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Quantifying treatment effects of hydroxychloroquine and azithromycin for COVID-19: a secondary analysis of an open label non-randomized clinical trial (Gautret et al, 2020)

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Human infections with a novel coronavirus (SARS-CoV-2) were first identified via syndromic surveillance in December of 2019 in Wuhan China. Since identification, infections (coronavirus disease-2019; COVID-19) caused by this novel pathogen have spread globally, with more than 250,000 confirmed cases as of March 21, 2020. An open-label clinical trial has just concluded, suggesting improved resolution of viremia with use of two existing therapies: hydroxychloroquine (HCQ) as monotherapy, and in combination with azithromycin (HCQ-AZ). The results of this important trial have major implications for global policy in the rapid scale-up and response to this pandemic. The authors present results with p-values for differences in proportions between the study arms, but their analysis is not able to provide effect size estimates. To address this gap, more modern analytical methods including survival models, have been applied to these data, and show modest to no impact of HCQ treatment, with more significant effects from the HCQ-AZ combination, potentially suggesting a role for co-infections in COVID-19 pathogenesis. The trial of Gautret and colleagues, with consideration of the effect sizes, and p-values from multiple models, does not provide sufficient evidence to support wide-scale rollout of HCQ monotherapy for the treatment of COVID-19; larger randomzied studies should be considered. However, these data do suggest further study of HCQ-AZ combination therapy should be prioritized as rapidly as possible.
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QUANTIFYING TREATMENT EFFECTS OF
HYDROXYCHLOROQUINE AND AZITHROMYCIN FOR COVID-19:
A SECONDARY ANALYSIS OF AN OPEN LABEL
NON-RANDOMIZED CLINIC AL TRI AL
A PR EPRIN T
Andrew A. Lover
Department of Biostatistics and Epidemiology
University of Massachusetts- Amherst
Amherst, MA
alover@umass.edu
March 22, 2020
ABS TR ACT
Human infections with a novel coronavirus (SARS-CoV-2) were first identified via syndromic
surveillance in December of 2019 in Wuhan China. Since identification, infections (coronavirus
disease-2019; COVID-19) caused by this novel pathogen have spread globally, with more than
250,000 confirmed cases as of March 21, 2020. An open-label clinical trial has just concluded,
suggesting improved resolution of viremia with use of two existing therapies: hydroxychloroquine
(HCQ) as monotherapy, and in combination with azithromycin (HCQ-AZ). [1, 2].
The results of this important trial have major implications for global policy in the rapid scale-up and
response to this pandemic. The authors present results with p-values for differences in proportions
between the study arms, but their analysis is not able to provide effect size estimates.
To address this gap, more modern analytical methods including survival models, have been applied to
these data, and show modest to no impact of HCQ treatment, with more significant effects from the
HCQ-AZ combination, potentially suggesting a role for co-infections in COVID-19 pathogenesis.
The trial of Gautret and colleagues, with consideration of the effect sizes, and p-values from multiple
models, does not provide sufficient evidence to support wide-scale rollout of HCQ monotherapy for
the treatment of COVID-19; larger randomzied studies should be considered. However, these data do
suggest further study of HCQ-AZ combination therapy should be prioritized as rapidly as possible.
Keywords COVID-19 ·Emerging pathogens ·Pharmaceutical therapies ·Clinical trials ·Secondary analyses
1 Introduction
Evidence-based public health programming is essential for global pandemic planning, and optimization of resources.
