| Type: | Package |
| Title: | Flexible Parametric Accelerated Hazards Models |
| Version: | 0.1.0 |
| Author: | Authors@R. c(person("Abdisalam", "Hassan","Muse" email="abdisalam.h.muse@gmail.com", role=c("aut", "ctb","cre")), person("Samuel", "Mwalili", role=c("ctb")), person("Oscar", "Ngesa", role=c("ctb")), person("Mutua", "Kilai", role = c("ctb")) ) |
| Maintainer: | Abdisalam Hassan Muse <abdisalam.h.muse@gmail.com> |
| Description: | Flexible parametric Accelerated Hazards (AH) regression models in overall and relative survival frameworks with 13 distinct Baseline Distributions. The AH Model can also be applied to lifetime data with crossed survival curves. Any user-defined parametric distribution can be fitted, given at least an R function defining the cumulative hazard and hazard rate functions. See Chen and Wang (2000) <doi:10.1080/01621459.2000.10474236>, and Lee (2015) <doi:10.1007/s10985-015-9349-5> for more details. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | flexsurv, rootSolve, stats, stats4 |
| Depends: | R (≥ 2.10) |
| RoxygenNote: | 7.1.2 |
| NeedsCompilation: | no |
| Packaged: | 2022-06-02 08:24:18 UTC; Admin |
| Repository: | CRAN |
| Date/Publication: | 2022-06-02 11:20:05 UTC |
Relative Survival AH model.
Description
The flexible parametric accelerated excess hazards (AEH) model's maximum likelihood estimation, log-likelihood, and information criterion. Baseline hazards:NGLL, GLL, KW,EW, MLL, PGW, GG, MKW, Log-logistic, Weibull, Log-normal, Burr-XII, and Gamma
Usage
AEHMLE(
init,
time,
delta,
n,
basehaz,
z,
hp.obs,
method = "Nelder-Mead",
maxit = 1000,
log = FALSE
)
Arguments
init |
: initial points for optimisation |
time |
: survival times |
delta |
: vital indicator (0-alive,1 - dead) |
n |
: The number of the observations of the data set |
basehaz |
: baseline hazard structure including baseline (NGLLAEH,GLLAEH,EWAEH,KWAEH,MLLAEH, PGWAEH,GGAEH,MKWAEH,LLAEH,WAEH,GAEH, LNAEH,BXIIAEEH) |
z |
: design matrix for covariates (p x n), p >= 1 |
hp.obs |
: population hazards (for uncensored individuals) |
method |
:"nlminb" or a method from "optim" |
maxit |
:The maximum number of iterations. Defaults to 1000 |
log |
:log scale (TRUE or FALSE) |
Format
By default the function calculates the following values:
AIC: Akaike Information Criterion;
CAIC: Consistent Akaikes Information Criterion;
BIC: Bayesian Information Criterion;
BCAIC: Bozdogan’s Consistent Akaike Information Criterion;
HQIC: Hannan-Quinn information criterion;
par: maximum likelihood estimates;
Value: value of the likelihood function;
Convergence: 0 indicates successful completion and 1 indicates that the iteration limit maxit.
Value
a list containing the output of the optimisation (OPT) and the information criterion including (AIC, BIC, CAIC, BCAIC, and HQIC).
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
data(bmt)
time<-bmt$Time
delta<-bmt$Status
z<-bmt$TRT
AEHMLE(init = c(1.0,0.5,1.0,0.5),time = time,delta = delta,n=nrow(z),
basehaz = "GLLAEH",z = z,hp.obs=0.6,method = "Nelder-Mead",
maxit = 1000)
Overall Survival AH model.
Description
The flexible parametric accelerated hazards (AH) model's maximum likelihood estimation, log-likelihood, and information criterion. Baseline hazards: NGLL, GLL,KW, EW, MLL, PGW, GG, MKW, Log-logistic, Weibull, Log-normal, Burr-XII, and Gamma
Usage
AHMLE(
init,
time,
delta,
n,
basehaz,
z,
method = "Nelder-Mead",
maxit = 1000,
log = FALSE
)
Arguments
init |
: initial points for optimisation |
time |
: survival times |
delta |
: vital indicator (0-alive,1 - dead,) |
n |
: The number of the observations of the data set |
basehaz |
: baseline hazard structure including baseline (NGLLAH,GLLAH,EWAH,KWAH,MLLAH,PGWAH,GGAH, MKWAH,LLAH,WAH,GAH,LNAH,BXIIAH) |
z |
: design matrix for covariates (p x n), p >= 1 |
method |
:"nlminb" or a method from "optim" |
maxit |
:The maximum number of iterations. Defaults to 1000 |
log |
:log scale (TRUE or FALSE) |
Format
By default the function calculates the following values:
AIC: Akaike Information Criterion;
CAIC: Consistent Akaikes Information Criterion;
BIC: Bayesian Information Criterion;
BCAIC: Bozdogan’s Consistent Akaike Information Criterion;
HQIC: Hannan-Quinn information criterion;
par: maximum likelihood estimates;
Value: value of the likelihood function;
Convergence: 0 indicates successful completion and 1 indicates that the iteration limit maxit.
