Description Usage Arguments Value References See Also Examples
Estimates P\{Y(z)=1S(1)=s_1\}, z=0,1, on a grid of s_1 values following the estimation method of Juraska, Huang, and Gilbert (2018), where Z is the
treatment group indicator (Z=1, treatment; Z=0, placebo), S(z) is a continuous or ordered categorical univariate biomarker under assignment to Z=z
measured at fixed time t_0 after randomization, and Y is a binary clinical endpoint (Y=1, disease; Y=0, no disease) measured after t_0. The
estimator employs the generalized product kernel density/probability estimation method of Hall, Racine, and Li (2004) implemented in the np
package. The risks
P\{Y(z)=1S(z)=s_1,X=x\}, z=0,1, where X is a vector of discrete baseline covariates, are estimated by fitting inverse probabilityweighted logistic regression
models using the osDesign
package.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 
formula 
a formula object with the binary clinical endpoint on the left of the 
bsm 
a character string specifying the variable name in 
tx 
a character string specifying the variable name in 
data 
a data frame with one row per randomized participant endpointfree at t_0 that contains at least the variables specified in 
pstype 
a character string specifying whether the biomarker response shall be treated as a 
bsmtype 
a character string specifying whether the baseline surrogate measure shall be treated as a 
bwtype 
a character string specifying the bandwidth type for continuous variables in the kernel density estimation. The options are 
hinge 
a logical value ( 
weights 
either a numeric vector of weights or a character string specifying the variable name in 
psGrid 
a numeric vector of S(1) values at which the conditional clinical endpoint risk in each study group is estimated. If 
saveFile 
a character string specifying the name of an 
saveDir 
a character string specifying a path for the output directory. If 
If saveFile
and saveDir
are both specified, the output list (named oList
) is saved as an .RData
file; otherwise it is returned only.
The output object (of class riskCurve
) is a list with the following components:
psGrid
: a numeric vector of S(1) values at which the conditional clinical endpoint risk is estimated in the components plaRiskCurve
and
txRiskCurve
plaRiskCurve
: a numeric vector of estimates of P\{Y(0)=1S(1)=s_1\} for s_1 in psGrid
txRiskCurve
: a numeric vector of estimates of P\{Y(1)=1S(1)=s_1\} for s_1 in psGrid
fOptBandwidths
: a conbandwidth
object returned by the call of the function npcdensbw
containing the optimal bandwidths, selected by likelihood
crossvalidation, in the kernel estimation of the conditional density of S(1) given the baseline surrogate measure and any other specified baseline covariates
gOptBandwidths
: a conbandwidth
object returned by the call of the function npcdensbw
or npudensbw
containing the optimal bandwidths,
selected by likelihood crossvalidation, in the kernel estimation of the conditional density of S(0) given any specified baseline covariates or the marginal density
of S(0) if no baseline covariates are specified in formula
cpointP
: if hinge=TRUE
, the estimate of the hinge point in the placebo group
cpointT
: if hinge=TRUE
, the estimate of the hinge point in the treatment group
Fong, Y., Huang, Y., Gilbert, P. B., and Permar, S. R. (2017), chngpt: threshold regression model estimation and inference, BMC Bioinformatics, 18.
Hall, P., Racine, J., and Li, Q. (2004), Crossvalidation and the estimation of conditional probability densities, JASA 99(468), 10151026.
Juraska, M., Huang, Y., and Gilbert, P. B. (2020), Inference on treatment effect modification by biomarker response in a threephase sampling design, Biostatistics, 21(3): 545560, https://doi.org/10.1093/biostatistics/kxy074.
bootRiskCurve
, summary.riskCurve
and plotMCEPcurve
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  n < 500
Z < rep(0:1, each=n/2)
S < MASS::mvrnorm(n, mu=c(2,2,3), Sigma=matrix(c(1,0.9,0.7,0.9,1,0.7,0.7,0.7,1), nrow=3))
p < pnorm(drop(cbind(1,Z,(1Z)*S[,2],Z*S[,3]) %*% c(1.2,0.2,0.02,0.2)))
Y < sapply(p, function(risk){ rbinom(1,1,risk) })
X < rbinom(n,1,0.5)
# delete S(1) in placebo recipients
S[Z==0,3] < NA
# delete S(0) in treatment recipients
S[Z==1,2] < NA
# generate the indicator of being sampled into the phase 2 subset
phase2 < rbinom(n,1,0.4)
# delete Sb, S(0) and S(1) in controls not included in the phase 2 subset
S[Y==0 & phase2==0,] < c(NA,NA,NA)
# delete Sb in cases not included in the phase 2 subset
S[Y==1 & phase2==0,1] < NA
data < data.frame(X,Z,S[,1],ifelse(Z==0,S[,2],S[,3]),Y)
colnames(data) < c("X","Z","Sb","S","Y")
qS < quantile(data$S, probs=c(0.05,0.95), na.rm=TRUE)
grid < seq(qS[1], qS[2], length.out=3)
out < riskCurve(formula=Y ~ S + factor(X), bsm="Sb", tx="Z", data=data, psGrid=grid)
# alternatively, to save the .RData output file (no '<' needed):
riskCurve(formula=Y ~ S + factor(X), bsm="Sb", tx="Z", data=data, saveFile="out.RData",
saveDir="./")

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