However, unadjusted analyses may provide distorted estimates, or not full utilize scarce clinical data, especially in with
consideration of intention-to-treat analyses, where all persons enrolled are analysed [
3
,
4
]. Finally, effect size estimates
allow for the magnitude of potential clinical impact to considered in policy adoption, as statistical significance may not
be biologically important [5].
https://www.umass.edu/sphhs/person/faculty/andrew-lover
. CC-BY 4.0 International licenseIt is made available under a
author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the(which was not peer-reviewed) The copyright holder for this preprint .https://doi.org/10.1101/2020.03.22.20040949doi: medRxiv preprint
Quantifying Treatment Effects of hydroxychloroquine and azithromycin for COVID-19: a secondary analysis of an
open label non-randomized clinical trial A PREPRINT
2 Data and Analysis
All analyses was performed using Stata 16.1 (College Station, TX, USA). Standard 95% confidence interval were used;
model parsimony was assessed using Akaike and Bayesian information criteria (AIC/BIC). All models were adjusted
for age and sex. Assessing the predictive power of logistic models used Tjur’s Rˆ
2. [6].
2.1 Data source
Data were obtained from [
2
], and the Supplemental Table 1 was digitized using Tabula software, with subsequent
hand-validation. Data for the six patients who were lost-to-follow-up (LTF) were manually entered into a database.
2.2 Primary outcome
The primary outcome as reported by the authors "The primary endpoint was virological clearance at day-6 post-
inclusion." An optimal analysis for this endpoints in a binary regression which avoids many of the potential biases in
logistic models when outcomes are common [7].
2.3 Secondary outcome
The stated "Secondary endpoint was virological clearance overtime during the study period...." and standard Cox
survival time models and Royston-Parmar flexible parameteric models [
8
] were used to capture the time-to-first negative
PCR (with a Ct threshold of >=35). The incorporation of the censored patients was as per standard methods.
3 Results
The sequence of confirmed viremia via PCR is shown in (Fig. 1), and the LFT patients are at the bottom of the figure.
Control
HCQ
LTF
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
Patient ID
D0 1 2 3 4 5 6
Study day
PCR(+) Ct < 35 PCR(-) Ct >=35 Missing data/LTF
Figure 1: Sequence plot of enrolled patients. (N= 42).
The primary outcome was assessed using binary regressions to provide relative risks for clearance of viremia between
the study arms. The main effect of interest (that of all combined HCQ-treated patients versus control), shows a
marginally significant risk ratio of 3.84 (95 % CI 1.02 - 14.42, p= 0.047). Analysis of the separate HCQ and HCQ+AZ
outcome was not possible due to quasi-separation of the model.
2
. CC-BY 4.0 International licenseIt is made available under a
author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the(which was not peer-reviewed) The copyright holder for this preprint .https://doi.org/10.1101/2020.03.22.20040949doi: medRxiv preprint
Quantifying Treatment Effects of hydroxychloroquine and azithromycin for COVID-19: a secondary analysis of an
open label non-randomized clinical trial A PREPRINT
variable RR 95% CI p-value
Study Arm
Control reference - -
HCQ 3.836 1.020 - 14.42 0.047
Age Years 1.009 0.996 - 1.022 0.176
Sex
Male reference - -
Female 0.585 0.335 - 0.963 0.176
Table 1: Risk ratios for clearance of virema, by day six, using binary regression (Primary outcome). (N=30).
To address the limitations of these models, Firth penalized-likelihood model were used, which deal well with separation
and quasi-separation. [9]
variable OR 95% CI p-value
Study Arm
Control reference - -
HCQ 5.216 0.797 - 34.143 0.085
HCQ+AZ 52.280 1.954 - 1,399.058 0.018
Age Years 0.981 0.939 - 1.025 0.399
Sex
Male reference - -
Female 0.971 0.168 - 5.622 0.974
Table 2: Odds ratios for clearance of viremia, by day six, from a Firth penalized likelihood regression (Primary outcome).
(N=30).
To assess the secondary outcome, Kaplan-Meier and Cox models were use initially to compare the time-to-event with
adjustment for covariates. Due severe proportional hazard violations in the HCQ-AZ group, flexible parametric models
were used to estimate effects.