Details
The function AHMLE returns MLE estimates and information criterion.
Value
a list containing the output of the optimisation (OPT) and the information criterion including (AIC, BIC, CAIC, BCAIC, and HQIC).
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
#Example #1
data(ipass)
time<-ipass$time
delta<-ipass$status
z<-ipass$arm
AHMLE(init = c(1.0,1.0,1.0,0.5),time = time,delta = delta,n=nrow(z),
basehaz = "GLLAH",z = z,method = "Nelder-Mead",
maxit = 1000)
#Example #2
data(bmt)
time<-bmt$Time
delta<-bmt$Status
z<-bmt$TRT
AHMLE(init = c(1.0,1.0,1.0,0.5),time = time,delta = delta,n=nrow(z),
basehaz = "GLLAH",z = z,method = "Nelder-Mead",
maxit = 1000)
#Example #3
data("e1684")
time<-e1684$FAILTIME
delta<-e1684$FAILCENS
TRT<-e1684$TRT
AGE<-e1684$TRT
z<-as.matrix(cbind(scale(TRT), scale(AGE) ))
AHMLE(init = c(1.0,1.0,1.0,0.5,0.75),time = time,delta = delta,n=nrow(z),
basehaz = "GLLAH",z = z,method = "Nelder-Mead",maxit = 1000)
#Example #4
data("LeukSurv")
time<-LeukSurv$time
delta<-LeukSurv$cens
age<-LeukSurv$age
wbc<-LeukSurv$wbc
tpi<-LeukSurv$tpi
z<-as.matrix(cbind(scale(age), scale(tpi),scale(wbc) ))
AHMLE(init = c(1.0,1.0,1.0,1.0,0.5,0.65,0.85),time = time,delta = delta,n=nrow(z),
basehaz = "NGLLAH",z = z,method = "Nelder-Mead",maxit = 1000)
Burr-XII (BXII) Cumulative Hazard Function.
Description
Burr-XII (BXII) Cumulative Hazard Function.
Usage
CHBXII(t, kappa, alpha)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
Value
the value of the BXII cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
CHBXII(t=t, kappa=0.5, alpha=0.35)
Exponentiated Weibull (EW) Cumulative Hazard Function.
Description
Exponentiated Weibull (EW) Cumulative Hazard Function.
Usage
CHEW(t, lambda, kappa, alpha)
Arguments
t |
: positive argument |
lambda |
: scale parameter |
kappa |
: shape parameter |
alpha |
: shape parameter |
Value
the value of the EW cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Rubio, F. J., Remontet, L., Jewell, N. P., & Belot, A. (2019). On a general structure for hazard-based regression models: an application to population-based cancer research. Statistical methods in medical research, 28(8), 2404-2417.
Examples
t=runif(10,min=0,max=1)
CHEW(t=t, lambda=0.9, kappa=0.5, alpha=0.75)
Gamma (G) Cumulative Hazard Function.
Description
Gamma (G) Cumulative Hazard Function.
Usage
CHG(t, shape, scale)
Arguments
t |
: positive argument |
shape |
: shape parameter |
scale |
: scale parameter |
Value
the value of the G cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
CHG(t=t, shape=0.85, scale=0.5)
Generalised Gamma (GG) Cumulative Hazard Function.
Description
Generalised Gamma (GG) Cumulative Hazard Function.
Usage
CHGG(t, kappa, alpha, eta)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
Value
the value of the GG cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
CHGG(t=t, kappa=0.5, alpha=0.35, eta=0.9)
Generalized Log-logistic (GLL) cumulative hazard function.
Description
Generalized Log-logistic (GLL) cumulative hazard function.
