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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.