THIS IS STILL A WORK IN PROCESS
The
pifpafpackage corresponds to an update on the previous homonimous package developed at INSP.
The pifpaf package allows users to estimate the population
attributable fraction, pif, and potential impact fraction, paf,
from cross-sectional studies that come from a survey design using the
bootstrap algorithms of @heeringa2015attributable.
There are two main scenarios when to use this package:
-
When using individual-level data from a survey to determine exposure and an independent source to determine the relative risk associated to that exposure (say a metanalysis). In this case one would use
pif()orpaf(). -
When using aggregated data from a survey to determine exposure and an independent source to determine the relative risk associated to that exposure (say a metanalysis). In this case one would use
pif_approximate()orpaf_approximate().
You can install the development version of pifpaf from
GitHub with:
#install.packages("remotes")
remotes::install_github("RodrigoZepeda/pifpaf", dependencies = TRUE)The main purpose of the package is to calculate potential impact fractions and population attributable fractions for a specific case: when the exposure information comes from cross-sectional studies (in particular, surveys) and the relative risk information from the literature (e.g. metanalysis).
In our package we assume the user has access to the data either as individual-level exposure (e.g. the user has access to the survey) or the user has access to aggregated data on the exposure (e.g. the user has the population-mean exposure and its confidence interval). The following table shows which function to use depending on the case:
| Exposure data | Population Attributable Fraction | Potential Impact Fraction |
|---|---|---|
| Individual-level | paf |
pif |
| Aggregated | paf_approximate |
pif_approximate |
Note If the user has available both individual-level and aggregated data they should prefer the individual-level information as it captures better the sample’s variability. The population attributable fraction and potential impact fractions estimated from aggregated data are only approximations and are biased estimates.
In the case of individual-level data the Population Attributable
Fraction (PAF) and the Potential Impact Fraction (PIF) can be estimated
directly via the pif() and paf() functions. For the purpose of this
example, we will use data from the Mexican National Health and Nutrition
Survey of 2018 (ENSANUT 2018):
data(ensanut)The data contains information on the age, sex, weight (grams), hypertension status, systolic blood pressure and change in sodium from a policy intervention.
head(ensanut)
#> age sex weight strata hypertension delta_na_phase_1
#> 1 28 Female 32133.58 222 FALSE 5.899257
#> 2 24 Female 75955.15 222 FALSE 19.112181
#> 3 45 Female 16075.99 222 FALSE 49.546444
#> 4 39 Male 83462.51 222 FALSE 6.273072
#> 5 41 Male 84734.87 223 FALSE 0.000000
#> 6 61 Male 118740.93 223 FALSE 0.000000
#> systolic_blood_pressure age_group
#> 1 118.0 [25,30)
#> 2 92.0 [20,25)
#> 3 93.0 [45,50)
#> 4 116.0 [35,40)
#> 5 110.5 [40,45)
#> 6 122.5 [60,65)-
graphPAFAllows only for estimation of population attributable fractions. Can use data from cross sectional, case-control, and cohort studies. It can also estimate multiple risk factors at the same time. Doesn’t allow for estimation of potential impact fractions. -
AFby @dahlqwist2016model allows the user to utilize cross sectional, case-control, and cohort studies but only for binary exposures. -
causalPAFallows for estimation of population attributable fractions given a causal diagram. -
pifpafan homonymous package using the same methods for the approximate. However, it doesn’t allow for survey data or for changes in the relative risk function.
WORK IN PROGRESS