| Type: | Package |
| Title: | The Self-Consistent, Competing Risks (SC-CR) Algorithms |
| Version: | 2.1 |
| Date: | 2020-12-11 |
| Author: | Peter Adamic, Alicja Wolny-Dominiak |
| Maintainer: | Alicja Wolny-Dominiak<woali@ue.katowice.pl> |
| Description: | The SC-SR Algorithm is used to calculate fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible making of the failure cause in a competing risks environment. In the version 2.0 the function creating the probability matrix from double-censored data is added. |
| Imports: | dplyr |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Packaged: | 2020-12-11 11:29:20 UTC; woali |
| Depends: | R (≥ 3.5.0) |
| Repository: | CRAN |
| Date/Publication: | 2020-12-11 12:10:03 UTC |
The Self-Consistent, Competing Risks (SC-CR) Algorithms
Description
The SC-SR Algorithm is used to calculate the cause-deleted life expectancy improvement for left and right censored data. In the version 2.0 the function creating the probability matrix from double-censored data is added.
Author(s)
Peter Adamic, Alicja Wolny-Dominiak Maintainer: <alicja.wolny-dominiak@ue.katowice.pl>
References
1. Adamic, P., Caron, S. (2014),
"SC-CR Algorithms with Informative Masking",
Scandinavian Actuarial Journal, 2014(4), 339-351.
2. Adamic, P., Dixon, S., Gillis, D. (2010),
"Multiple Decrement Modeling in the Presence of Interval
Censoring and Masking", Scandinavian Actuarial Journal, 2010(4), 312-327.
3. Adamic, P., Ouadah, S. (2009),
"A Kernel Method for Modeling Interval Censored Competing
Risks", South African Statistical Journal, 43(1), 1-20.
4. Turnbull, B. (1976). The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data, Journal of the Royal Statistical Society. Series B (Methodological), 38(3), 290-295.
The alpha matrix
Description
The matrix corresponding I_(ijy) function
Usage
alpha(data, tau)
Arguments
data |
input matrix of probabilities |
tau |
the vector of time points corresponding to columns in input matrix |
References
Adamic, P., Caron, S. (2014), "SC-CR Algorithms with Informative Masking", Scandinavian Actuarial Journal, 2014(4), 339-351.
Examples
data(censoredMatrix)
res <- inputM(censoredMatrix)
alpha(res$input, res$tau)
The double-censored data
Description
A data frame with 8 observations on the following 5 variables.
Format
La numeric vector
Ra numeric vector
C1a numeric vector
C2a numeric vector
C3a numeric vector
Examples
data(censoredMatrix)
str(censoredMatrix)
The probability matrix creator
Description
The function creating the probability matrix and tau time vector from the double-censored data.
Arguments
data |
censored data |
Value
input |
the probability matrix |
tau |
time tau |
Author(s)
Alicja Wolny-Dominiak, Peter Adamic
Examples
data(censoredMatrix)
res <- inputM(censoredMatrix)
res$input
res$tau
Self-Consistent, Competing Risks (SC-CR) Algorithms
Description
This package describes an algorithm for producing fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible masking of the failure cause in a competing risks environment. It is a generalization of Turnbull's (1976) classic univariate algorithm. The algorithm was published in Adamic et al. (2010) and Adamic & Caron (2014).
Usage
survCompeting(data, tau, n, nc, epsilon)
Arguments
data |
input matrix of probabilities |
tau |
the vector of time points corresponding to columns in input matrix |
n |
the number of intervals in the dataset corresponding to rows in input matrix |
nc |
the number of causes (competing risks) |
epsilon |
small predermined value > 0 |
Value
Yj |
estimated number at risk at time tau_j |
djc |
estimated number of events occuring at time tau_j by cause c |
pjc |
estimated probability for risk at time tau_j by cause c |
djList |
the list of d_j for every cause c |
pjList |
the list of p_j for every cause c |
pjListold |
the list of p_j for every cause c in the (iter - 1) iteration |
iter |
the number of iterations in the algorithm |
Author(s)
Peter Adamic, Alicja Wolny-Dominiak
References
1. Adamic, P., Caron, S. (2014),
"SC-CR Algorithms with Informative Masking",
Scandinavian Actuarial Journal, 2014(4), 339-351.
2. Adamic, P., Dixon, S., Gillis, D. (2010),
"Multiple Decrement Modeling in the Presence of Interval
Censoring and Masking", Scandinavian Actuarial Journal, 2010(4), 312-327.
3. Adamic, P., Ouadah, S. (2009),
"A Kernel Method for Modeling Interval Censored Competing
Risks", South African Statistical Journal, 43(1), 1-20.
4. Turnbull, B. (1976). The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data, Journal of the Royal Statistical Society. Series B (Methodological), 38(3), 290-295.
Examples
data(censoredMatrix)
df <- inputM(censoredMatrix)
res <- survCompeting(df$input, df$tau, 8, 3, 0.01)
res
#summary
round(res$Yj, 2)
round(res$djc, 2)
round(res$pjc, 2)
res$iter
sum(unlist(res$pjList))
sum(unlist(res$pjListold))