitalized Patients with Suspected or 
Confirmed COVID-19 (HAHPS). 
 
2.2 
Study Objective: Assess the efficacy and safety of hydroxychloroquine (HCQ) as opposed to 
azithromycin for suspected or confirmed COVID-19. 
 
2.3 
Study Hypothesis: Hydroxychloroquine is superior to azithromycin in improving outcomes among 
patients with suspected or confirmed COVID-19. 
 
2.4 
Study design: Phase 2 prospective, randomized, open-label active comparator trial. 
 
2.5 
Inclusion criteria 
 
 
Adult (age ≥ 18 years) 
 
Confirmed OR suspected COVID-19, 
o Confirmed: Positive assay for COVID-19 within the last 10 days 
o Suspected: Pending assay for COVID-19 WITH high clinical suspicion 
 
Scheduled for admission or already admitted to an inpatient bed 
 
2.6 
Exclusion criteria 
 
 
Allergy to hydroxychloroquine or azithromycin 
 
History of bone marrow transplant 
 
Known G6PD deficiency 
 
Chronic hemodialysis or glomerular filtration rate < 20ml/min 
 
Psoriasis 
 
Porphyria 
 
Concomitant use of digitalis, flecainide, amiodarone, procainamide, propafenone, cimetidine, dofetilide, 
phenobarbital, phenytoin, or sotalol 
 
Known history of long QT syndrome 
 
Current known QTc>500 msec 
 
Pregnant or nursing 
 
Prisoner 
 
Weight < 35kg 
 
Seizure disorder 
 
Severe liver disease 
 
Outpatient use of hydroxychloroquine for treatment of a disease other than COVID-19 OR has received 
more than 2 days of hydroxychloroquine or azithromycin for suspected or confirmed COVID-19 
 
Patient has recovered from COVID-19 and/or is being discharged from the hospital on day of enrollment. 
 
Treating physician refuses to allow patient participation in the study 
 
Unable to obtain informed consent 
 
Prior enrollment in this study 
 
2.7 
Sample size: The study will enroll as many as 300 patients. 
 
2.8 
Primary Endpoint:  
 
COVID Ordinal Outcomes Scale at 14 days 
 


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2.9  
Interim Monitoring. A Data Safety Monitoring Board will oversee this trial according to a DSMB Charter. 
3.0 STUDY DESCRIPTION 
 
3.1 
Background 
COVID-19 is pandemic with high mortality among hospitalized patients despite a benign course in the large 
majority of patients infected. Efficacious therapies are desperately needed. Hydroxychloroquine and 
chloroquine have antiviral and immune-modulating effects.1 Hydroxychloroquine is thought to be better 
tolerated and is used extensively in mild auto-immune disease.  
 
3.1.1 Evidence supporting possible efficacy for hydroxychloroquine.  
In cell models, chloroquine both interferes with terminal glycosylation of the ACE2 receptor (the cell surface 
receptor by which SARS-CoV2 enters human cells) and increases endosomal pH, which interferes (at least in 
vitro) with a crucial step in viral replication.1,2 Hydroxychloroquine is 5x more potent than chloroquine in a Vero 
cell model of SARS-CoV-2 infection.3 In independent experiments, chloroquine has confirmed in vitro activity 
against SARS-CoV-2.4 Additionally, hydroxychloroquine has in vitro efficacy against SARS-CoV-1.5 An informal 
(and methodologically inadequate) report from a case series of 20 patients in France suggested the possibility 
of more rapid viral clearance with both hydroxychloroquine and azithromycin, suggesting azithromycin may be 
a useful comparator for hydroxychloroquine.6 (The data have been widely misinterpreted in popular 
discussions as demonstrating efficacy and suggesting synergy; neither appears to be the case on the basis of 
the data presented.) According to news releases, an as-yet-unpublished set of case series in China (N 
reportedly 120) suggests the possibility of rapid viral clearance and low rates of progression to critical illness. 
In addition to in vitro anti-viral effects chloroquine and hydroxychloroquine appear to have immune-modulatory 
effects, especially via suppression of release of TNF and IL6, especially in macrophages.1,7-10 
 
3.1.2 Evidence against efficacy for hydroxychloroquine. 
Chloroquine and hydroxychloroquine have been promoted as extremely broad anti-infective agents for 
decades. The reported effects include suppression of fungi, atypical bacteria, and viruses. Other than the 
effects on ACE2 glycosylation, the mechanisms invoked as evidence for efficacy against SARS-CoV-2 have 
also been invoked for a wide range of viruses. However, when chloroquine and hydroxychloroquine have been 
studied in humans, neither agent has demonstrated consistent efficacy in clinical trials, including in HIV, 
influenza, hepatitis, and Dengue.11 In one trial, hydroxychloroquine even increased HIV viral load.12 Expert 
opinion advises against hydroxychloroquine for MERS, another serious coronavirus.13 The long history of 
clinical failure despite in vitro anti-viral activity suggests that in vitro evidence may not suggest clinical efficacy 
for hydroxychloroquine. 
 
The ANZICS guidelines emphasize that novel treatments should be administered within clinical trials; the 
Surviving Sepsis Campaign guidelines (http://bit.ly/SSCCOVID-19) also affirm the lack of evidence to support 
the clinical use of (hydroxy)chloroquine. WHO guidance 
(https://apps.who.int/iris/bitstream/handle/10665/331446/WHO-2019-nCoV-clinical-2020.4-eng.pdf) also 
strongly affirms that “investigational anti-COVID-19 therapeutics should be done under ethically approved, 
randomized, controlled trials.” The evidence thus strongly favors equipoise.14 
 
3.1.3. Relevance of azithromycin. 
Azithromycin, a macrolide antibiotic with anti-inflammatory properties has a longstanding, well established 
safety record. In a secondary analysis of an ARDS trial population (N=235) azithromycin was associated with 
higher survival.15 A retrospective study (with propensity matching) of 125 patients with sepsis-associated 
ARDS suggested lower 60-day mortality.16 A secondary analysis of a prospective cohort (N=873) suggested 
higher survival with azithromycin.17 While azithromycin is commonly recommended in combination with a beta-


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lactam for community-acquired pneumonia (which can be confused with COVID-19), randomized trial evidence 
suggests that omitting the macrolide is in fact non-inferior.18 
 
3.1.4. Role for an active comparator arm 
Given (1) the likelihood that COVID-19 will occur in pandemic waves, (2) clinicians and investigators have 
identified many treatments with early suggestions of benefit but no solid evidence for efficacy, (3) the good 
safety profile of hydroxychloroquine and azithromycin, and (4) the low probability that more than one will be 
effective, it is reasonable to rapidly evaluate the range of proposed candidates, favoring commonly used and 
non-toxic treatments for control groups. A pragmatic, comparative effectiveness trial of two active, nontoxic 
agents in current popular use during the COVID-19 pandemic is urgently indicated. 
 
3.2  
Study Design 
 
Phase 2 prospective, randomized, open-label, active comparator trial. 
 
3.3  
Study Objective 
 
Assess the efficacy of hydroxychloroquine (HCQ) as opposed to azithromycin for suspected or confirmed 
COVID-19. 
 
3.4 
Study Hypothesis 
 
Hydroxychloroquine is superior to azithromycin in improving clinical outcomes among patients with suspected 
or confirmed COVID-19. 
 
3.5  Primary endpoint 
 
The WHO proposed (https://www.who.int/blueprint/priority-diseases/key-action/COVID-
19_Treatment_Trial_Design_Master_Protocol_synopsis_Final_18022020.pdf) a slight modification of a 
preexisting COVID-19 ordinal endpoint, adding an extra status relevant only to prevention trials (no disease), 
and adding an extra status to reflect the difference between simple invasive ventilation and invasive ventilation 
plus additional organ support (ECMO, CRRT, or vasopressors). In the interest of synchronization, we will 
collect data to allow comparison with either the WHO or NIAID version of the scale. For analysis purposes, our 
primary endpoint is the WHO COVID Ordinal Outcomes Scale at day 14 (with the recognition that no patients 
are expected to have the status of never having developed a respiratory illness). The details of the endpoint 
are displayed in Table 1. 
 
Table 1. WHO COVID Ordinal Outcomes Scale * 
Patient State 
Descriptor 
Score 
Ambulatory 
No limitation of activities 
1 
 
Limitation of activities 
2 
Hospitalized, mild disease 
No oxygen therapy 
3 
 
Oxygen by mask or nasal 
 
cannulae 
4 
Hospitalized, severe disease 
Non-invasive ventilation or 
high-flow oxygen 
5 
 
Invasive mechanical 
ventilation without other 
organ support 
6 


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Invasive mechanical 
ventilation with other organ 
support (e.g., ECLS, CRRT, 
vasopressors) 
7 
Death 
Dead 
8 
ECLS: extracorporeal life support; CRRT: continuous renal replacement therapy 
*The score for the day reflects the worst status for the given calendar day 
 
3.6 Secondary endpoints 
 
Hospital-free days at 28 days (calculated as a worst-rank ordinal) 
Ventilator-free days at 28 days (calculated as a worst-rank ordinal) 
ICU-free days at 28 days (calculated as a worst-rank ordinal) 
Time to a 1-point decrease in the WHO ordinal recovery score 
 
The composite endpoints (-free days) will be calculated according to standard procedures (using the “last off” 
method) and using the worst-rank ordinal approach by which death is counted as -1 days. 
4.0  
Study Population and enrollment 
 
Our goal is to enroll all hospitalized patients at study hospitals with suspected or confirmed COVID-19 in a 
pragmatic randomized design. 
 
4.1  
Screening 
 
Daily screening in hospitals to identify hospitalized patients with the diagnosis of suspected or confirmed 
COVID-19. Patients who meet all inclusion criteria and no exclusion criteria will be asked to provide written 
informed consent (often via a legally authorized representative [LAR]), after which, the patient will be 
considered an enrolled participant. 
 
