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The goal of {epitabulate} is to facilitate producing tables for descriptive epidemiological analysis. This is a product of the R4EPIs project; learn more at https://r4epi.github.io/sitrep/
You can install {epitabulate} from CRAN:
install.packages("epitabulate")You can also install the in-development version from GitHub using the {remotes} package (but there’s no guarantee that it will be stable):
# install.packages("remotes")
remotes::install_github("R4EPI/epitabulate") Here is an example of the tables produced by {epitabulate} from a randomly generated dataset.
library(dplyr)
#>
#> Attache Paket: 'dplyr'
#> Die folgenden Objekte sind maskiert von 'package:stats':
#>
#> filter, lag
#> Die folgenden Objekte sind maskiert von 'package:base':
#>
#> intersect, setdiff, setequal, union
library(epitabulate)
# define how many people to simulate
n <- 1000
# generate a fake dataset
linelist <- data.frame(
sex = sample(c("male", "female"), n, replace = TRUE),
age_group = sample(c("<5", "5-14", "15-29", "30-44", "45-59", "60+"), n, replace = TRUE,
prob = c(0.1, 0.15, 0.25, 0.2, 0.15, 0.15)),
fever = sample(c("yes", "no"), n, replace = TRUE, prob = c(0.3, 0.7)),
death = sample(c("yes", "no"), n, replace = TRUE, prob = c(0.05, 0.95)),
observation_time = round(runif(n, min = 1, max = 365), 1) # days under observation
)
# turn yes/no variables into logical (TRUE/FALSE) variables
linelist <- linelist |>
mutate(
fever = grepl("yes", fever),
death = grepl("yes", death)
)There are three functions that will provide quick statistics for
different rates based on binomial estimates of proportions from
binom::binom.wilson()
attack_rate()case_fatality_rate()mortality_rate()attack_rate(10, 50)
#> cases population ar lower upper
#> 1 10 50 20 11.24375 33.03711
case_fatality_rate(2, 50)
#> deaths population cfr lower upper
#> 1 2 50 4 1.103888 13.46009
mortality_rate(40, 50000)
#> deaths population mortality per 10 000 lower upper
#> 1 40 50000 8 5.87591 10.89109In addition, it’s possible to rapidly calculate Case fatality rate from a linelist, stratified by different groups (e.g. gender):
case_fatality_rate_df(linelist,
deaths = death,
group = sex,
add_total = TRUE,
mergeCI = TRUE)
#> # A tibble: 4 × 5
#> sex deaths population cfr ci
#> <fct> <int> <int> <dbl> <chr>
#> 1 female 30 508 5.91 (4.17-8.31)
#> 2 male 28 492 5.69 (3.97-8.10)
#> 3 (Missing) 0 0 NaN (NaN-NaN)
#> 4 Total 58 1000 5.8 (4.51-7.42)It is also possible to add these proportions on to {gtsummary} tabulations
cfr <- linelist |>
select(death) |>
gtsummary::tbl_summary(
statistic = everything() ~ "{N}",
label = death ~ "All participants"
) %>%
add_cfr(deaths_var = "death")
#> There was a warning for variable "death"
#> ! cfr by strata is not currently available, ignoring `by` argument#> # A tibble: 1 × 5
#> `**Characteristic**` `**N = 1,000**` Deaths `CFR (%)` `95%CI`
#> <chr> <chr> <chr> <chr> <chr>
#> 1 All participants 1,000 58 5.80 (4.51-7.42)It is possible to add counts to {gtsummary}
tbl_uvregression
## Odds ratios
or <- gtsummary::tbl_uvregression(linelist,
method = glm,
y = death,
include = fever,
method.args = list(family = binomial),
exponentiate = TRUE,
hide_n = TRUE) |>
add_crosstabs(wide = TRUE)#> tbl_id1 variable var_label var_type row_type header_row N_obs N_event N
#> 1 1 fever fever dichotomous label FALSE 1000 58 1000
#> coefficients_type coefficients_label label term var_class var_nlevels
#> 1 logistic OR fever feverTRUE logical 2
#> contrasts contrasts_type n_obs n_event_FALSE n_nonevent_FALSE
#> 1 contr.treatment treatment 288 11 277
#> n_event_TRUE n_nonevent_TRUE estimate std.error statistic nevent ci
#> 1 47 665 0.5618711 0.3424902 -1.68321 58 0.27, 1.06
#> conf.low conf.high p.value n_event_NA n_nonevent_NA reference_row
#> 1 0.2732414 1.059923 0.09233451 NA NA FALSE## Risk ratios
rr <- gtsummary::tbl_uvregression(linelist,
method = MASS::glm.nb,
y = death,
include = fever,
exponentiate = TRUE,
hide_n = TRUE) |>
add_crosstabs(wide = TRUE)
#> There was a warning constructing the model for variable "fever". See message
#> below.