3
. CC-BY 4.0 International licenseIt is made available under a
author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the(which was not peer-reviewed) The copyright holder for this preprint .https://doi.org/10.1101/2020.03.22.20040949doi: medRxiv preprint
Quantifying Treatment Effects of hydroxychloroquine and azithromycin for COVID-19: a secondary analysis of an
open label non-randomized clinical trial A PREPRINT
0.0
0.2
0.4
0.6
0.8
1.0
Proportion with undetectable
viremia via PCR
D0 1 2 3 4 5 6
Days
Control
HCQ-AZ
HCQ
Figure 2: Unadjusted Kaplan-Meier plot of all enrolled patients (N= 42).
variable HR 95% CI p-value
Study Arm
Control reference - -
HCQ 3.026 0.925 - 9.895 0.067
HCQ+AZ 7.073 1.722 - 29.044 0.007
Age Years 0.982 0.955 - 1.010 0.213
Sex
Male reference - -
Female 1.016 0.365 - 2.825 0.976
Table 3: Hazard ratios for time-to-first negative PCR, using flexible parametric models. (N=36). (Secondary Outcome)
4 Discussion and Conclusions
Together these results, especially in consideration of the loss to followup of six patients, do not provide sufficient
evidence to support HCQ monotherapy for the treatment of COVID-19.
This analysis is not without limitations: interpretation of the presented dataset may be incorrect; the analysis models for
the primary and secondary outcome endpoints is inferred from the original authors’ description and power calcualtions
(case-control design); and not all covariates were available in the original data [10].
However, taken together, this analysis does suggest further studies of HCQ-AZ combination therapy should be prioritized
with great haste. The rapid increase in confirmed infections within the last few days suggests that the pandemic is
accelerating, and there are major opportunity costs associated with all choices [
11
]; and rapid science will be critical for
progress [12].
4
. CC-BY 4.0 International licenseIt is made available under a
author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the(which was not peer-reviewed) The copyright holder for this preprint .https://doi.org/10.1101/2020.03.22.20040949doi: medRxiv preprint
Quantifying Treatment Effects of hydroxychloroquine and azithromycin for COVID-19: a secondary analysis of an
open label non-randomized clinical trial A PREPRINT
References
[1]
Hydroxychloroquine and azithromycin as a treatment of COVID-19 – IHU. Library Catalog: www.mediterranee-
infection.com.
[2]
Philippe Gautret, Jean Christophe Lagier, Philippe Parola, Van Thuan Hoang, Line Medded, Morgan Mailhe,
Barbara Doudier, Johan Courjon, Valerie Giordanengo, Vera ESTEVES Vieira, Herve TISSOT Dupont, Stephane
Honore, Philippe Colson, Eric Chabriere, Bernard LA Scola, Jean Marc Rolain, Philippe Brouqui, and Didier
Raoult. Hydroxychloroquine and Azithromycin as a treatment of COVID-19: preliminary results of an open-label
non-randomized clinical trial. medRxiv, page 2020.03.16.20037135, March 2020. Publisher: Cold Spring Harbor
Laboratory Press.
[3]
Ian R White, Nicholas J Horton, James Carpenter, Stuart J Pocock, et al. Strategy for intention to treat analysis in
randomised trials with missing outcome data. Bmj, 342:d40, 2011.
[4] Sandeep K Gupta. Intention-to-treat concept: a review. Perspectives in clinical research, 2(3):109, 2011.
[5]
Shinichi Nakagawa and Innes C Cuthill. Effect size, confidence interval and statistical significance: a practical
guide for biologists. Biological reviews, 82(4):591–605, 2007.
[6]
Tue Tjur. Coefficients of determination in logistic regression models—a new proposal: The coefficient of
discrimination. The American Statistician, 63(4):366–372, 2009.
[7]
Louise-Anne McNutt, Chuntao Wu, Xiaonan Xue, and Jean Paul Hafner. Estimating the relative risk in cohort
studies and clinical trials of common outcomes. American journal of epidemiology, 157(10):940–943, 2003.
[8]
Patrick Royston and Mahesh KB Parmar. Flexible parametric proportional-hazards and proportional-odds models
for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics
in medicine, 21(15):2175–2197, 2002.
[9] Joseph Coveney. Firthlogit: Stata module to calculate bias reduction in logistic regression. 2015.