4.2  
Inclusion criteria 
 
 
Adult (age ≥ 18 years) 
 
Confirmed OR suspected COVID-19, 
o Confirmed: Positive assay for COVID-19 within the last 10 days 
o Suspected: Pending assay for COVID-19 WITH high clinical suspicion* 
 
Scheduled for admission or already admitted to an inpatient bed 
 
*The suspected COVID-19 criterion is intended to help match clinician behavior during the pandemic at the 
time that clinical decisions are being made. Delays in testing may force clinicians to make treatment 
decisions before all data are known. The fundamental notion of the suspected COVID-19 criterion is the 
experienced clinician’s belief that the patient has a high likelihood of being positive for COVID-19. The 
objective data supporting that belief will be updated regularly via FAQ and communication to study 
personnel and clinical pharmacists.  
 
At trial launch, these objective data are 
 
Known COVID-19 exposure with a credible incubation period before disease onset OR 
 
All of the following  
o ≤ 10 days of new cough or obvious worsening of a chronic cough 


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o Bilateral infiltrates consistent with COVID-19 OR new hypoxemia (<92% on room air or use 
of supplemental oxygen) 
o No alternative explanation for the acute illness presentation 
 
Given the dynamic nature of testing, the performance of the suspected COVID-19 criteria will be monitored 
at least weekly. If substantial rates of enrollment are observed among patients who are subsequently shown 
to be COVID-19 negative, the suspected COVID-19 criteria may be adjusted based on consultation with the 
protocol committee and the DSMB. If rapid turnaround time is achieved at a given site for COVID-19 testing, 
the suspected COVID-19 criterion may be suspended, as treatment based on positive test results would 
likely become clinical practice. Such changes will be communicated via a FAQ document and will not 
require a formal protocol amendment as long as they are consistent with the principles outlined herein. The 
IRB will be provided with copies of the FAQ as it is updated. 
 
4.3  
Exclusion criteria 
 
 
Allergy to hydroxychloroquine or azithromycin 
 
History of bone marrow transplant 
 
Known G6PD deficiency 
 
Chronic hemodialysis or glomerular filtration rate < 20ml/min 
 
Psoriasis 
 
Porphyria 
 
Concomitant use of digitalis, flecainide, amiodarone, procainamide, propafenone, cimetidine, dofetilide, 
phenobarbital, phenytoin, or sotalol 
 
Known history of long QT syndrome 
 
Current known QTc>500 msec 
 
Pregnant or nursing 
 
Prisoner 
 
Weight < 35kg 
 
Seizure disorder 
 
Severe liver disease 
 
Outpatient use of hydroxychloroquine for treatment of a disease other than COVID-19 OR has received 
more than 2 days of hydroxychloroquine or azithromycin for suspected or confirmed COVID-19 
 
Patient has recovered from COVID-19 and/or is being discharged from the hospital on day of enrollment. 
 
Treating physician refuses to allow patient participation in the study 
 
Unable to obtain informed consent 
 
Prior enrollment in this study 
 
Severe liver disease is defined as clinical diagnosis of cirrhosis plus presence or history of ascites, varices, 
hepatic encephalopathy, or hospital admission for liver failure. 
5.0 Study Procedures 
 
Note that given the high contagiousness of SARS-CoV-2, the limited supply of personal protective equipment, 
and risk to coordinators’ health, study visits, procedures, and monitoring will be performed in a no-touch mode. 
This also applies to the consenting process, which will be performed via a consenting mechanism that confirms 
the identity of the patient, provides access to the consent form, and obtains a signature to complete 
documentation, without requiring physical proximity of research staff and the study subject.  
 


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5.1 Study commencement. After informed consent and enrollment, coordinators will complete the baseline 
CRF, and initiate study procedures, including randomization (variable permuted block randomization by 
hospital).  
 
5.2 Study drug. This is an open label, randomized, active comparator trial. Patients who are discharged home 
will not continue treatment after hospital discharge. If the patient has already received azithromycin or 
hydroxychloroquine prior to randomization (no more than 2 days), the prior doses will count toward the 5-day 
total dose. For enrolled patients whose laboratory testing returns negative for SARS-CoV-2, if the clinical team 
believes that another cause of the patient’s presentation is more likely than COVID-19 in light of the negative 
laboratory testing, the clinical team may stop study drug. Such discontinuation will be recorded. Since the 
fundamental clinical question is whether to start treatment in patients with suspected or confirmed COVID-19, 
these patients will remain in the primary analytic cohort.  
 
The trial will be temporarily suspended for enrollment at a given site in the event that no study drug is available 
at that site. 
 
5.2.1. Hydroxychloroquine. Patients in the hydroxychloroquine arm will receive hydroxychloroquine 400mg po 
BID x 1 day, then 200mg po BID x 4 days3 (dose reductions for weight < 45kg or GFR<50ml/min). For patients 
< 45kg, doses will be halved. For patients with GFR<50ml/min, the final dose of hydroxychloroquine will not be 
administered. The drug dose chosen falls at the lower end of doses proposed in various international trials, but 
it has proven in vitro efficacy, with a ratio of lung tissue trough concentrations to the EC50 (effective 
concentration to suppress 50% of viral activity) of >20.3 Given in vitro confirmation of the adequacy of the dose 
and likely superior safety profile at the lower dose, we chose the total dose of 2.4gm over 5 days.  
 
Note: if the clinical attending physician believes that bacterial pneumonia is likely and requires a second 
antibacterial agent for “atypical” infection (an uncommon occurrence in COVID-19), patients may receive 
another agent (e.g., doxycycline or levofloxacin) as appropriate at the clinician’s discretion. 
 
5.2.2. Azithromycin. Patients in the azithromycin arm will receive azithromycin 500mg on day 1 plus 250mg 
daily on days 2-5 (may be administered IV per clinician preference).  
 
5.3 Study Assessments. Coordinators and/or data queries will classify the COVID Ordinal Outcomes Scale on 
days 1–7, 14. They will also review for adverse events, including especially rash, self-report of visual changes, 
or symptomatic hypoglycemia. They will ensure that an EKG is performed at 24 to 48 hours post-randomization 
time. If a clinical EKG is not performed during this timeframe, a research EKG will be performed. In addition, 
ventilator status and ICU and hospital stay (using the last-off method) will be used to determine ventilator- and 
ICU-free days at 28 days using the worst rank ordinal technique, whereby death is scored as -1 days. In 
addition, coordinators and/or data queries will classify the COVID Ordinal Outcomes Scale on day 14. If this 
cannot be determined from the medical record, the patient will be contacted via telephone, mail, or electronic 
methods to determine their status on day 14. 
 
5.2.4 Six-month assessment. A brief visit will ascertain the EQ-5D-5L to measure quality of life. Mortality and 
readmissions will also be assessed at that visit, which may be performed by telephone, electronic means, or in-
person, depending on the subject’s needs and preferences. 
 
5.4 Approach to potential risks. Hydroxychloroquine has been used in many thousands of patients as an anti-
malarial agent and in systemic lupus erythematosus and rheumatoid arthritis as a mild anti-inflammatory agent. 
The safety has been well established. Azithromycin has been used in millions of patients and is effective 
against the causes of bacterial pneumonia. Its immunomodulatory effects are also of interest in a variety of 
syndromes. While both agents have known side effects, such side effects are generally rare and well-tolerated. 


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This pragmatic study protocol is designed to optimally protect patients against risk through careful choice of 
exclusion criteria, pragmatic monitoring for common adverse events, and careful oversight by a DSMB. 
 
5.4.1 Potential risks and risk mitigation. The package insert for hydroxychloroquine lists visual impairment, 
cardiac impairment, QTc prolongation, worsening of psoriasis or porphyria, proximal myoneuropathy, 
suicidality, and hypoglycemia. Other reported risks include nausea, diarrhea, vomiting, abdominal pain, 
headache, dizziness, allergic reactions, rash, low blood counts, seizures, and death. All of these risks are quite 
rare and where observed are seen after prolonged administration, generally in the range of years. While ocular 
examinations are recommended after a year (and for acute symptoms), routine ocular examinations have 
never been recommended for brief duration of therapy. While QTc prolongation has been described, it is 
generally modest, and routine EKG monitoring is not normally practiced with administration of 
hydroxychloroquine. In addition, there is some concern that in patients with very low body weight or with low 
GFR the risk of adverse events may be increased. The potential risks of azithromycin primarily relate to QTc 
prolongation and Clostridioides difficile infection (CDI). Both are clearly described but quite rare with 
azithromycin.19 Additional risks include nausea, diarrhea, vomiting, and abdominal pain, allergic reactions, 
rash, infusion site reaction when given through a vein, abnormal liver function, drug interactions, development 
of drug-resistant bacteria, heart problems, muscle weakness, cardiac arrest, death. When hydroxychloroquine 
and azithromycin are co-administered, a modest prolongation in QTc may be observed, which is why we chose 
the more conservative threshold of 500msec rather than 550msec as an exclusion criterion. 
 
In order to mitigate these risks, we have defined eligibility criteria that exclude patients with elevated risk, keep 
dosage low, and restrict treatment to just 5 days and avoid outpatient therapy. Patients are monitored within 
the hospital during treatment. Research coordinators and/or data queries will monitor for adverse events. 
 
5.4.1.1 Monitoring EKG. An EKG will be performed between 24 and 48 hours after randomization (if no clinical 
EKG has been performed, then the EKG will be performed as a study procedure). This EKG is performed to 
determine whether the QTc is prolonged after the loading dose. If the QTc is > 500 ms, study drug will be 
stopped and an assessment made. If there are no other contributors than study drug to prolonged QTc, study 
drug will be stopped permanently. If there are contributors to QTc that can be reversed (e.g., electrolytes or 
other QT-prolonging drugs), these will be reversed at the discretion of the clinical team, with a follow-up EKG 
performed at the clinical team’s discretion. If the QTc is again <500 ms, the study drug will be resumed, with 
another EKG the day after resumption of study drug. If QTc is again >500 ms, study drug will be permanently 
discontinued. 
 
