maple.aft {mable} | R Documentation |
Mable fit of AFT model with given regression coefficients
Description
Maximum approximate profile likelihood estimation of Bernstein polynomial model in accelerated failure time based on interal censored event time data with given regression coefficients which are efficient estimates provided by other semiparametric methods.
Usage
maple.aft(
formula,
data,
M,
g,
tau = NULL,
p = NULL,
x0 = NULL,
controls = mable.ctrl(),
progress = TRUE
)
Arguments
formula |
regression formula. Response must be |
data |
a dataset |
M |
a positive integer or a vector |
g |
the given |
tau |
the right endpoint of the support or truncation interval |
p |
an initial coefficients of Bernstein polynomial of degree |
x0 |
a working baseline covariate |
controls |
Object of class |
progress |
if |
Details
Consider the accelerated failure time model with covariate for interval-censored failure time data:
S(t|x) = S(t \exp(\gamma^T(x-x_0))|x_0)
, where x_0
is a baseline covariate.
Let f(t|x)
and F(t|x) = 1-S(t|x)
be the density and cumulative distribution
functions of the event time given X = x
, respectively.
Then f(t|x_0)
on a support or truncation interval [0, \tau]
can be approximated by
f_m(t|x_0; p) = \tau^{-1}\sum_{i=0}^m p_i\beta_{mi}(t/\tau)
,
where p_i \ge 0
, i = 0, \ldots, m
, \sum_{i=0}^mp_i=1
,
\beta_{mi}(u)
is the beta denity with shapes i+1
and m-i+1
, and
\tau
is larger than the largest observed time, either uncensored time, or right endpoint of interval/left censored,
or left endpoint of right censored time. We can approximate S(t|x_0)
on [0, \tau]
by
S_m(t|x_0; p) = \sum_{i=0}^{m} p_i \bar B_{mi}(t/\tau)
, where \bar B_{mi}(u)
is
the beta survival function with shapes i+1
and m-i+1
.
Response variable should be of the form cbind(l, u)
, where (l,u)
is the interval
containing the event time. Data is uncensored if l = u
, right censored
if u = Inf
or u = NA
, and left censored data if l = 0
.
The truncation time tau
and the baseline x0
should be chosen so that
S(t|x) = S(t \exp(\gamma^T(x-x_0))|x_0)
on [\tau, \infty)
is negligible for
all the observed x
.
The search for optimal degree m
stops if either m1
is reached or the test
for change-point results in a p-value pval
smaller than sig.level
.
Value
A list with components
-
m
the selected optimal degreem
-
p
the estimate ofp=(p_0, \dots, p_m)
, the coefficients of Bernstein polynomial of degreem
-
coefficients
the given regression coefficients of the AFT model -
SE
the standard errors of the estimated regression coefficients -
z
the z-scores of the estimated regression coefficients -
mloglik
the maximum log-likelihood at an optimal degreem
-
tau.n
maximum observed time\tau_n
-
tau
right endpoint of trucation interval[0, \tau)
-
x0
the working baseline covariates -
egx0
the value ofe^{\gamma^T x_0}
-
convergence
an integer code, 1 indicates either the EM-like iteration for finding maximum likelihood reached the maximum iteration for at least onem
or the search of an optimal degree using change-point method reached the maximum candidate degree, 2 indicates both occured, and 0 indicates a successful completion. -
delta
the finaldelta
ifm0 = m1
or the finalpval
of the change-point for searching the optimal degreem
;
and, if m0<m1
,
-
M
the vector(m0, m1)
, wherem1
is the last candidate when the search stoped -
lk
log-likelihoods evaluated atm \in \{m_0, \ldots, m_1\}
-
lr
likelihood ratios for change-points evaluated atm \in \{m_0+1, \ldots, m_1\}
-
pval
the p-values of the change-point tests for choosing optimal model degree -
chpts
the change-points chosen with the given candidate model degrees
Author(s)
Zhong Guan <zguan@iusb.edu>
References
Guan, Z. (2019) Maximum Approximate Likelihood Estimation in Accelerated Failure Time Model for Interval-Censored Data, arXiv:1911.07087.
See Also
Examples
## Breast Cosmesis Data
bcos=cosmesis
bcos2<-data.frame(bcos[,1:2], x=1*(bcos$treat=="RCT"))
g<-0.41 #Hanson and Johnson 2004, JCGS,
res1<-maple.aft(cbind(left, right)~x, data=bcos2, M=c(1,30), g=g, tau=100, x0=1)
op<-par(mfrow=c(1,2), lwd=1.5)
plot(x=res1, which="likelihood")
plot(x=res1, y=data.frame(x=0), which="survival", model='aft', type="l", col=1,
add=FALSE, main="Survival Function")
plot(x=res1, y=data.frame(x=1), which="survival", model='aft', lty=2, col=1)
legend("bottomleft", bty="n", lty=1:2, col=1, c("Radiation Only", "Radiation and Chemotherapy"))
par(op)