dat.hannum2020 | R Documentation |
Results from 35 studies measuring olfactory loss in COVID-19 patients using either objective or subjective measures.
dat.hannum2020
The data frame contains the following columns:
authorName | character | (first) author of study |
DOI | character | article DOI number |
ni | numeric | number of Covid-19 positive patients in the study |
xi | numeric | number of Covid-19 positive patients in the study with olfactory loss |
percentOlfactoryLoss | numeric | percent of the sample with olfactory loss |
objectivity | character | objective or subjective measure used |
measured | character | outcome measure |
testType | character | type of test used |
country | character | country where patients were treated |
patientType | character | type of patient information and location where being treated |
One of the symptoms of COVID-19 infection is olfactory loss (loss of smell) either recently acquired anosmia (complete loss of smell) or hyposmia (partial loss of smell). One challenge to reaching this symptom is the wide range of reported prevalence for this symptom ranging from 5 percent to 98 percent. In this dataset studies were grouped into one of two groups based on the type of method used to measure smell loss (either subjective measures, such as self-reported smell loss, or objective measures using rated stimuli).
medicine, covid-19, proportions
W. Kyle Hamilton whamilton@ucmerced.edu https://kylehamilton.com
Ramirez VA , Hannum ME, Lipson SJ, Herriman RD, Toskala AK, Lin C, Joseph PV, Reed DR. 2020. COVID-19 Smell Loss Prevalence Tracker. Available from: https://vicente-ramirez.shinyapps.io/COVID19_Olfactory_Dashboard/
and https://github.com/vramirez4/OlfactoryLoss
(accessed August 11, 2021)
Hannum, M. E., Ramirez, V. A., Lipson, S. J., Herriman, R. D., Toskala, A. K., Lin, C., Joseph, P. V., & Reed, D. R. (2020). Objective sensory testing methods reveal a higher prevalence of olfactory loss in COVID-19 positive patients compared to subjective methods: A systematic review and meta-analysis. Chemical Senses, 45(9), 865–874. https://doi.org/10.1093/chemse/bjaa064
# copy data into 'dat' and examine data dat <- dat.hannum2020 dat ## Not run: # load metafor package library(metafor) # compute effect size dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat) # split data into objective and subjective datasets dat_split <- split(dat, dat$objectivity) dat_objective <- dat_split[["Objective"]] dat_subjective <- dat_split[["Subjective"]] # random-effects model all studies res_all <- rma(yi, vi, data=dat) print(res_all, digits=2) # random-effects model objective res_objective <- rma(yi, vi, data=dat_objective) print(res_objective, digits=2) # random-effects model subjective res_subjective <- rma(yi, vi, data=dat_subjective) print(res_subjective, digits=2) ## End(Not run)
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