Assessing the efficacy and safety of hydroxychloroquine as outpatient treatment of COVID-19: a randomized controlled trial

6/18 Late treatment study

Schwartz et al., CMAJ Open, doi:10.9778/cmajo.20210069 (Peer Reviewed)
Assessing the efficacy and safety of hydroxychloroquine as outpatient treatment of COVID-19: a randomized controlled trial

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Small early terminated late treatment RCT not showing significant differences. The HCQ group was a median of 7 days from symptom onset at baseline, which may not include the delay delivering the medication. From the 4 HCQ hospitalizations, only one is in the per-protocol analysis, and that patient was hospitalized one day after randomization (authors do not specify if the patient received and took any HCQ before the hospitalization). The trial was terminated early due to the fraudulent Lancet article (wording here is notably different between the submitted and published versions). Per-protocol analysis, the submitted version, and the peer-review comments (two reviewers, only one with substantial feedback) are in the supplementary material.

Schwartz et al., 6/18/2021, Double Blind Randomized Controlled Trial, Canada, North America, peer-reviewed, 20 authors, dosage 800mg day 1, 400mg days 2-5.

risk of ICU admission, 133.3% higher, RR 2.33, p = 1.00, treatment 1 of 111 (0.9%), control 0 of 37 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).

risk of hospitalization, 533.3% higher, RR 6.33, p = 0.57, treatment 4 of 111 (3.6%), control 0 of 37 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).

risk of ICU admission, 141.9% higher, RR 2.42, p = 1.00, treatment 1 of 74 (1.4%), control 0 of 31 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), per-protocol.

risk of hospitalization, 141.9% higher, RR 2.42, p = 1.00, treatment 1 of 74 (1.4%), control 0 of 31 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), per-protocol.

Effect extraction follows pre-specified rules prioritizing more serious outcomes. For an individual study the most serious outcome may have a smaller number of events and lower statistical signficance, however this provides the strongest evidence for the most serious outcomes when combining the results of many trials.

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