Usage
CHGLL(t, kappa, alpha, eta)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
Value
the value of the GLL cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Muse, A. H., Mwalili, S., Ngesa, O., Almalki, S. J., & Abd-Elmougod, G. A. (2021). Bayesian and classical inference for the generalized log-logistic distribution with applications to survival data. Computational intelligence and neuroscience, 2021.
Examples
t=runif(10,min=0,max=1)
CHGLL(t=t, kappa=0.5, alpha=0.35, eta=0.9)
Kumaraswamy Weibull (KW) Cumulative Hazard Function.
Description
Kumaraswamy Weibull (KW) Cumulative Hazard Function.
Usage
CHKW(t, alpha, kappa, eta, zeta)
Arguments
t |
: positive argument |
alpha |
: scale parameter |
kappa |
: shape parameter |
eta |
: shape parameter |
zeta |
: shape parameter |
Value
the value of the KW cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
CHKW(t=t, alpha=0.35, kappa=0.5, eta=1.20, zeta=1.5)
Log-logistic (LL) Cumulative Hazard Function.
Description
Log-logistic (LL) Cumulative Hazard Function.
Usage
CHLL(t, kappa, alpha)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
Value
the value of the LL cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
CHLL(t=t, kappa=0.5, alpha=0.35)
Lognormal (LN) Cumulative Hazard Function.
Description
Lognormal (LN) Cumulative Hazard Function.
Usage
CHLN(t, kappa, alpha)
Arguments
t |
: positive argument |
kappa |
: meanlog parameter |
alpha |
: sdlog parameter |
Value
the value of the LN cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
CHLN(t=t, kappa=0.75, alpha=0.95)
Modified Kumaraswamy Weibull (MKW) Cumulative Hazard Function.
Description
Modified Kumaraswamy Weibull (MKW) Cumulative Hazard Function.
Usage
CHMKW(t, alpha, kappa, eta)
Arguments
t |
: positive argument |
alpha |
: Inverse scale parameter |
kappa |
: shape parameter |
eta |
: shape parameter |
Value
the value of the MKW cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
CHMKW(t=t,alpha=0.35, kappa=0.7, eta=1.4)
Modified Log-logistic (MLL) cumulative hazard function.
Description
Modified Log-logistic (MLL) cumulative hazard function.
Usage
CHMLL(t, kappa, alpha, eta)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
Value
the value of the MLL cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Kayid, M. (2022). Applications of Bladder Cancer Data Using a Modified Log-Logistic Model. Applied Bionics and Biomechanics, 2022.
Examples
t=runif(10,min=0,max=1)
CHMLL(t=t, kappa=0.75, alpha=0.5, eta=0.9)
New Generalized Log-logistic (GLL) cumulative hazard function.
Description
New Generalized Log-logistic (GLL) cumulative hazard function.
Usage
CHNGLL(t, kappa, alpha, eta, zeta)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
zeta |
: shape parameter |
Value
the value of the NGLL cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Hassan Muse, A. A new generalized log-logistic distribution with increasing, decreasing, unimodal and bathtub-shaped hazard rates: properties and applications, in Proceedings of the Symmetry 2021 - The 3rd International Conference on Symmetry, 8–13 August 2021, MDPI: Basel, Switzerland, doi:10.3390/Symmetry2021-10765.
Examples
t=runif(10,min=0,max=1)
CHNGLL(t=t, kappa=0.5, alpha=0.35, eta=0.7, zeta=1.4)
Power Generalised Weibull (PGW) cumulative hazard function.
Description
Power Generalised Weibull (PGW) cumulative hazard function.
Usage
CHPGW(t, kappa, alpha, eta)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
Value
the value of the PGW cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Alvares, D., & Rubio, F. J. (2021). A tractable Bayesian joint model for longitudinal and survival data. Statistics in Medicine, 40(19), 4213-4229.
Examples
t=runif(10,min=0,max=1)
CHPGW(t=t, kappa=0.5, alpha=1.5, eta=0.6)
Weibull (W) Cumulative Hazard Function.
Description
Weibull (W) Cumulative Hazard Function.
Usage
CHW(t, kappa, alpha)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
Value
the value of the W cumulative hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
CHW(t=t, kappa=0.75, alpha=0.5)
The Leukemia Survival Data
Description
A dataset on the survival of acute myeloid leukemia in 1,043 pateietns, first analyzed by Henderson et al. (2002). It is of interest to investigate possible spatial variation in survival after accounting for known subject-specific prognostic factors, which include age, sex, white blood cell count (wbc) at diagnosis, and the Townsend score (tpi) for which higher values indicates less affluent areas. Both exact residential locations of all patients and their administrative districts (24 districts that make up the whole region) are available.
