| Title: | Attributable Burden of Disease |
| Version: | 2021.1.2 |
| Description: | Provides functions for estimating the attributable burden of disease due to risk factors. The posterior simulation is performed using arm::sim as described in Gelman, Hill (2012) <doi:10.1017/CBO9780511790942> and the attributable burden method is based on Nielsen, Krause, Molbak <doi:10.1111/irv.12564>. |
| Depends: | R (≥ 3.5.0) |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | data.table, magrittr, glue, pbs, dlnm, lubridate, mvmeta, tsModel, stats, lme4, arm, tibble, stringr, ggplot2, utils, progress |
| Suggests: | testthat, knitr, rmarkdown |
| RoxygenNote: | 7.1.1 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2021-03-29 04:00:29 UTC; rstudio |
| Author: | Richard Aubrey White
 [aut, cre],
Aurora Hofman
[aut],
Folkehelseinstituttet [cph] |
| Maintainer: | Richard Aubrey White <hello@rwhite.no> |
| Repository: | CRAN |
| Date/Publication: | 2021-03-30 09:10:02 UTC |
Fake data for mortality in Norway
Description
Fake data for mortality in Norway
Usage
data_fake_county
Format
- location_code
Location code of the Norwegian municipalities
- week
Week
- season
Season used for influenza like illnesses
- yrwk
Year and week
- x
Number of weeks from the start of the season
- pop
Population size
- pr100_ili
Per hundred ILI, percentage of consultations diagnosed as influenza like illnesses
- pr100_ili_lag_1
pr100_ili_lag_1
- temperature
temperature
- temperature_high
temperature_high
- deaths
deaths
Fake data for mortality in Norway nationally
Description
Fake data for mortality in Norway nationally
Usage
data_fake_nation
Format
- location_code
Location code
- week
Week
- season
Season used for influenza like illnesses
- yrwk
Year and week
- x
Number of weeks from the start of the season
- pop
Population size
- pr100_ili
Per hundred ILI, percentage of consultations diagnosed as influenza like illnesses
- pr100_ili_lag_1
pr100_ili_lag_1
- temperature
temperature
- temperature_high
temperature_high
- deaths
deaths
Estimates simulations of expected responses
Description
For each exposure the dataset is copied and the original value replaced by the reference value. Then the sim function is used to generate 500 simulations of expected responses for each row. Finally the dataset is transformed to obtain expected response for original and reference values of the given exposures for each original row of the dataset.
Usage
est_attrib(fit, data, exposures, n_sim = 500)
Arguments
fit |
A model fit constructed by fit_attrib |
data |
The observed data |
exposures |
The exposures that will get reference expected mortalities |
n_sim |
Number of simulations For more details see the help vignette:
|
Details
The burden method is based on Nielsen, Krause, Molbak <doi:10.1111/irv.12564>.
For more details see the help vignette:
vignette("intro", package="attrib")
Value
Dataset with expected responses for all simulations including expected responses given the exposure reference values
Examples
response <- "deaths"
fixef <- "pr100_ili_lag_1 + sin(2 * pi * (week - 1) / 52) + cos(2 * pi * (week - 1) / 52)"
ranef <- " (pr100_ili_lag_1| season)"
offset <- "log(pop)"
data <- attrib::data_fake_nation
fit <- fit_attrib(data = data, response = response, fixef = fixef, ranef = ranef, offset = offset)
exposures <- c(pr100_ili_lag_1 = 0)
n_sim <- 5
new_data <- est_attrib(fit, data, exposures, n_sim)
new_data[]
Data fit
Description
Data fit using glmer from lme4 with family poisson to fit the dataset with the given formula.
Usage
fit_attrib(data, response, fixef, ranef, offset = NULL)
Arguments
data |
The observed data to be fitted. |
response |
The response |
fixef |
The fixed effects |
ranef |
The random effects |
offset |
The offsets. |
Value
The model fit of the data with additional attributes offset, response and fit_fix. Offset and response are the same as in the input and fit_fix is the linear model of the fix effects.
For more details see the help vignette:
vignette("intro", package="attrib")
Examples
response <- "deaths"
fixef <- "pr100_ili_lag_1 + sin(2 * pi * (week - 1) / 52) + cos(2 * pi * (week - 1) / 52)"
ranef <- " (pr100_ili_lag_1| season)"
offset <- "log(pop)"
data <- attrib::data_fake_nation
fit_attrib(data = data, response = response, fixef = fixef, ranef = ranef, offset = offset)
Generates simulations of expected mortality by simulating the model coefficients.
Description
With the given fit from fit_attrib the function sim, from package arm as described in Gelman, Hill (2012) <doi:10.1017/CBO9780511790942>, is used to generate 500 simulations of all the coefficients, from there respective posterior distributions. This is then used to compute the expected response for all simulations and rows in the input dataset.
Usage
sim(fit, data, n_sim)
Arguments
fit |
A model fit created by fit_attrib |
data |
The data with either observed values or reference values. |
n_sim |
Number of simulations |
Details
vignette("intro", package="attrib")
Value
A dataset with 500 simulations of the expected response for each row in the original dataset.
Examples
response <- "deaths"
fixef <- "pr100_ili_lag_1 + sin(2 * pi * (week - 1) / 52) + cos(2 * pi * (week - 1) / 52)"
ranef <- " (pr100_ili_lag_1| season)"
offset <- "log(pop)"
data <- attrib::data_fake_nation
fit <- fit_attrib(data = data, response = response, fixef = fixef, ranef = ranef, offset = offset)
n_sim <- 5
sim(fit, data, n_sim)