5.4.1.2 Daily medication review. For the 5 days of therapy, study personnel and/or clinical pharmacists will 
review medications being prescribed for patients on study. If a medication is being prescribed that is known to 
prolong QTc, study personnel and/or clinical pharmacists will contact the prescribing clinician to discuss 
whether the new medication is necessary and important and to make a clinical determination whether study 
drug and the new medication should be continued. This review will highlight especially the two classes of 
medications enumerated in Appendix 5. 
 
5.4.2 Reporting and Regulatory Compliance. All relevant information will be reported in compliance with 
relevant regulations and Good Clinical Practice. This is not a registrational trial but will be performed under an 
IND exemption, which was obtained before study launch. 
 
5.4.3 ClinicalTrials.gov registration. An appropriate entry in Clinicaltrials.gov will be created before any patient 
enrollments occur. Updates to the Clinicaltrials.gov entry will be made in compliance with relevant regulations. 
 
5.4.4 Records retention. Study sites will arrange for the retention of raw data as per institutional protocol but for 
a minimum of 5 years after receipt of any applicable regulatory notification relevant to this study. All data and 


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documents will be made available when requested by appropriate authorities. Records will be maintained to 
verify the existence of each patient in the study, as per standard protocol. 
 
5.4.5 Protection against loss of privacy. In order to protect a potential subject’s privacy, we will take multiple 
steps to protect the study subject from breach of confidentiality. The list linking the subject’s name and medical 
record number will be kept behind the hospital firewall in a password-protected file. This file will only be 
accessible through the hospital server to those individuals given password approval to access the file. 
Furthermore, the electronic database (REDCap) will be coded with a unique study identifier rather than with 
individually identifiable PHI (other than certain dates of service, as indicated above).  
 
5.5 Cohort retention techniques. Study follow-up will incorporate published, validated techniques for 
maximizing cohort retention.20-23 These retention techniques include careful logging of contact attempts, 
confirmation of contact information at each encounter, multi-modality contact approaches (e.g., letters, emails, 
phone calls, social media, spontaneous home visits) as necessary. We anticipate minimal difficulty with cohort 
retention. 
 
5.6 Data collection. Research coordinators and/or investigators will collect data and record them on paper 
and/or electronic CRFs. Data quality will be reviewed using front-end range and logic checks at the time of 
data entry and back-end monitoring of data using electronic reports. Patient records and case report forms will 
be examined locally (including, as appropriate, duplicate data entry) on a random basis to evaluate the 
accuracy of the data entered into the database and monitor for protocol compliance with expanded quality 
assurance efforts if the non-congruence rates exceed 5%. The coordinating center will maintain these data 
according to relevant regulations and security standards within a REDCap database. Note that given concerns 
for contagion during the pandemic, coordinators will practice “no-touch” practices for data collection and entry.  
 
5.7 Analysis and interpretation of data. A formal statistical analysis plan (SAP) will be written and finalized 
before the conclusion of enrollment and before the beginning of data analysis. The principles of this SAP are 
outlined here. 
 
General. Descriptive summaries will be produced for subject disposition, demographic and clinical 
characteristics by the randomized hydroxychloroquine and azithromycin groups. The primary analysis and 
other secondary analyses of efficacy outcomes will be performed in accordance with the intention-to-treat (ITT) 
principle, with all patients analyzed according to their randomized treatment assignment, irrespective of 
compliance. In addition to the formal statistical analyses described below, the proportions of subjects with each 
level of the 8-point COVID scale will be displayed in frequency tables and in graphical summaries for each day, 
from day 0 (baseline) through day 14 of follow-up. Other ordinal secondary outcomes will be summarized using 
medians, 10th, 25th, 75th, and 90th percentiles and plots of cumulative distribution functions.  
 
Adherence to the study medications will be summarized by providing the distributions of the number of days (0, 
1, 2, 3, 4, or 5) for which study medication is taken in the two randomized groups.  
 
Analysis Sets. The primary analysis and analyses of secondary efficacy outcomes will be performed in the ITT 
population which consists of all randomized patients. Summaries of safety outcomes will be performed in the 
safety population consisting of all patients who receive at least one dose of study medication.  
We will also perform secondary analyses of efficacy and safety within the subsets of the ITT and safety 
populations restricted to patients who were confirmed to have COVID-19 (following an approach used in e.g., 
the VIOLET trial24). 
 
Primary Analysis. The prespecified primary analysis will compare the day 14 assessment of the 8-level COVID 
Ordinal Outcomes Scale between the randomized hydroxychloroquine and azithromycin groups. This analysis 
will be performed using a proportional odds logistic regression model, with randomized treatment group as the 


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independent variable and patient age and the baseline level of the COVID Ordinal Outcomes Scale as 
covariates. Due to restriction of the cohort to hospitalized patients, the baseline COVID Ordinal Outcomes 
Scale distribution will include patients with levels 3 through 7. Under this model, the treatment effect is 
expressed as the odds ratio comparing the odds of a score > k vs. a score ≤ k between the hydroxychloroquine 
and azithromycin groups, so that an odds ratio less than 1 represents treatment benefit. The primary analysis 
using the proportional odds model is closely linked to the Wilcoxon-rank-sums test, and is thus expected to 
provide approximately valid inference even if the proportional odds assumption is violated.   
 
The primary analysis will be performed using a Bayesian framework, with a slightly conservative but relatively 
diffuse normal prior distribution assumed for the log transformed odds ratio. The normal prior will be assigned a 
mean of 0 (thus taking an agnostic position as to the direction of the treatment effect), and a standard deviation 
of 0.5415. This prior distribution assigns a probability of 0.80 to an odds ratio between 0.5 and 2.0, with a 
probability of 0.10 that the odds ratio is below 0.50 and also a probability of 0.10 that the odds ratio is greater 
than 2.0. The prior distributions for the intercept parameters and covariate regression coefficients in the 
proportional odds model will be defined in the SAP.  
 
The results of the Bayesian analysis will be expressed as 1) a graphical display of the posterior distribution of 
the odds ratio, 2) the posterior mean, median and mode with 95% credible intervals, and 3) the following 
probabilities of clinical relevance based on the posterior distribution of the odds ratio (OR) given the data: 
 
P1 = Pr(OR < 1), indicating the evidence for any benefit;   
P2 = Pr(OR < 1/1.25), indicating the evidence for a moderate benefit or greater; 
P3 = Pr(OR > 1), indicating the evidence for any harm; 
P4 = Pr(OR > 1.25), indicating the evidence for a moderate harm or greater; 
P5 = Pr(1/1.2 < OR < 1.2), indicating the evidence for similarity between the two treatments.  
 
Secondary Analyses. We will apply similar Bayesian proportional odds models to evaluate the effectiveness of 
the hydroxychloroquine vs. azithromycin interventions on each of the following secondary outcomes: 
ventilator-, ICU-, and hospital-free days through day 28 (with death assigned as a worst-rank ordinal level25,26 
to avoid survivorship bias). We will also apply a primary proportional odds model similar to that used for the 
primary analysis at day 14 to evaluate the effect of the randomized treatments on the COVID Ordinal 
Outcomes Scale at day 7.  
 
Missing Data. We expect less than 5% of subjects will have missing data on either the primary or key 
secondary outcomes listed above. Hence, our primary analyses will be restricted to non-missing observations, 
without imputation. For the primary outcome, a sensitivity analysis will be performed by applying multiple 
imputation, in which the imputation model will incorporate non-missing measurements on the COVID Ordinal 
Outcomes Scale obtained prior to the day 14 assessment as auxiliary variables.  
 
Safety. The safety of both the hydroxychloroquine vs. azithromycin treatments being evaluated in this study is 
already well established in studies of thousands of patients and in post-marketing surveillance. Safety will 
however be evaluated in the context of COVID-19 by providing counts (proportions) of adverse events, with 
special attention to those listed in the package insert for hydroxychloroquine and azithromycin and careful 
investigation of any SUSARs.  
 
Interim Monitoring. Under the Bayesian design, the posterior distribution describing the accumulating evidence 
provided by the data for treatment benefit or harm can be updated repeatedly as the trial proceeds, without the 
requirement for formal adjustments to type-1 error to account for multiple looks as is required by frequentist 
interim analysis. The coordinating center will work with the DSMB to identify a threshold for the posterior 
probability of benefit such that a posterior probability of benefit exceeding this threshold would lead to early 
termination for efficacy. The Bayesian interim monitoring approach will allow the DSMB flexibility in its choice 


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of times for interim looks. Additional details on the interim monitoring plan will be provided in the DSMB 
charter.  
 
The general strategy employed would potentially stop early for efficacy but not for futility, as we anticipate that 
the data from this trial could be patient-level meta-analyzed with other trials in similar populations. Given the 
nature of the COVID-19 pandemic (with sudden and transient numbers in a given location), it may be difficult 
for any single trial to answer a question definitively on its own. We will also include feasibility evaluations to 
allow us to determine when the first wave of COVID19 has resolved, such that further enrollment in the trial is 
unlikely. Given the potentially cyclic nature of COVID19, the DSMB and the PI and trial statistician may make a 
determination, given the totality of the evidence, whether to suspend or halt the trial if there is clear evidence 
that the first wave is over in Utah, with the option to resume enrollment during a second wave of illness. 
 