Format
A data frame with 1043 rows and 9 variables:
time: survival time in days
cens: right censoring status 0=censored, 1=dead
xcoord: coordinates in x-axis of residence
ycoord: coordinates in y-axis of residence
age: age in years
sex:male=1 female=0
wbc:white blood cell count at diagnosis, truncated at 500
tpi: the Townsend score for which higher values indicates less affluent areas
district:administrative district of residence
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Henderson, R., Shimakura, S., and Gorst, D. (2002), Modeling spatial variation in leukemia survival data, Journal of the American Statistical Association, 97(460), 965-972.
Bone Marrow Transplant (bmt) data set
Description
Bone marrow transplant study which is widely used in the hazard-based regression models
Format
There were 46 patients in the allogeneic treatment and 44 patients in the autologous treatment group
Time: time to event
Status: censor indicator, 0 for censored and 1 for uncensored
TRT: 1 for autologous treatment group; 0 for allogeneic treatment group
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Robertson, V. M., Dickson, L. G., Romond, E. H., & Ash, R. C. (1987). Positive antiglobulin tests due to intravenous immunoglobulin in patients who received bone marrow transplant. Transfusion, 27(1), 28-31.
Exponentiated Weibull (EW) Probability Density Function.
Description
Exponentiated Weibull (EW) Probability Density Function.
Usage
dexpweibull(t, lambda, kappa, alpha, log = FALSE)
Arguments
t |
: positive argument |
lambda |
: scale parameter |
kappa |
: shape parameter |
alpha |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the EW probability density function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
dexpweibull(t=t, lambda=0.6,kappa=0.5, alpha=0.45, log=FALSE)
Generalised Gamma (GG) Probability Density Function.
Description
Generalised Gamma (GG) Probability Density Function.
Usage
dggamma(t, kappa, alpha, eta, log = FALSE)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the GG probability density function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
dggamma(t=t, kappa=0.5, alpha=0.35, eta=0.9,log=FALSE)
Melanoma data set
Description
Eastern Cooperative Oncology Group (ECOG) data used for modeling hazard-based regression models
Format
A data frame with 284 observations on the following 5 variables.
TRT: 0=control group, 1=IFN treatment group
FAILTIME: observed relapse-free time
FAILCENS: relapse-free censor indicator
AGE:continuous variable, which is centered to the mean
SEX: 0 for male, 1 fopr female
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Kirkwood, J. M., Manola, J., Ibrahim, J., Sondak, V., Ernstoff, M. S., & Rao, U. (2004). A pooled analysis of eastern cooperative oncology group and intergroup trials of adjuvant high-dose interferon for melanoma. Clinical Cancer Research, 10(5), 1670-1677.
Burr-XII (BXII) Hazard Function.
Description
Burr-XII (BXII) Hazard Function.
Usage
hBXII(t, kappa, alpha, log = FALSE)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the BXII hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
hBXII(t=t, kappa=0.85, alpha=0.45,log=FALSE)
Exponentiated Weibull (EW) Hazard Function.
Description
Exponentiated Weibull (EW) Hazard Function.
Usage
hEW(t, lambda, kappa, alpha, log = FALSE)
Arguments
t |
: positive argument |
lambda |
: scale parameter |
kappa |
: shape parameter |
alpha |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the EW hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Khan, S. A. (2018). Exponentiated Weibull regression for time-to-event data. Lifetime data analysis, 24(2), 328-354.
Examples
t=runif(10,min=0,max=1)
hEW(t=t, lambda=0.9, kappa=0.5, alpha=0.75, log=FALSE)
Gamma (G) Hazard Function.
Description
Gamma (G) Hazard Function.
Usage
hG(t, shape, scale, log = FALSE)
Arguments
t |
: positive argument |
shape |
: shape parameter |
scale |
: scale parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the G hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
hG(t=t, shape=0.5, scale=0.85,log=FALSE)
Generalised Gamma (GG) Hazard Function.
Description
Generalised Gamma (GG) Hazard Function.
Usage
hGG(t, kappa, alpha, eta, log = FALSE)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the GG hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Agarwal, S. K., & Kalla, S. L. (1996). A generalized gamma distribution and its application in reliabilty. Communications in Statistics-Theory and Methods, 25(1), 201-210.
Examples
t=runif(10,min=0,max=1)
hGG(t=t, kappa=0.5, alpha=0.35, eta=0.9,log=FALSE)
Generalized Log-logistic (GLL) hazard function.
