Yman2016: Antibody acquisition models

In ben-arnold/tmleAb: Targeted maximum likelihood estimation for antibody measurements

Description

Fit a parametric model of age-dependent antibody acquisition (and loss)

Usage

Yman2016(logAb, Age, type = "constant", print = TRUE)

Arguments

logAb	Log antibody level (numeric)
Age	Age of the individual at the time of measurement (numeric)
type	Specify type of model to fit – either constant, changepoint, or linear. At this time only constant is supported and this argument is currently ignored.
print	Logical. Print parameter estimates?

Details

Yman2016 estimates rates of antibody acquisision (alpha) and loss (r) using maximum likelihood under a parametric model that assumes constant rates of acquisition and loss, and that antibody levels follow a log-normal distribution. The function also estimates nuissance parameter (sigma, the standard deviation of the log-normal distribution). Future updates may include the changepoint and linear decrease models from the original paper

Value

Returns an object of class Yman2016 that is just the maximum likelihood fit results passed from optim.

References

Yman V, White MT, Rono J, Arcà B, Osier FH, Troye-Blomberg M, et al. Antibody acquisition models: A new tool for serological surveillance of malaria transmission intensity. Sci Rep. ; 2016;6: 19472. (http://www.nature.com/articles/srep19472)

Examples

## Not run: 
# simulate data assuming the Yman 2016 model is the truth
set.seed(1234)
age <- runif(1000,min=1,max=20)
alpha <- 0.5
r <- 0.07
sigma <- log(1.7)
abtrue <- (alpha/r)*(1-exp(-r*age))
abobs <- exp( log(abtrue) +rnorm(length(age),mean=0,sd=sigma) )

# Fit the model and obtain predictions
X <- data.frame(age=age)
Y <- log(abobs)
yman2016fit <- Yman2016(logAb=Y,Age=X$Age)
pY <- predict(yman2016fit$fit,newdata=X)

# compare against truth
cbind(c(alpha,r,sigma),yman2016fit$fit$object$par)
# figure (not run)
# plot(age,log(abobs),cex=0.25,col="gray40",pch=16)
# lines(age[order(age)],log(abtrue[order(age)]),lwd=3,lty=2)
# lines(age[order(age)],pY[order(age)],lwd=1)

# super learner fit
# (note that it selects Yman 2016 under
# conditions where it models the truth)
SL.library <- c("SL.glm","SL.loess","SL.Yman2016")
SLfit <- SuperLearner(Y=log(abobs),X=data.frame(age=age),SL.library=SL.library)
SLfit

# The Yman 2016 model can only use age, not additional covariates,
# so it will throw a warning if X includes more than 1 covariate
# and it will assume that the first column of X is age
SLfit2 <- SuperLearner(Y=log(abobs),X=data.frame(age=age,age2=age^2),SL.library=SL.library)
SLfit2

## End(Not run)

ben-arnold/tmleAb documentation built on May 12, 2019, 10:55 a.m.