Description Usage Arguments Value Note References See Also Examples
The function fits a semiparametric additive hazards model
λ(t|Z=z) = λ_0(t) + β'z.
to two-phase sampling data. The estimating procedures follow Hu (2014).
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formula |
a formula object for the regression model of the form
response ~ predictors. The outcome is a survival object created by |
data |
a data frame. Input dataset. |
R |
a phase II membership indicator. A vector of values of 0 and 1. The subject is selected to phase II if R = 1. |
Pi |
the probability of a subject to be selected to the phase II subsample. |
weights |
weight assigned to each individual, inverse of the selection probability |
ties |
a string. If there are ties in the survival time, when ties = 'break' a small random number is added to the survival time to break the ties. |
robust |
a logical variable. Robust standard errors are provided if robust = TRUE. |
calibration.variables |
a vector of strings of some column names of the data. These are the variables available for every observation. They are used to calibrate the weight assigned to each subject |
seed |
an integer. Seed number used to generate random increment when breaking ties. The default number is 20. |
... |
additional arguments to be passed to the low level regression fitting functions. |
An object of class 'ah.2h' representing the fit.
This function estimates both model-based and robust standard errors. It can be used to analyze case-cohort studies with subsampling among cases. It allows weight calibration with auxiliary information from the full cohort (phase I sample). By this means, more information is used and thus weight calibration potentially could further improve the precision of prediction or our estimation on the regression coefficients.
Jie Hu (2014) A Z-estimation System for Two-phase Sampling with Applications to Additive Hazards Models and Epidemiologic Studies. Dissertation, University of Washington.
predict.ah.2ph
for prediction based on fitted additive
hazards model with two-phase sampling and nwtsco
for the description
of nwtsco dataset.
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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | library(survival)
### fit an additive hazards model to two-phase sampling data without calibration
fit1 <- ah.2ph(Surv(trel,relaps) ~ age + histol, data = nwts2ph, R = in.ph2, Pi = Pi,
robust = FALSE, ties = 'break')
summary(fit1)
### use weight instead of the selection probability Pi in the input
fit1 <- ah.2ph(Surv(trel,relaps) ~ age + histol, data = nwts2ph, R = in.ph2, weights = wts,
robust = FALSE, ties = 'break')
summary(fit1)
### fit an additve hazards model with calibration on age
fit2 <- ah.2ph(Surv(trel,relaps) ~ age + histol, data = nwts2ph, R = in.ph2,
Pi = Pi, robust = FALSE, ties = 'break', calibration.variables = 'age')
summary(fit2)
### calibrate on age square
### note if users create a calibration variable, then
### the new variable should be added to the original data frame
nwts2ph$age2 <- nwts2ph$age^2
fit3 <- ah.2ph(Surv(trel,relaps) ~ age + histol, data = nwts2ph,
R = in.ph2, Pi = Pi, robust = FALSE, ties = 'break', calibratio.variables = 'age2')
summary(fit3)
#############################################################################
## When phase II samples are obtained by finite Sampling
#############################################################################
### calculating the sample size for each straum
### calculate the strata size
strt.size <- table(nwts2ph$strt)
ph2.strt.size <- table(subset(nwts2ph, in.ph2 == 1)$strt)
### fit an additve hazards model with finite stratified sampling
### calculate the sampling fractions
frac <- ph2.strt.size/strt.size
### treating the problem as bernoulli sampling coupled with calibration on strata sizes
### using frac as the sampling probilities
nwts2ph_by_FPSS <- nwts2ph
nwts2ph_by_FPSS$Pi <- NULL
for (i in 1:length(strt.size)){
nwts2ph_by_FPSS$Pi[nwts2ph_by_FPSS$strt ==i] <- frac[i]
}
### create strt indicators, which become our calibration variables
for (i in 1:length(strt.size)){
nwts2ph_by_FPSS$strt_ind <- as.numeric(nwts2ph_by_FPSS$strt ==i)
names(nwts2ph_by_FPSS)[ncol(nwts2ph_by_FPSS)]= paste0('strt', i)
}
### fit an additve hazards model with finate sampling
fit4 <- ah.2ph(Surv(trel,relaps) ~ age + histol,
data = nwts2ph_by_FPSS,
R = in.ph2, Pi = Pi,
robust = FALSE,
ties = 'break',
calibration.variables = c('strt1','strt2','strt3'))
summary(fit4)
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