adrftools is an R package for estimating the average
dose-response function (ADRF), which characterizes the effect of a
continuous treatment on an outcome. Functionality is included for
performing g-computation on a regression model to estimate the ADRF,
plot the ADRF, and test hypotheses about the ADRF. See
vignette("adrftools") for a full tutorial on using
adrftools.
You can install the current stable version of adrftools from CRAN with:
install.packages("adrftools")You can install the development version of adrftools from GitHub with:
# install.packages("pak")
pak::pak("ngreifer/adrftools")Below we use adrftools to estimate and characterize the effect of blood lead levels on scores on a cognitive test, adjusting for potential confounders using g-computation.
library(adrftools)
data("nhanes3lead")Here, the treatment is logBLL, the outcome is
Math, and the confounders to be adjusted for are
Age, Male, Race,
PIR, Enough_Food, Smoke_in_Home,
Smoke_Pregnant, and NICU. We’ll first fit an
outcome model that adjusts for these confounders.
fit <- lm(Math ~ splines::ns(logBLL, df = 5) *
(Age + Race + Male + PIR + Enough_Food +
Smoke_in_Home + Smoke_Pregnant + NICU),
data = nhanes3lead)Next we’ll use adrftools::adrf() to compute the
ADRF.
adrf_bll <- adrf(fit, treat = "logBLL")
adrf_bll#> An <effect_curve> object
#>
#> - curve type: ADRF
#> - response: Math
#> - treatment: logBLL
#> + range: -0.3567 to 2.4248
#> - inference: unconditionalWe can plot the ADRF using plot():
plot(adrf_bll)
By default, this produces simultaneous 95% confidence bands.
adrf_bll is an <effect_curve> object,
which is a function. We can supply to it values of the treatment to
compute the corresponding value of the ADRF at those treatment
values:
adrf_bll(logBLL = c(0, .5, 1, 1.5, 2))#> ADRF Estimates
#> ────────────────
#> logBLL Estimate
#> 0.0 8.394
#> 0.5 8.305
#> 1.0 8.066
#> 1.5 7.484
#> 2.0 7.078
#> ────────────────We can also produce confidence intervals using summary()
on the above output:
adrf_bll(logBLL = c(0, .5, 1, 1.5, 2)) |>
summary()#> ADRF Estimates
#> ──────────────────────────────────────────
#> logBLL Estimate Std. Error CI Low CI High
#> 0.0 8.394 0.1706 7.959 8.829
#> 0.5 8.305 0.1247 7.987 8.623
#> 1.0 8.066 0.1129 7.778 8.354
#> 1.5 7.484 0.1436 7.117 7.850
#> 2.0 7.078 0.2186 6.520 7.635
#> ──────────────────────────────────────────
#> Inference: unconditional, simultaneous
#> Confidence level: 95% (t* = 2.55, df = 2455)To test an omnibus hypothesis about the ADRF, such as whether it is
flat or linear, we can use summary() on the
<effect_curve> object itself:
# Test for flatness
summary(adrf_bll, hypothesis = "flat")#> Omnibus Curve Test
#> ───────────────────────────────────────────────────────
#> H₀: ADRF is flat for values of logBLL between -0.3567
#> and 2.4248
#>
#> P-value
#> < 0.0001
#> ───────────────────────────────────────────────────────
#> Computed using the Imhof approximation# Test for linearity
summary(adrf_bll, hypothesis = "linear")#> Omnibus Curve Test
#> ───────────────────────────────────────────────────────
#> H₀: ADRF is linear for values of logBLL between
#> -0.3567 and 2.4248
#>
#> P-value
#> 0.3159
#> ───────────────────────────────────────────────────────
#> Computed using the Imhof approximationThe results indicate that the ADRF is not flat, but there isn’t enough evidence to claim it is nonlinear in the range specified. We might project a linear model onto the ADRF to produce a more interpretable summary:
proj <- curve_projection(adrf_bll, model = "linear")Below, we plot the linear projection along with the original ADRF:
plot(adrf_bll, proj = proj)
To examine the coefficient describing the projection, we can use
summary() on the projection:
summary(proj)#> ADRF Projection Coefficients
#> ──────────────────────────────────────────────────────────────
#> Term Estimate Std. Error t P-value CI Low CI High
#> (Intercept) 8.498 0.1329 63.96 < 0.0001 8.237 8.758
#> logBLL -0.642 0.1266 -5.07 < 0.0001 -0.891 -0.394
#> ──────────────────────────────────────────────────────────────
#> Inference: unconditional
#> Confidence level: 95% (t* = 1.961, df = 2455)
#> Null value: 0Based on the above, we might conclude the following:
There is some relationship between
logBLLandMathadjusting for the included potential confounders, based on rejection of the hypothesis that the ADRF is flat forlogBLLvalues between -0.3567 and 2.4248 (p < .0001). There isn’t evidence that the ADRF is nonlinear within this range (p = .3153). The linear projection has a coefficient of -.643 (p < .0001), indicating that the expected potential outcome ofMathdecreases by this amount for each unit increase inlogBLL.
See vignette("adrftools") for more information on how to
use adrftools and the other features available.