dat.axfors2021 | R Documentation |
Results from 33 trials examining the effectiveness of hydroxychloroquine or chloroquine in patients with COVID-19.
dat.axfors2021
The data frame contains the following columns:
id | character | registry number |
acronym | character | shortened registry number |
patient_setting | character | patient setting |
blinding_exact | character | study blinding |
high_dose | character | high or low dose of medication |
Published | character | publication status |
hcq_cq | character | medication type (hcq = hydroxychloroquine or cq = chloroquine) |
hcq_arm_event | numeric | number of deaths in the treatment group |
hcq_arm_total | numeric | number of patients in the treatment group |
control_arm_event | numeric | number of deaths in the control group |
control_arm_total | numeric | number of patients in the control group |
Control | character | control group type (Standard of Care or Placebo) |
The dataset includes the results from 33 published and unpublished randomized clinical trials that examined the effectiveness of hydroxychloroquine or chloroquine in patients with COVID-19. The results given here are focused on the total mortality in the treatment versus control groups.
medicine, covid-19, odds ratios
W. Kyle Hamilton whamilton@ucmerced.edu https://kylehamilton.com
Axfors, C., Schmitt, A., Janiaud, P., van ’t Hooft, J., Moher, D., Goodman, S., … Hemkens, L. G. (2021, March 9). Hydroxychloroquine and chloroquine for survival in COVID-19: An international collaborative meta-analysis of randomized trials. https://doi.org/10.17605/OSF.IO/QESV4
Axfors, C., Schmitt, A. M., Janiaud, P., van’t Hooft, J., Abd-Elsalam, S., Abdo, E. F., Abella, B. S., Akram, J., Amaravadi, R. K., Angus, D. C., Arabi, Y. M., Azhar, S., Baden, L. R., Baker, A. W., Belkhir, L., Benfield, T., Berrevoets, M. A. H., Chen, C.-P., Chen, T.-C., … Hemkens, L. G. (2021). Mortality outcomes with hydroxychloroquine and chloroquine in COVID-19 from an international collaborative meta-analysis of randomized trials. Nature Communications, 12(1), 2349. https://doi.org/10.1038/s41467-021-22446-z
# copy data into 'dat' and examine data dat <- dat.axfors2021 dat ## Not run: # load metafor package library(metafor) # calculate log odds ratios and corresponding sampling variances dat <- escalc(measure="OR", ai=hcq_arm_event, n1i=hcq_arm_total, ci=control_arm_event, n2i=control_arm_total, data=dat) # meta-analysis Hydroxychloroquine res_hcq <- rma(yi, vi, subset=(hcq_cq=="hcq"), slab = id, data=dat) print(res_hcq, digits=2) # meta-analysis Chloroquine res_cq <- rma(yi, vi, subset=(hcq_cq=="cq"), slab = id, data=dat) print(res_cq, digits=2) ## End(Not run)
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