5.8 Power and sample size analysis 
 
We provide an approximate sample size and power calculation by providing the largest odds ratios 
(corresponding to the minimum detectable treatment effects, which are indicated by odds ratios less than 1, 
reflecting treatment benefit) which can be detected in a frequentist analysis under of the primary outcome 
under a proportional model without covariate adjustment.  The target sample size of this trial is 300 
randomized subjects; the table also considers smaller sample sizes to indicate the power the trial will reach 
under optimistic scenarios for controlling the COVID-19 outbreak. The power calculations were performed 
using statistical simulation (N=10,000 simulations per combination), and provide results very close to those 
provided by an approximation provided by Whitehead.27 We consider three scenarios for the distribution in the 
azithromycin group for the 8 categories of the COVID Ordinal Outcomes Scale: Scenario 1: Uniform 
distribution with probabilities of 0.125 for each score level, Scenario 2: Overall probabilities at 14 days 
averaging across both groups of the recent COVID-19 trial by Cao et al.28 (1= 0, 2= 0.36, 3=0.16, 4=0.23, 
5=0.05, 6=0.02, 7=0.02, 8=0.16), and Scenario 3, with probabilities averaged between Scenarios 1 and 2.  
 
Maximum Detectable Odds Ratios (below 1) with 80% Power,  
2-sided α= 0.05, Assuming 95% Follow-up 
Sample size  
Scenario 1 
(uniform) 
Scenario 2 
Scenario 3 
100 
0.34 
0.32 
0.33 
150 
0.43 
0.41 
0.42 
200 
0.48 
0.46 
0.48 
250 
0.52 
0.50 
0.52 
300 
0.55 
0.54 
0.55 
We note that when expressed as risk ratios the minimum detectable effects are closer to 1 than the odds ratios 
in the table. For example, in Scenario 3 with N = 300, the detectable odds ratio of 0.55 for a COVID Ordinal 
Outcomes Scale score > 3 translates to a detectable risk ratio of 0.702 = 0.337/0.48. Thus the target sample 
size of 300 provides good power to detect an important signal for superiority of hydroxychloroquine over 
azithromycin. In any case, we anticipate that the data from this study can meaningfully contribute to future 
meta-analyses of therapeutics for COVID-19. 
6.0 Human Subjects Considerations 
 
6.1 Pandemic considerations. 
The exponentially rapid spread of COVID-19 across the globe and the very high rate of contagion, especially in 
healthcare environments, necessitates flexibility in specific logistical details that is nevertheless robustly 
consistent with the ethical mandates of clinical research. The key aspects of this pragmatic trial special to the 


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pandemic circumstance are (1) time-sensitivity of trial launch and (2) the need for no-touch operations on the 
part of research staff to protect their safety. Time-sensitivity and the emotional valence of proposed therapies 
for a frightening pandemic infection—plus the need to rapidly evaluate a large number of proposed therapies, 
many of which are already entering clinical practice in advance of evidence—motivate the use of an 
expeditious, pragmatic, real-world, open-label, active comparator design for this evaluation of the efficacy of 
hydroxychloroquine. The mandate to protect research staff while assuring that informed consent is obtained 
drives the choice of electronic consent. “No-touch” consenting procedures will abide by Intermountain 
guidelines and standards for consenting in pandemic circumstances, including a HIPAA waiver as appropriate. 
 
6.2 Institutional Review Board oversight.  
Institutional Review Board (IRB) approval is required and will be secured before any subject is enrolled at the 
study site. IRB notices will be written and dated. The investigator will promptly report to IRB all changes in the 
research activity and all unanticipated problems involving risk to human subjects or others, and will not make 
any changes in the research without IRB approval, except where necessary to eliminate apparent immediate 
hazard to human subjects. 
 
6.3 Selection of subjects.  
Federal regulations at 45 CFR 46(a)(3) require the equitable selection of subjects. The sites will be evaluated 
to determine whether patient eligibility criteria are met, drawing on routine clinical data. No protocol-specific 
tests nor procedures will be performed as part of the screening process. Study exclusion criteria neither 
unjustly exclude classes of individuals from participation in the research nor unjustly include classes of 
individuals for participation in the research. 
 
6.4 Justification for including vulnerable subjects.  
The present research aims to investigate patients with COVID-19. Due to the nature of the condition, many of 
these patients will have impaired decision-making capabilities. Moreover, those with intact decision-making 
capacities likely have milder disease than those with impaired capacity, creating substantial risk of sampling 
bias. Therefore, this study could not be conducted if limited to enrolling only those subjects with retained 
decision-making capacity. Hence, subjects recruited for this trial are not being unfairly burdened with 
involvement in this research simply because they are easily available. They are the core group of patients most 
likely to experience benefit from hydroxychloroquine or azithromycin. 
 
6.5 Informed consent.  
Federal regulations at 21 CFR 50.20 and 45 CFR 46.111(a)(5) require that informed consent be sought from 
each prospective subject or the subject’s LAR. Each study participant or LAR must sign and date an IRB-
approved informed consent form. The consent form (via a HIPAA authorization) will also indicate that protected 
health information (PHI) may be released to relevant study personnel and regulatory agencies. 
 
Some of the patients approached for participation in this research protocol will be decisionally impaired due to 
their critical illness. Hence, some patients will not be able to provide informed consent. Accordingly, informed 
consent will be sought from the participant’s LAR, as defined at 45 CFR 46.102(c): “an individual or judicial or 
other body authorized under applicable law to consent on behalf of a prospective subject to the subject’s 
participation in the procedures(s) involved in the research.” In this study, LARs will be identified as stipulated 
by relevant regulations and statutes, as per the standards defined for clinical decision making, as is the 
standard practice in human subjects research. 
 
According to a previous President’s Bioethics Committee (National Bioethics Advisory Committee (NBAC)), an 
investigator should accept a relative or friend of the potential subject who is recognized as an LAR for 
purposes of clinical decision making under the law of the state where the research takes place.29 Finally, 
OHRP has stated in their determination letters that a surrogate could serve as a LAR for research decision 


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making if such an individual is authorized under applicable state law to provide consent for the “procedures” 
involved in the research study.30 
 
In cases of LAR consent, study personnel obtaining informed consent are responsible for ensuring that the 
LAR understands the risks and benefits of participating in the study, answering any questions the LAR may 
have throughout the study and sharing any new information in a timely manner that may be relevant to the 
LAR’s willingness to permit the subject’s continued participation in the trial. The study personnel obtaining 
consent will make every effort to minimize coercion, whether actual or perceived. All study participants or their 
LARs will be informed of the objectives of the study and the potential risks. The informed consent document 
will be used to explain the risks and benefits of study participation to the LAR in simple terms before the patient 
is enrolled in the study, and to document that the LAR is satisfied with his or her understanding of the risks and 
benefits of participating in the study and desires to participate in the study. The electronic consent form will 
document the receipt of informed consent. 
 
6.6 Continuing consent.  
For subjects for whom consent was initially obtained from a LAR but who subsequently regain decisional 
capacity while in hospital, sites will seek reconsent for continuing participation. Reconsent will also use 
electronic consenting methods; in the event that reconsent is not able to be obtained before 6-month followup, 
then consent for that telephone call will be obtained via oral consent, as the only additional study procedure at 
that point is a brief telephone call. 
 
6.7 Withdrawal of consent.  
Patients may withdraw or be withdrawn (by their LAR) from the study at any time. Data recorded up to the time 
of withdrawal will be included in the study analysis, unless consent to use data has also been withdrawn. If a 
patient (or LAR) requests withdrawal from the study after completion of study treatments, study endpoints will 
be determined unless consent to do so has also been withdrawn. 
 
6.8 Confidentiality.  
Federal regulations at 45 CFR 46.111(a)(7) require, when appropriate, adequate provisions to protect the 
privacy of subjects and to maintain the confidentiality of data. To maintain confidentiality, reports will be 
identified only by a coded study number. Only the study team will have access to the codes. All records will be 
kept in a locked, password protected computer. All computer entry and networking programs will access coded 
numbers only. All paper CRFs will be maintained in locked offices. Information shared outside Intermountain 
will be deidentified in accordance with federal regulations, e.g., HIPAA, except as otherwise indicated. Any 
data sharing will be regulated by appropriate data use agreements, where required. For the purposes of 
reporting, PHI may be disclosed to relevant regulatory agencies. 
 
6.9 Protocol deviations and violations.  
The investigator will not implement any deviation from, or changes of the protocol without prior review and 
documented approval/favorable opinion from the IRB of an amendment, except where necessary to eliminate 
immediate hazard(s) to study subjects, or when the change(s) involves only logistical or administrative aspects 
of the trial (e.g., change of telephone number(s)). Any deviation from the approved protocol will be documented 
and explained by the investigator or a person designated by the investigator. The investigator may implement a 
deviation from, or a change to, the protocol to eliminate immediate hazard(s) to trial subjects without prior IRB 
approval/favorable opinion. As soon as possible, the implemented deviation or change, the reasons for it, and 
if appropriate, the proposed protocol amendment will be submitted to the IRB. 
7 Adverse Events 
 
Patient safety is central to this study protocol. 


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7.1 Adverse event reporting. The following adverse events will be collected in the adverse event case report 
forms:  
 
 
Serious adverse events  
 
Nonserious adverse events that are considered by the investigator to be related to study drug or study 
procedures or of uncertain relationship 
 
Adverse events that lead to permanent discontinuation of the study drug. 
 
A clinical trial adverse event is any untoward medical event associated with the use of a drug or study 
procedure in humans, whether or not it is considered related to a drug or study procedure (see Appendix 4).  
After randomization, adverse events related to protocol procedures or occurring after the patient 
receives the first dose of study drug must be evaluated by the study investigator. If the adverse event is judged 
to be reportable, the investigator will report to the DSMB his/her assessment of the potential relatedness of 
each adverse event to study drug or study procedure. Investigators will assess whether there is a reasonable 
possibility that the study drug or procedure caused the event, based on the criteria outlined in Appendix 4. 
Investigators will also consider whether the event is unanticipated or unexplained given the patient’s clinical 
course, previous medical conditions, and concomitant therapies.  
Adverse events that will be collected prospectively as part of the study-specific outcome (and thus 
would not be separately reported as adverse events) are listed in Appendix 4. Study-specific outcomes will be 
reported as adverse events if they are both serious and Definitely or Possibly Related to study drug. 
 
