estimation.weights.phase3 {CaseCohortCoxSurvival} | R Documentation |
estimation.weights.phase3
Description
Estimates the weights for the third phase of sampling (due to missingness).
Usage
estimation.weights.phase3(B.phase3, total.phase2, gamma0 = NULL, niter.max = NULL,
epsilon.stop = NULL)
Arguments
B.phase3 |
matrix for the case-cohort (phase-three data), with phase-three
sampling strata indicators. It should have as many columns as phase-three strata
( |
total.phase2 |
vector of length |
gamma0 |
vector of length |
niter.max |
maximum number of iterations for the iterative optimization
algorithm. Default is |
epsilon.stop |
threshold for the difference between the estimated weighted
total and the total in the whole cohort. If this difference is less than the
value of |
Details
estimation.weights.phase3
estimates the phase-three sampling weights by solving in
\gamma
\sum_{j=1}^J \sum_{i=1}^{n^{(j)}} \lbrace \xi_{i,j} V_{i,j}
\text{exp}( \gamma' B_{i,j}) B_{i,j} - \xi_{i,j} B_{i,j} \rbrace = 0,
with \xi_{i,j}
the phase-two sampling indicator and V_{i,j}
the phase-three
sampling indicator of individual i
in stratum j
, and with
\sum_{j=1}^J \sum_{i=1}^{n^{(j)}} \xi_{i,j} B_{i,j}
the total in the
phase-two data. See Section 5.2 in Etievant and Gail (2023).
The Newton Raphson method is used to solve the optimization problem.
In the end, the estimated weights are given by \text{exp}(\hat \gamma' B_{i,j})
,
and \sum_{j=1}^J \sum_{i=1}^{n^{(j)}} \xi_{i,j} V_{i,j} \text{exp}(\hat \gamma' B_{i,j}) B_{i,j}
gives the estimated total.
Value
gamma.hat
: vector of length J^{(3)}
with final gamma values.
estimated.weights
: vector with the estimated phase-three weights for the
individuals in the case-cohort (phase-three data), computed from B.phase3
and gamma.hat
.
estimated.total
: vector with the estimated totals, computed from the
estimated.weights
and B.phase3
.
References
Etievant, L., Gail, M.H. (2023). Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data. Submitted.
See Also
influences.missingdata
, influences.RH.missingdata
,
influences.CumBH.missingdata
and influences.PR.missingdata
.
Examples
data(dataexample.missingdata, package="CaseCohortCoxSurvival")
casecohort <- dataexample.missingdata$casecohort # a simulated stratified case-cohort
# phase-two data: dataexample.missingdata$casecohort.phase2
B.phase2 <- dataexample.missingdata$B.phase2
B.phase3 <- dataexample.missingdata$B.phase3
total.B.phase2 <- colSums(B.phase2)
J3 <- ncol(B.phase3)
estimation.weights.p3 <- estimation.weights.phase3(B.phase3 = B.phase3,
total.phase2 = total.B.phase2,
gamma0 = rep(0, J3),
niter.max = 10^(4),
epsilon.stop = 10^(-10))
# print estimated phase-three weights
#estimation.weights.p3$estimated.weights