[10]
Caitlin Rivers, Jean-Paul Chretien, Steven Riley, Julie A Pavlin, Alexandra Woodward, David Brett-Major,
Irina Maljkovic Berry, Lindsay Morton, Richard G Jarman, Matthew Biggerstaff, et al. Using “outbreak science”
to strengthen the use of models during epidemics. Nature Communications, 10(1):1–3, 2019.
[11]
Theo Lorenc and Kathryn Oliver. Adverse effects of public health interventions: a conceptual framework. J
Epidemiol Community Health, 68(3):288–290, 2014.
[12] Michael A Johansson, Nicholas G Reich, Lauren Ancel Meyers, and Marc Lipsitch. Preprints: An underutilized
mechanism to accelerate outbreak science. PLoS medicine, 15(4), 2018.
Revision History
Revision Date Author(s) Description
1.0 17.1.21 AAL created
1.1 17.1.22 AAL Updated survival models; minor edits
1.2 17.1.22 AAL minor edits
5
. CC-BY 4.0 International licenseIt is made available under a
author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the(which was not peer-reviewed) The copyright holder for this preprint .https://doi.org/10.1101/2020.03.22.20040949doi: medRxiv preprint
... Thus it is not confirmatory that whatever works in-vitro can be work well in-vivo, hence there is an urgent need to study clinical trials [38][39][40][41][42]. Further sudden increased use of HCQ as a prophylaxis despite of strong effective evidences may faces detrimental side effects and leads to create shortage of HCQ drug [36][37][38]. ...
... It is mentioned by Ferari in media report Health-News that, use of HCQ +AZT sometimes may become more harmful than disease itself [39]. Moreover, recently, a death is reported by self-medicating use of CQ against COVID-19, hence, selfmedication as a prophylaxis should be avoided which may be harmful or some-times may be lethal [41,42]. Prophylaxis use of HCQ should be done after medical (practitioner) advice only. ...
... Prophylaxis use of HCQ should be done after medical (practitioner) advice only. Thus at present, more confirmatory results/study reports are still awaited for prophylaxis use as well as in-vivo clinical use of HCQ to treat COVID-19 [36][37][38][39][40][41][42]. ...
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... In [11], about [10], it is told "The trial of Gautret and colleagues, with consideration of the effect sizes, and p-values from multiple models, does not provide sufficient evidence to support widescale rollout of HCQ monotherapy for the treatment of COVID-19; larger randomized studies should be considered". ...
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... Similarly, HCQ was predicted to interfere with glycosylation of several proteins involved in the humoral immune response, probably also through inhibition of UDP-N-acetylglucosamine 2-epimerase (Brufsky 2020). Despite the widespread use of HCQ and CQ to treat COVID-19, few controlled clinical trials have been performed so far and thus the potential benefits of these drugs for COVID-19 remain controversial Liu et al. 2020;Zhou et al. 2020c;Gautret et al. 2020a, b;Clementi et al. 2020;Lover 2020;Hulme et al. 2020;Chen et al. 2020c). ...
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Logistic regression yields an adjusted odds ratio that approximates the adjusted relative risk when disease incidence is rare (<10%), while adjusting for potential confounders. For more common outcomes, the odds ratio always overstates the relative risk, sometimes dramatically. The purpose of this paper is to discuss the incorrect application of a proposed method to estimate an adjusted relative risk from an adjusted odds ratio, which has quickly gained popularity in medical and public health research, and to describe alternative statistical methods for estimating an adjusted relative risk when the outcome is common. Hypothetical data are used to illustrate statistical methods with readily accessible computer software.
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The module implements a penalized maximum likelihood estimation method proposed by David Firth (University of Warwick) for reducing bias in generalized linear models. In this module, the method is applied to logistic regression. Others, notably Georg Heinze and his colleagues (Medical University of Vienna), have advocated the method for use under conditions of complete and quasi-complete separation, in which conventional maximum likelihood fails in obtaining finite estimates.