7.2 Serious Adverse Events  
 
Serious adverse event collection begins after the patient or LAR has signed informed consent and has 
received study drug or undergone study procedures. If a patient experiences a serious adverse event after 
consent, but prior to receiving study drug, the event will NOT be collected unless the investigator feels the 
event may have been caused by a study procedure.  
 
Investigators must alert the DSMB of any serious and study drug or study procedure related adverse event 
within 24 hours of investigator awareness of the event. Alerts issued via telephone are to be immediately 
followed with official notification on the adverse event case report form. See Appendix 4 for reporting timelines 
for serious, unexpected, study related events (SAEs) and serious, unexpected suspected adverse reactions 
(SUSARs). 
 
As per federal definitions (primarily in 21 CFR 312.32(a)), a serious adverse event is any adverse event that 
results in one of the following outcomes:  
 
 
Death 
 
A life-threatening event, one that places the subject at immediate risk of death (this does not include an 
event that, had it been more serious, would have placed the subject at immediate risk of death) 
 
Prolonged inpatient hospitalization or rehospitalization  
 
Persistent or significant disability/incapacity, indicating a substantial disruption of a person's ability to 
conduct normal life functions (i.e., the adverse event resulted in a significant, persistent or permanent 
change, impairment, damage or disruption in the patient's body function/structure, physical activities 
and/or quality of life).  
 
Important medical events that may not result in death, be life-threatening, or require hospitalization may 
be considered serious adverse events when, based upon appropriate medical judgment, they may jeopardize 
the patient and may require medical or surgical intervention to prevent one of the outcomes listed in this 
definition.  


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Serious adverse events will be collected during the first five days, regardless of the investigator's 
opinion of causation. Thereafter, serious adverse events are not required to be reported unless the investigator 
feels the events were related to study drug or a protocol procedure. 
 
 
 
 
 


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APPENDICES 
 
Appendix 1. Visit schedule 
 
 
 
Visit 
Procedure 
Baseline 
24-48 
hours 
post- 
randomiz
ation 
Daily 
through 
5 days 
14 days 
Hospital 
discharge 
28 days 
6 months 
Screening procedures 
X 
 
 
 
 
 
 
Informed consent and LAR 
identification 
X 
 
 
 
 
 
 
Patient reconsent (where 
necessary) 
 
 
 
 
X 
 
 
Data collection 
X 
 
 
 
X 
 
 
EKG (research or clinical) 
 
X 
 
 
 
 
 
Study drug 
 
 
X 
 
 
 
 
Active Adverse Event review 
 
 
X 
 
 
 
 
Passive Adverse Event review 
 
 
 
 
X 
 
 
WHO ordinal scale 
 
 
 
X* 
 
 
 
VFD, ICUFD, HFD 
 
 
 
 
 
X 
 
EQ-5D-5L 
 
 
 
 
 
 
X 
LAR: legally authorized representative; WHO: World Health Organization; VFD: Ventilator-free days; ICUFD: ICU-free days; 
HFD: hospital-free days; EQ-5D-5L: EuroQol-5 dimensions-5 level 
*measured daily, days 1–7, 14 on all patients; measured days 1–14 on patients still hospitalized on day 14. 


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Appendix 2: Screening logs 
 
Screening activities will be carried out pursuant to 45 CFR 46.116(g) and 45 CFR 164.512(i). Patients in study 
hospitals will be entered into the screening log on the basis of an ordered COVID-19 test. Those who meet 
criteria for suspected or confirmed COVID-19 will be evaluated for all inclusion and all exclusion criteria. If all 
eligibility criteria are met, an informed consent conversation will occur. A HIPAA authorization waiver will be 
sought for this screening activity as per 45 CFR 164.512(i). 
 
 
 


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Appendix 3. Authorship criteria and management plan 
 
After the conduct of the Main Study, the following policy will guide authorship decisions.   
The ICMJE criteria for authorship will be followed. An author must make:  
 
Substantial contributions to the conception or design of the work; or the acquisition, analysis, or 
interpretation of data for the work; (a minimum of at least 2 of the above is needed) AND  
 
Drafting the work or revising it critically for important intellectual content; AND  
 
Final approval of the version to be published; AND  
 
Agreement to be accountable for all aspects of the work in ensuring that questions related to the 
accuracy or integrity of any part of the work are appropriately investigated and resolved. 
Contributors who do not meet all 4 of these criteria for authorship will be listed as collaborators rather than 
authors. 
 
For publications related to the Main Study (Main publications): 
The overall PI will direct the authorship decisions for Main publications and will determine the following: 
 
Whether the results related to the aims are published as part of one manuscript or divided into multiple 
publications. 
 
To which journals the manuscript(s) will be submitted 
 
Any revisions and revision decisions for resubmission to journals for publication 
 
The order of authorship for the main publications in order of contribution as determined by the Protocol 
Committee according to the ICMJE recommendations within the following parameters: 
o The first and senior author should be members of the Protocol Committee. The first and senior 
author can make preliminary decisions about the scope of the manuscript, write the initial draft and 
decide on journal for submission and then present the plan to the rest of the Protocol Committee for 
approval. 
o All members of the Protocol Committee will be authors on the Main publications, as long as they 
meet ICMJE criteria. 
o Among Site PIs who contributed equally to the analysis and write-up of the Main publications, 
authorship order will be according to the number of patients recruited from the Site PI’s site to the 
Main Study, with higher enrolling sites coming before lower enrolling sites. 
o If the journal limits the number of authors that can be listed, then the study will be attributed to the 
Protocol Committee and the Study Group, and the Site PIs and their sites will be listed in the 
acknowledgments, as collaborators, based on the number of patients enrolled from their site. In the 
event of ties, authorship order will be determined alphabetically, by site name. 
o Secondary site investigators from each site will not be included as authors unless they are on the 
protocol committee, but they will be listed as collaborators, based on the number of patients 
enrolled from their site. In the event of ties, authorship order will be determined alphabetically, by 
site name. 
 
For publications of ancillary studies: 
 
Any Site Investigator or member of the Protocol Committee can propose an ancillary study to be 
considered by the Protocol Committee. The Protocol Committee must evaluate the proposal for 
appropriateness and overlap with other proposals. 
 
The ancillary study proposal should include the following: 
o Background or Rationale 
o Study Objective or Hypothesis 
o Proposed methods: 
 
Study Design 
 
Study Population 


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 
Data Requested:  List data needed for analysis of exposure, outcomes, and associated 
covariates. 
 
New Data or Intervention requested:  List all data to be collected solely for the purpose of 
the proposed Ancillary Study. 
 
Proposed statistical approach 
 
Ancillary studies will be opened for other investigators to enlist as authors if they are interested. Lists of 
ancillary studies will be distributed periodically for review and sign up.  
 
The order of authorship will be decided by the investigator who proposed and leads the Ancillary Study, 
guided by the general principles outlined above and consistent with the ICMJE criteria. 
 
All abstracts and manuscripts from ancillary studies must be submitted to protocol committee for approval 
prior to submission. 
 
 
 
 


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Appendix 4. 
 
As noted in section 7.2, investigators will report all adverse events that are serious and study drug or 
study procedure related to the DSMB. The DSMB and the PI will then notify the IRB.  
The PI, in consultation with the DSMB, will determine whether a SAE has a reasonable possibility of 
having been caused by the study drug or procedure, as outlined in 21 CFR 312.32(a)(1). The PI, in 
consultation with the DSMB, will also determine whether the event is unexpected. An adverse is considered 
“unexpected” if it is not listed in the investigator brochure or the study protocol (21 CFR 312.32(a)). If a 
determination is made that a serious adverse event has a reasonable possibility of having been caused by the 
drug, it will be classified as a suspected adverse reaction. If the suspected adverse reaction is unexpected, it 
will be classified as a serious unexpected suspected adverse reaction (SUSAR).  
The PI will report all unexpected and study related deaths, SAEs, and SUSARs to the DSMB within 24 
hours of learning of the event. The PI and the DSMB will report their findings from review to the IRB within 7 
days after report of the event to the DSMB. A final written report will be provided to the DSMB and IRB within 
15 calendar days. The DSMB will also review all adverse events and clinical outcomes during scheduled 
interim analyses. 
 
A4.1. UNANTICIPATED PROBLEMS (UP)  
Investigators must also report Unanticipated Problems, regardless of severity, associated with the study 
drug or study procedures within 24 hours. An unanticipated problem is defined as follows: any incident, 
experience, or outcome that meets all of the following criteria:  
 
Unexpected, in terms of nature, severity, or frequency, given the research procedures that are 
described in the protocol-related documents, such as the IRB-approved research protocol and informed 
consent document; and the characteristics of the subject population being studied;  
 
Related or possibly related to participation in the research, in this guidance document, possibly related 
means there is a reasonable possibility that the incident, experience, or outcome may have been 
caused by the procedures involved in the research;  
 
Suggests that the research places subjects or others at a greater risk of harm (including physical, 
psychological, economic, or social harm) than was previously known or recognized.  
 
