| Title: | The Self-Consistent, Competing Risks (SC-CR) Algorithms |
|---|---|
| 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. |
| Authors: | Peter Adamic, Alicja Wolny-Dominiak |
| Maintainer: | Alicja Wolny-Dominiak<[email protected]> |
| License: | GPL-2 |
| Version: | 2.1 |
| Built: | 2024-10-31 21:14:51 UTC |
| Source: | CRAN |
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.
Peter Adamic, Alicja Wolny-Dominiak Maintainer: <[email protected]>
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 matrix corresponding I_(ijy) function
alpha(data, tau)alpha(data, tau)
data |
input matrix of probabilities |
tau |
the vector of time points corresponding to columns in input matrix |
Adamic, P., Caron, S. (2014), "SC-CR Algorithms with Informative Masking", Scandinavian Actuarial Journal, 2014(4), 339-351.
data(censoredMatrix) res <- inputM(censoredMatrix) alpha(res$input, res$tau)data(censoredMatrix) res <- inputM(censoredMatrix) alpha(res$input, res$tau)
A data frame with 8 observations on the following 5 variables.
La numeric vector
Ra numeric vector
C1a numeric vector
C2a numeric vector
C3a numeric vector
data(censoredMatrix) str(censoredMatrix)data(censoredMatrix) str(censoredMatrix)
The function creating the probability matrix and tau time vector from the double-censored data.
data |
censored data |
input |
the probability matrix |
tau |
time tau |
Alicja Wolny-Dominiak, Peter Adamic
data(censoredMatrix) res <- inputM(censoredMatrix) res$input res$taudata(censoredMatrix) res <- inputM(censoredMatrix) res$input res$tau
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).
survCompeting(data, tau, n, nc, epsilon)survCompeting(data, tau, n, nc, epsilon)
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 |
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 |
Peter Adamic, Alicja Wolny-Dominiak
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.
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))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))