#> ! Iterationsgrenze erreicht and Iterationsgrenze erreicht#> tbl_id1 variable var_label var_type row_type header_row N_obs N_event N
#> 1 1 fever fever dichotomous label FALSE 1000 58 1000
#> coefficients_type coefficients_label label term var_class var_nlevels
#> 1 poisson RR fever feverTRUE logical 2
#> contrasts contrasts_type exposure n_obs_FALSE n_event_FALSE n_obs_TRUE
#> 1 contr.treatment treatment 288 288 11 712
#> n_event_TRUE estimate std.error statistic nevent ci conf.low
#> 1 47 0.5786052 0.3349472 -1.633496 58 0.28, 1.07 0.284962
#> conf.high p.value n_obs_NA n_event_NA reference_row
#> 1 1.073527 0.1023647 NA NA FALSE
## Incidence rate ratios
irr <- gtsummary::tbl_uvregression(linelist,
method = glm,
y = death,
include = fever,
method.args = list(family = poisson,
offset = log(observation_time)),
exponentiate = TRUE,
hide_n = TRUE) |>
add_crosstabs(wide = TRUE)#> tbl_id1 variable var_label var_type row_type header_row N_obs N_event N
#> 1 1 fever fever dichotomous label FALSE 1000 58 1000
#> coefficients_type coefficients_label label term var_class var_nlevels
#> 1 poisson IRR fever feverTRUE logical 2
#> contrasts contrasts_type n_obs exposure_FALSE n_event_FALSE
#> 1 contr.treatment treatment 288 51881.9 11
#> exposure_TRUE n_event_TRUE estimate std.error statistic nevent ci
#> 1 131182.9 47 0.5917744 0.3349395 -1.566342 58 0.29, 1.10
#> conf.low conf.high p.value exposure_NA n_event_NA reference_row
#> 1 0.2914501 1.09795 0.1172686 NA NA FALSEIt is also possible to run stratified analysis to produce Cochran Mantel-haenszel estimates.
cmh <- tbl_cmh(data = linelist,
case = death,
exposure = fever,
strata = age_group,
measure = "OR") |>
add_crosstabs()#> # A tibble: 21 × 10
#> `**Strata**` `**Characteristic**` `**Case (n)**` `**Control (n)**` `**OR**`
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Crude fever <NA> <NA> <NA>
#> 2 <NA> FALSE 47 665 <NA>
#> 3 <NA> TRUE 11 277 0.56
#> 4 <5 fever <NA> <NA> <NA>
#> 5 <NA> FALSE 1 76 <NA>
#> 6 <NA> TRUE 1 19 4.00
#> 7 15-29 fever <NA> <NA> <NA>
#> 8 <NA> FALSE 14 152 <NA>
#> 9 <NA> TRUE 2 60 0.36
#> 10 30-44 fever <NA> <NA> <NA>
#> # ℹ 11 more rows
#> # ℹ 5 more variables: `**95% CI**` <chr>, `**p-value**` <chr>,
#> # `**CMH estimate**` <chr>, `**95% CI**` <chr>, `**p-value**` <chr>These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.