A4.2. DETERMINING RELATIONSHIP OF ADVERSE EVENTS TO STUDY DRUG OR PROCEDURES  
Investigators will grade the strength of the relationship of an adverse event to esmolol or study procedures as 
follows:  
• Definitely Related: The event follows: (a) a reasonable, temporal sequence from study drug or a study 
procedure; and (b) cannot be explained by the known characteristics of the patient’s clinical state or 
other therapies; and (c) evaluation of the patient’s clinical state indicates to the investigator that the 
experience is definitely related to study procedures.  
• Probably or Possibly Related: The event should be assessed following the same criteria for “Definitely 
Related”. If in the investigator’s opinion at least one or more of the criteria are not present, then 
“probably” or “possibly” associated should be selected.  
• Probably Not Related: The event occurred while the patient was receiving esmolol or undergoing 
study procedures but can reasonably be explained by the known characteristics of the patient’s clinical 
state or other therapies.  
• Definitely Not Related: The event is definitely produced by the patient’s clinical state or by other 
therapies administered to the patient.  
• Uncertain Relationship: The event does not meet any of the criteria previously outlined.  
 
A4.3. CLINICAL OUTCOMES THAT MAY BE EXEMPT FROM ADVERSE EVENT REPORTING  
Study-specific clinical outcomes of COVID-19 or ARDS are exempt from adverse event reporting unless the 
investigator deems the event to be related to the administration of study drug or the conduct of study 


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procedures (or of uncertain relationship). The following are examples of events that will be considered study-
specific clinical outcomes  
 
Death (unless Definitely or Probably related to study drug or procedures)  
 
Respiratory events: Changes in degree of hypoxemia or its treatment 
 
Cardiovascular events: changes in doses of vasoactive drugs; cardiac arrest; cardiac arrhythmias.  
 
Organ dysfunction events: changes in the SOFA score or its constituents. 
 
ICU admission 
 
Rash 
 
Hypoglycemia 
 
Seizure 
 
Elevations in ALT, AST, lipase, and amylase 
 
Acute Kidney injury and dialysis 
 
Cytopenias 
 
Nausea/vomiting 
 
Impaired vision 
 
C. difficile infection 
 
 
 
 


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A4.4. DECISION TREE FOR REPORTING ADVERSE EVENTS 
 
 
 
 
Adapted from Ranieri VM, Thompson BT, Barie PS et al. Drotrecogin alfa (activated) in adults with septic 
shock. N Engl J Med. 2012 May 31;366 (22):2055-64. Epub 2012 May 22. PubMed PMID: 22616830 with 
permission of Dr. Thompson. 
 
 
 
 


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Appendix 5. Potential medication interactions with hydroxychloroquine 
 
A. Medications considered contraindicated, which if ordered on an inpatient during the 5-day study period 
will prompt study personnel or clinical pharmacists to discuss with treating clinicians whether stopping 
the study drug is appropriate or whether this medication could be stopped or substituted: amiodarone, 
cimetidine, dofetilide, phenobarbital, phenytoin, sotalol. 
 
B. Medications considered to present a potential interaction with hydroxychloroquine, which if ordered on 
an inpatient during the 5-day study period, will prompt study personnel or clinical pharmacists to 
discuss with treating clinicians the risk-benefit assessment of this medication and potential need for 
additional monitoring: ampicillin, antacids, cyclosporine, digoxin, flecainide, mefloquine, methotrexate, 
mexilitine, rifampicin, rifapentine. 
 
 


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REFERENCES 
 
 
1. 
Savarino A, Boelaert JR, Cassone A, Majori G, Cauda R. Effects of chloroquine on viral infections: an 
old drug against today's diseases. The Lancet infectious diseases 2003;3:722-7. 
2. 
Vincent MJ, Bergeron E, Benjannet S, et al. Chloroquine is a potent inhibitor of SARS coronavirus 
infection and spread. Virol J 2005;2:69. 
3. 
Yao X, Ye F, Zhang M, et al. In Vitro Antiviral Activity and Projection of Optimized Dosing Design of 
Hydroxychloroquine for the Treatment of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). 
Clin Infect Dis 2020. 
4. 
Wang M, Cao R, Zhang L, et al. Remdesivir and chloroquine effectively inhibit the recently emerged 
novel coronavirus (2019-nCoV) in vitro. Cell Res 2020;30:269-71. 
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Biot C, Daher W, Chavain N, et al. Design and synthesis of hydroxyferroquine derivatives with 
antimalarial and antiviral activities. J Med Chem 2006;49:2845-9. 
6. 
Gautret P, al e. Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-
label non-randomized clinical trial. Int J Antimicrob Agents 2020;ePublished. 
7. 
Dijkmans B, Verweij C. Chloroquine and hydroxychloroquine equally affect tumor necrosis factor-
alpha, interleukin 6, and interferon-gamma production by peripheral blood mononuclear cells. The Journal of 
rheumatology 1997;24:55-60. 
8. 
Picot S, Peyron F, Vuillez J-P, Polack B, Ambroise-Thomas P. Chloroquine inhibits tumor necrosis 
factor production by human macrophages in vitro. J Infect Dis 1991;164:830. 
9. 
Jang C-H, Choi J-H, Byun M-S, Jue D-M. Chloroquine inhibits production of TNF-α, IL-1β and IL-6 
from lipopolysaccharide-stimulated human monocytes/macrophages by different modes. Rheumatology 
(Oxford) 2006;45:703-10. 
10. 
Sperber K, Quraishi H, Kalb T, Panja A, Stecher V, Mayer L. Selective regulation of cytokine secretion 
by hydroxychloroquine: inhibition of interleukin 1 alpha (IL-1-alpha) and IL-6 in human monocytes and T cells. 
The Journal of Rheumatology 1993;20:803-8. 
11. 
Akpovwa H. Chloroquine could be used for the treatment of filoviral infections and other viral 
infections that emerge or emerged from viruses requiring an acidic pH for infectivity. Cell Biochem Funct 
2016;34:191-6. 
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Paton NI, Goodall RL, Dunn DT, et al. Effects of hydroxychloroquine on immune activation and disease 
progression among HIV-infected patients not receiving antiretroviral therapy: a randomized controlled trial. 
JAMA 2012;308:353-61. 
13. 
Mo Y, Fisher D. A review of treatment modalities for Middle East respiratory syndrome. J Antimicrob 
Chemother 2016;71:3340-50. 
14. 
Cortegiani A, Ingoglia G, Ippolito M, Giarratano A, Einav S. A systematic review on the efficacy and 
safety of chloroquine for the treatment of COVID-19. J Crit Care 2020. 
15. 
Walkey AJ, Wiener RS. Macrolide antibiotics and survival in patients with acute lung injury. Chest 
2012;141:1153-9. 
16. 
Kawamura K, Ichikado K, Takaki M, et al. Efficacy of azithromycin in sepsis-associated acute 
respiratory distress syndrome: a retrospective study and propensity score analysis. Springerplus 2016;5:1193. 
17. 
Simonis FD, de Iudicibus G, Cremer OL, et al. Macrolide therapy is associated with reduced mortality in 
acute respiratory distress syndrome (ARDS) patients. Annals of translational medicine 2018;6. 
18. 
Postma DF, Van Werkhoven CH, Van Elden LJ, et al. Antibiotic treatment strategies for community-
acquired pneumonia in adults. N Engl J Med 2015;372:1312-23. 
19. 
Ray WA, Murray KT, Hall K, Arbogast PG, Stein CM. Azithromycin and the risk of cardiovascular 
death. N Engl J Med 2012;366:1881-90. 


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20. 
Robinson KA, Dennison CR, Wayman DM, Pronovost PJ, Needham DM. Systematic review identifies 
number of strategies important for retaining study participants. J Clin Epidemiol 2007;60:757-65. 
21. 
Tansey CM, Matte AL, Needham D, Herridge MS. Review of retention strategies in longitudinal studies 
and application to follow-up of ICU survivors. Intensive Care Med 2007;33:2051-7. 
22. 
Hunt JR, White E. Retaining and tracking cohort study members. Epidemiol Rev 1998;20:57-70. 
23. 
Hall EA, Zuniga R, Cartier J, et al. Staying in touch: a fieldwork manual of tracking procedures for 
locating substance abusers in follow-up studies. 2d ed. Los Angeles: UCLA Integrated Substance Abuse 
Programs; 2003. 
24. 
Ginde AA, Brower RG, Caterino JM, et al. Early High-Dose Vitamin D3 for Critically Ill, Vitamin D-
Deficient Patients. N Engl J Med 2019. 
25. 
Colantuoni E, Scharfstein DO, Wang C, et al. Statistical methods to compare functional outcomes in 
randomized controlled trials with high mortality. BMJ 2018;360:j5748. 
26. 
Novack V, Beitler JR, Yitshak-Sade M, et al. Alive and Ventilator Free: A Hierarchical, Composite 
Outcome for Clinical Trials in the Acute Respiratory Distress Syndrome. Crit Care Med 2020;48:158-66. 
27. 
Whitehead J. Sample size calculations for ordered categorical data. Stat Med 1993;12:2257-71. 
28. 
Cao B, Wang Y, Wen D, et al. A trial of lopinavir–ritonavir in adults hospitalized with severe Covid-19. 
N Engl J Med 2020. 
29. 
National Bioethics Advsory Committee. Research involving Persons with Mental Disorders that May 
Affect Decision-making Capacity. Rockville, MD: U.S. Government Printing Office; 1998. 
30. 
Compliance Determination Letters. 2002. at http://ohrp.osophs.dhhs.gov/detrm_letrs/.) 
 

