Description Usage Arguments Details Author(s) References Examples
Nonparametric estimation of an isotonic covariate effect for proportional hazards model.
1 |
formula |
a formula object: a response ~ a univariate covariate. The response must be survival outcome unsing the Surv function in the survival package. |
trt |
Treatment group. It must be coded by 0 or 1. This argument is optional. |
data |
data.frame or list that includes variables named in the formula argument. |
shape |
direction of the covariate effect on the hazard function, "increasing" or "decreasing". |
K |
an anchor constraint is imposed at K (default is 0). |
maxdec |
maximum number of decisimal for output (default is 2). |
maxiter |
maximum number of iteration (default is 10^4). |
eps |
stopping convergence criteria (default is 10^-3). |
The isoph function allows to analyze isotonic proportional hazards model, defined as
λ(t|z,trt)=λ0(t)exp(ψ(z)+β trt),
where λ0 is a baseline hazard function, ψ is an isotonic function, z is a univariate variable, β is a regression parameter and trt is a binary treatment group variable. One point has to be fixed with ψ(K)=0 , where K is an anchor point. A direction of ψ is defined as monotone increasing or monotone decreasing in Z prior to data analysis. Pseudo iterative convex minorant algorithm is used.
Yunro Chung [cre], Anastasia Ivanova, Michael G. Hudgens and Jason P. Fine
Yunro Chung, Anastasia Ivanova, Michael M. Hudgens, Jason P. Fine, Partial likelihood estimation of isotonic proportional hazards models, Biometrika. In print.
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# 1. time-independent covariate with monotone increasing effect
###
# 1.1. create a test data set 1
test1=list(
time= c(2, 5, 1, 7, 9, 5, 3, 6, 8, 9, 7, 4, 5, 2, 8),
status=c(0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1),
z= c(2, 1, 1, 3, 5, 6, 7, 9, 3, 0, 2, 7, 3, 9, 4)
)
# 1.2. Fit isotonic proportional hazards model
res1 = isoph(Surv(time, status)~z, data=test1, shape="increasing")
# 1.3. print result
print(res1)
plot(res1)
###
# 2. time-independent covariate with monotone increasing effect and treatment group
###
# 2.1. create a test data set 1
test2=list(
time= c(2, 5, 1, 7, 9, 5, 3, 6, 8, 9, 7, 4, 5, 2, 8),
status=c(0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1),
z= c(2, 1, 1, 3, 5, 6, 7, 9, 3, 0, 2, 7, 3, 9, 4),
trt= c(1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0)
)
# 2.2. Fit isotonic proportional hazards model
res2 = isoph(Surv(time, status)~z, trt=trt, data=test2, shape="increasing")
# 2.3. print result
print(res2)
plot(res2)
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