SAP_001.pdf: 
 
 
 
Official Title: Hydroxychloroquine 
vs. Azithromycin for Hospitalized 
Patients With Suspected or 
Confirmed COVID-19 (HAHPS): A 
Prospective Pragmatic Trial 
 
 
NCT #: NCT04329832 
 
 
Document Date: Jun 29 2020 


HAHPS SAP 
 
HAHPS Study 
Statistical Analysis Plan version 1.0, 29JUN2020 
 
Responsible analytic personnel 
Tom Greene, PhD 
Professor of Medicine and Epidemiology 
University of Utah 
 
Allison Butler, MStat 
Biostatistician, Intermountain Medical Center 
 
Danielle Groat, PhD 
Biostatistician, Intermountain Medical Center 
 
 
 
Responsible investigators 
 
 
 
 
 
 
 
Samuel Brown, MD 
 
 
 
 
 
 
 
Associate Professor of Medicine 
 
 
 
 
 
 
 
Intermountain Medical Center/University of Utah 
 
 
 
 
 
 
 
 
Lindsay Leither, DO 
 
 
 
 
 
 
 
Physician investigator 
 
 
 
 
 
 
 
Intermountain Medical Center 
 
 
 
 
 
 
 
 
Robert Paine, MD 
 
 
 
 
 
 
 
Professor of Medicine 
 
 
 
 
 
 
 
University of Utah 
 
 
 
 
 
 
 
 
Ithan Peltan, MD 
 
 
 
 
 
 
 
Assistant Professor of Medicine 
 
 
 
 
 
 
 
Intermountain Medical Center 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 


HAHPS SAP 
 
Table of Contents 
BACKGROUND 
2 
SUMMARY OF ANALYTIC APPROACH 
3 
General 
3 
Analysis Sets 
3 
Primary Analysis 
4 
Secondary Analyses 
7 
Sub-groups 
8 
Missing Data 
8 
Safety reporting 
9 
Concomitant medications 
11 
Interim Monitoring 
12 
Sample size and power 
12 
 
 
 
Background 
 
The HAHPS trial is a randomized, open label, active comparator trial of hydroxychloroquine vs. 
azithromycin for hospitalized patients with COVID-19. The HAHPS trial enrolled patients admitted to 
the hospital with suspected or confirmed COVID-19. As outlined in the protocol and a prior 
publication,1 the eligibility criteria excluded patients with markers of increased risk associated with 
administration of hydroxychloroquine or azithromycin. The trial is being conducted at Intermountain 
and University of Utah hospitals. While the trial is open-label, outcomes assessors and investigators 
are blinded to outcomes by treatment group. This SAP was written by blinded personnel, without 
knowledge of study outcomes. 
 


HAHPS SAP 
Summary of analytic approach 
 
General. Descriptive summaries will be produced for subject disposition, demographic and clinical 
characteristics of the randomized hydroxychloroquine and azithromycin groups. The primary analysis 
and other secondary analyses of efficacy outcomes will be performed in accordance with the 
intention-to-treat (ITT) principle, with all patients analyzed according to their randomized treatment 
assignment, irrespective of compliance. In addition to the formal statistical analyses described below, 
the proportions of subjects with each level of the 8-point COVID scale will be displayed in frequency 
tables and in graphical summaries for each day, from day 0 (baseline) through day 14 of follow-up. 
Other ordinal secondary outcomes will generally be summarized using medians, 10th, 25th, 75th, and 
90th percentiles and plots of cumulative distribution functions. In cases where a substantial proportion 
of subjects fall into an extreme category, several of the indicated quantiles may take on the value of 
this category and a quantile-based summary may not be fully informative. In these cases, we will 
tabulate the proportions of subjects which fall into designated categories which are defined based on 
clinical relevance. 
Adherence to the study medications will be summarized by providing the distributions of the 
proportion of doses of study medication which were administered to each patient overall and by 
randomized group. We will compute two proportions: 1) The proportion of protocol-intended dose, in 
which doses for which the patient had a protocol-specified reason not to receive study medication 
(e.g., prolonged QTc, died, or discharged home) will not be counted in the denominator for proportion 
of scheduled doses received. 2) The proportion of the total ideal dose, in which doses for which the 
patient had a protocol-specified reason not to receive study medication will be counted. We will also 
describe the median (IQR) for total doses received for both protocol-intended dose and ideal dose. 
 
Analysis Sets. The primary analysis and analyses of secondary efficacy outcomes will be performed 
in the ITT population, which consists of all randomized patients. Although the trial was launched with 
the flexibility to enroll patients with suspected rather than confirmed COVID-19, no patients were 


HAHPS SAP 
actually enrolled under that criterion. The ITT population thus simplifies to patients with laboratory-
confirmed COVID-19. Summaries of safety outcomes will be performed in the safety population 
consisting of all patients who receive at least one dose of study medication. In a subgroup of the ITT 
population, we will compare study endpoints among patients who had not received azithromycin 
before randomization. 
 
Primary Analysis. The prespecified primary analysis will compare the day 14 assessment of the 8-
level COVID Ordinal Outcomes Scale between the randomized hydroxychloroquine and azithromycin 
groups. We will perform this analysis using a proportional odds logistic regression model (also called 
an ordinal regression model),2 with randomized treatment group as the independent variable. Patient 
age, comorbidities (dichotomized as either no comorbidities or any comorbidities, with the 
comorbidities drawn from hypertension, COPD, and the Charlson constituent comorbidities3 (Table1), 
and the baseline level of the COVID Ordinal Outcomes Scale will be included in the analysis as 
covariates. Due to restriction of the cohort to hospitalized patients, the baseline COVID Ordinal 
Outcomes Scale distribution will include only patients with levels 3 through 7. In a sensitivity analysis, 
we will include the total amount of pre-randomization azithromycin received as another covariate in 
the primary model. 
Table 1. Charlson Comorbidities 
Moderate/Severe Liver Disease 
Cerebrovascular Disease (CVD) 
Peripheral Vascular Disease (surgical)† 
Renal disease 
Peripheral Vascular Disease (diagnosis) † 
Paralysis (Hemiplegia or Paraplegia) 
Dementia 


HAHPS SAP 
Mild liver disease 
Congestive heart failure (CHF) 
Chronic Obstructive Pulmonary Disease (COPD) 
Diabetes with complications 
Diabetes 
 
Under the ordinal regression model, the treatment effect is expressed as the odds ratio 
comparing the odds of patients having a score that exceeds a given threshold vs. a score that is less 
than or equal to that threshold, and those odds are compared between the hydroxychloroquine and 
azithromycin groups. An odds ratio less than 1 thus represents treatment benefit for 
hydroxychloroquine over azithromycin. The model assumes that this odds ratio is the same across all 
possible thresholds across the 8-level ordinal scale. However, the primary analysis using the 
proportional odds model is closely linked to the Wilcoxon-rank-sums test and is thus expected to 
provide approximately valid inference even if the proportional odds assumption is violated.4  
Nonetheless, we will graphically assess the compatibility of the data with the proportional odds 
assumption by running a series of binary logistic regressions with varying cutpoints on the dependent 
variable and visually checking the deviation of coefficients across cutpoints. If the proportional odds 
assumption is clearly violated, our primary analysis under the proportional odds model will be 
supplemented with separate odds ratios comparing different groups of categories for which the 
proportional odds assumption is violated.    
Following a structure proposed by Harrell and collaborators 
(http://hbiostat.org/proj/covid19/bayesplan.html), the primary analysis will be performed using a 
Bayesian framework, with a conservative normal prior distribution assumed for the log transformed 
odds ratio. The normal prior will be assigned a mean of 0 (thus taking an agnostic position as to the 
direction of the treatment effect), and a standard deviation of 0.354. This prior distribution assigns a 
probability of 0.95 to an odds ratio between 0.5 and 2.0, with a probability of 0.025 that the odds ratio 


HAHPS SAP 
is below 0.50 and also a probability of 0.025 that the odds ratio is greater than 2.0. The use of this 
moderately conservative Bayesian prior will tend to shrink the observed proportional odds ratio 
towards 0, and thus moderately discount extreme observed odds ratios which may occur particularly 
for early interim analyses with relatively small sample size. 
The results of the Bayesian analysis will be expressed as (1) a graphical display of the 
posterior distribution of the odds ratio, (2) the posterior mean, median and mode with 95% credible 
intervals, and (3) the following probabilities of clinical relevance based on the posterior distribution of 
the odds ratio (OR) given the data: 
 
P1 = Pr(OR < 1), indicating the evidence for any benefit of hydroxychloroquine over 
azithromycin;   
P2 = Pr(OR < 1/1.25), indicating the evidence for a moderate benefit or greater of 
hydroxychloroquine over azithromycin; 
P3 = Pr(OR > 1), indicating the evidence for any harm of hydroxychloroquine over 
azithromycin; 
P4 = Pr(OR > 1.25), indicating the evidence for a moderate harm or greater of 
hydroxychloroquine over azithromycin; 
P5 = Pr(1/1.2 < OR < 1.2), indicating the evidence for similarity between the two treatments.  
For the purposes of primary inference, we will employ the 95% posterior probability of P1 or P3 as 
evidence for either benefit or harm of hydroxychloroquine over azithromycin. Evidence for moderate 
harm of hydroxychloroquine (P4) would be considered to be demonstrated at a lower 75% posterior 
probability threshold. In any case, the distributions of P1–P5 will be displayed for reference in the 
statistical report of the primary analysis. For clarity and simplicity, posterior Bayesian probabilities will 
be estimated by applying a normal approximation to the maximum likelihood estimate of the 
proportional odds ratio obtained by fitting the likelihood function to the data. This simplification will 


HAHPS SAP 
avoid the complication of specifying prior distributions for the intercept coefficients in the proportional 
odds model. 
 
Secondary Analyses. We will apply similar Bayesian proportional odds models to evaluate the 
effectiveness of the hydroxychloroquine vs. azithromycin interventions for efficacy analyses of all 
ordinal outcomes. We will also present numbers (proportions) of patients in each ordinal status for all 
endpoints for descriptive purposes and to assist with future meta analyses. If the graphical 
diagnostics (described above) indicate that the proportional odds model is untenable for certain 
outcomes, we will report that fact, and supplement the primary analysis using the proportional odds 
model with separate logistic regression for different cutpoints or quantile regression in order to fully 
characterize the relationship of each outcome with treatment assignment. 
Ordinal secondary outcomes include ventilator-, ICU-, and hospital-free days through day 28 
(with death assigned as a worst-rank ordinal level5,6 to avoid survivorship bias for each of these 
outcomes). We will use the last-off method—only time after the last liberation from, e.g., ventilation 
counts toward the total number of -free days. Similar to the primary outcome measured at 14 days, 
we will apply a primary proportional odds model similar to that used for the primary analysis at day 14 
to evaluate the effect of the randomized treatments on the COVID Ordinal Outcomes Scale at day 7. 
We will present the results of each of these analyses as the posterior median odds ratio with 95% 
credible interval under the moderately skeptical Bayesian prior described above. For ease of use for 
investigators performing meta-analyses, we will include detailed descriptions including number of 
patients for each value of the endpoints by treatment group as tables in the online supplement. 
 
We include two time-to-event endpoints, censored at 28 days: time to a 1-point decrease in the 
COVID Ordinal scale and time to recovery (defined as in the ACTT-1 trial). The time to recovery 
endpoint was added after publication of the methods paper but before unblinding of investigators. It 
was added to assist with future meta analyses given increasing interest in it as an endpoint. Both 
time-to-event endpoints will be displayed by treatment group (for descriptive purposes) as Kaplan-


HAHPS SAP 
Meier curves. Patients who die prior to day 28 are assigned an artificial follow-up time of 28 days 
censored at that point. This will assign to these patients a worse outcome than any surviving patients, 
and avoid conflating prolonged hospitalization with death. Descriptively, these time-to-event 
endpoints will be displayed as median (IQR). If fewer than half of patients meet the event before 
censoring, we will choose an appropriate quantile (e.g., first quartile) for description and display the 
proportions with events during the respective weeks of follow-up. For the sake of consistency, we will 
use the same ordinal regression model with the same prior distribution for the odds ratio as was used 
for the primary analysis to generate median posterior odds ratios and credible intervals for these 
endpoints. 
 
Sub-groups. On a purely exploratory level, we will summarize effect variation in sub-groups defined 
by (a) age ≥ 65 years, (b) symptom duration ≥ 10 days, (c) in ICU at enrollment, (d) presence of at 
least one comorbidity, (e) race/ethnicity, and (f) receipt of any clinical azithromycin before enrollment. 
At early interim analyses, the sample size will be too small to support meaningful statistical inference, 
and we will limit presentation of results in sub-groups to descriptive summaries using the summary 
measures described for each outcome above. 
 
Missing Data. We expect fewer than 5% of subjects will have missing data on either the primary or 
key secondary outcomes listed above. Hence, our primary analyses will be restricted to non-missing 
observations, without imputation. For the primary outcome, a sensitivity analysis will be performed by 
applying single imputation. Where daily COVID Ordinal Outcomes Scale scores are missing after 
hospital discharge, we will use last carried forward with the following modifications—patients 
discharged to a facility other than home will be classified as “home with limitations” (score 2), and 
patients found subsequently to be home with limitations will be rated as “home with limitations” from 
hospital discharge through that follow-up point. Patients who are discharged to home and found to 
have no limitations at day 14 will be rated as “home without” limitations starting at the time of hospital 


HAHPS SAP 
discharge. This approach will be used because persisting limitations are unlikely to have resolved and 
then recurred by 14 days. Similar single imputation rules will be devised for sensitivity analyses of 
secondary efficacy endpoints consistent with prior practice in the field and will be determined before 
the given analysis is performed to avoid bias from knowledge of outcomes. 
 
Safety reporting 
 
The HAHPS trial uses the adverse event reporting developed for the PROWESS-SHOCK 
trial.7,8 This approach is based on the high incidence of conditions resulting from the underlying 
disease that would be classified as adverse events if they occurred in healthy outpatients but are 
unlikely to be related to the study drug. Instead of being classified as adverse events, such events are 
monitored as study clinical or safety outcomes (Table 2). The exception to this rule are events which 
are either serious and study-related or which lead to permanent discontinuation of study drug. Those 
latter are reported as adverse events and are classified within the MedDRA taxonomy. 
For reporting purposes, for each safety outcome or adverse event each patient will be 
classified as having experienced the outcome at least once (vs. never having experienced the 
outcome). We will separately provide counts (proportions) of safety outcomes and individual adverse 
events. In early interim analyses, comparisons of safety outcomes between treatment groups will be 
viewed as primarily descriptive. However, we will provide nominal p-values to compare adverse 
events and safety outcomes between groups without multiple comparison adjustment using the Chi 
square or Fisher exact test as appropriate to provide a crude assessment of the compatibility of 
observed differences between treatment groups with chance. Isolated p-values which are < 0.05 will 
be interpreted recognizing that 5% of such p-values will reach this threshold by chance in the 
absence of an actual effect of the treatment. 
QTc was monitored at 24–48h after randomization as a study procedure. QTc monitoring at 
baseline was not mandatory but was described where it was available. We also described the change 
between baseline and 24–48h after randomization in QTc. These results will be summarized 


HAHPS SAP 
descriptively by treatment group. If the baseline QTc is available in at least 80% of participants, an 
analysis of covariance will be performed to compare follow-up QTc between treatment groups with 
adjustment for baseline QTc. Otherwise, a 2-sample t-test will be used to compare the follow-up QTc 
levels. 
 
  
Table 2 Safety outcomes (measured daily 
for first five days) 
Death unrelated to study drug or procedures 
Seizure 
Receipt of vasopressors 
Atrial or ventricular arrhythmia 
Cardiomyopathy 
cardiac arrest 
Hypoxemia requiring supplemental oxygen 
ARDS 
Mechanical ventilation 
ECMO 
Elevated LFTs 
Pancreatitis 
Acute kidney injury 
Renal replacement therapy 
Symptomatic hypoglycemia 
Hematologic or coagulation events 
Cytopenias 
Severe dermatologic reaction 


HAHPS SAP 
Nausea/vomiting 
Visual changes 
C. diff infection 
 
 
 
Concomitant medications 
 
The classes of concomitant medications listed in Table 3 were measured at baseline (before 
randomization) and through day 5 after randomization. Concomitant medications will be descriptively 
summarized overall and by treatment group. In addition, some patients in the hydroxychloroquine arm 
received azithromycin either before or in some cases after randomization. The proportion receiving 
azithromycin was recorded, and in exploratory summaries, safety outcomes will be displayed in that 
group as well as in the groups who received only azithromycin or only hydroxychloroquine. 
Table 3. Concomitant 
medications 
ACE inhibitors 
ARBs 
NSAIDs 
Glucocorticoids 
Immune-suppression 
Chloroquine 
Hydroxychloroquine 
Azithromycin 
Zinc 
Vitamin C 


HAHPS SAP 
 
Interim Monitoring.  
Under the Bayesian design, the posterior distribution describing the accumulating evidence 
provided by the data for treatment benefit or harm was updated at successive interim analyses during 
the trial.9 After consultation with the DSMB and consistent with the DSMB charter written before the 
first interim analysis, we performed interim analyses after enrollment of every 60 patients. This 
cadence for interim analyses was confirmed through simulation to yield performance characteristics 
consistent with a trial-wise alpha of approximately 0.05 if a frequentist approach had been taken.10 
We adopted a Bayesian approach to allow the DSMB to recommend continuation or closure of the 
trial based on the totality of evidence, including data from the present trial and from other relevant 
experiments or trials. While the trial is not designed to be stopped for futility based on the trial data 
alone, the DSMB is authorized to incorporate the totality of evidence in making decisions about 
recommending closure of the trial to further enrollment. 
 
Sample size and power. We generated an approximate sample size and power calculation by 
providing the odds ratios closest to 1 (corresponding to the minimum detectable treatment effects, 
which are indicated by odds ratios less than 1, reflecting treatment benefit for hydroxychloroquine 
over azithromycin) which can be detected in a frequentist analysis of the primary outcome under a 
proportional model without covariate adjustment. The target sample size of this trial is 300 
randomized subjects; Table 4 also considers smaller sample sizes to indicate the power the trial will 
reach under various scenarios for enrollment depending on state-wide disease dynamics. The power 
calculations were performed using statistical simulation (N=10,000 simulations per combination of 
parameters), and provide results very close to those provided by an approximation provided by 
Whitehead.11 For the sample size calculation, we considered three scenarios for the distribution in the 
azithromycin group for the 8 categories of the COVID Ordinal Outcomes Scale: Scenario 1: Uniform 
distribution with probabilities of 0.125 for each score level, Scenario 2: Overall probabilities at 14 days 


HAHPS SAP 
averaging across both groups of the recent COVID-19 trial by Cao et al.12 (1= 0, 2= 0.36, 3=0.16, 
4=0.23, 5=0.05, 6=0.02, 7=0.02, 8=0.16), and Scenario 3, with probabilities averaged between 
Scenarios 1 and 2.  
 
Table 4. Maximum Detectable Odds Ratios (below 1) with 80% 
Power, 2-sided α= 0.05, Assuming 95% Follow-up 
Sample size  
Scenario 1 
(uniform) 
Scenario 2 
Scenario 3 
100 
0.36 
0.35 
0.36 
150 
0.44 
0.42 
0.44 
200 
0.49 
0.47 
0.49 
250 
0.52 
0.52 
0.52 
300 
0.56 
0.55 
0.55 
 
 
We note that when expressed as risk ratios the minimum detectable effects are closer to 1 than the 
odds ratios in Table 4. For example, in Scenario 3 with N = 300, the detectable odds ratio of 0.55 for 
a COVID Ordinal Outcomes Scale score > 3 translates to a detectable risk ratio of 0.702 = 
0.337/0.48. Thus the target sample size of 300 provides sufficient power to detect a substantial signal 
for superiority of hydroxychloroquine over azithromycin. 
 
 
 
 
 


HAHPS SAP 
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