Description
Generalized Log-logistic (GLL) hazard function.
Usage
hGLL(t, kappa, alpha, eta, log = FALSE)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the GLL hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Muse, A. H., Mwalili, S., Ngesa, O., Alshanbari, H. M., Khosa, S. K., & Hussam, E. (2022). Bayesian and frequentist approach for the generalized log-logistic accelerated failure time model with applications to larynx-cancer patients. Alexandria Engineering Journal, 61(10), 7953-7978.
Examples
t=runif(10,min=0,max=1)
hGLL(t=t, kappa=0.5, alpha=0.35, eta=0.7, log=FALSE)
Kumaraswamy Weibull (KW) Hazard Function.
Description
Kumaraswamy Weibull (KW) Hazard Function.
Usage
hKW(t, alpha, kappa, eta, zeta, log = FALSE)
Arguments
t |
: positive argument |
alpha |
: scale parameter |
kappa |
: shape parameter |
eta |
: shape parameter |
zeta |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the KW hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Cordeiro, G. M., Ortega, E. M., & Nadarajah, S. (2010). The Kumaraswamy Weibull distribution with application to failure data. Journal of the Franklin Institute, 347(8), 1399-1429.
Examples
t=runif(10,min=0,max=1)
hKW(t=t, alpha=0.35, kappa=0.5, eta=1.20, zeta=1.5, log=FALSE)
Log-logistic (LL) Hazard Function.
Description
Log-logistic (LL) Hazard Function.
Usage
hLL(t, kappa, alpha, log = FALSE)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the LL hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
hLL(t=t, kappa=0.5, alpha=0.35,log=FALSE)
Lognormal (LN) Hazard Function.
Description
Lognormal (LN) Hazard Function.
Usage
hLN(t, kappa, alpha, log = FALSE)
Arguments
t |
: positive argument |
kappa |
: meanlog parameter |
alpha |
: sdlog parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the LN hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
hLN(t=t, kappa=0.5, alpha=0.75,log=FALSE)
Modified Kumaraswamy Weibull (MKW) Hazard Function.
Description
Modified Kumaraswamy Weibull (MKW) Hazard Function.
Usage
hMKW(t, alpha, kappa, eta, log = FALSE)
Arguments
t |
: positive argument |
alpha |
: inverse scale parameter |
kappa |
: shape parameter |
eta |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the MKW hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Khosa, S. K. (2019). Parametric Proportional Hazard Models with Applications in Survival analysis (Doctoral dissertation, University of Saskatchewan).
Examples
t=runif(10,min=0,max=1)
hMKW(t=t, alpha=0.35, kappa=0.7, eta=1.4, log=FALSE)
Modified Log-logistic (MLL) hazard function.
Description
Modified Log-logistic (MLL) hazard function.
Usage
hMLL(t, kappa, alpha, eta, log = FALSE)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the MLL hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
hMLL(t=t, kappa=0.75, alpha=0.5, eta=0.9,log=FALSE)
New Generalized Log-logistic (GLL) hazard function.
Description
New Generalized Log-logistic (GLL) hazard function.
Usage
hNGLL(t, kappa, alpha, eta, zeta, log = FALSE)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
zeta |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the NGLL hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
hNGLL(t=t, kappa=0.5, alpha=0.35, eta=0.7, zeta=1.4, log=FALSE)
Power Generalised Weibull (PGW) hazard function.
Description
Power Generalised Weibull (PGW) hazard function.
Usage
hPGW(t, kappa, alpha, eta, log = FALSE)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the PGW hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
hPGW(t=t, kappa=0.5, alpha=1.5, eta=0.6,log=FALSE)
Weibull (W) Hazard Function.
Description
Weibull (W) Hazard Function.
Usage
hW(t, kappa, alpha, log = FALSE)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
log |
:log scale (TRUE or FALSE) |
Value
the value of the w hazard function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
hW(t=t, kappa=0.75, alpha=0.5,log=FALSE)
IRESSA Pan-Asia Study (IPASS) data set
Description
Argyropoulos and Unruh (2015) published reconstructed IPASS clinical trial data. Despite being reconstructed, this data set retains all of the features shown in references, as well as full access to the observations from this clinical trial.The database spans the months of March 2006 to April 2008.The study's main goal is to compare gefitinib to carboplatin/paclitaxel doublet chemotherapy as first-line treatment in terms of progression-free survival (in months) in selected non-small-cell lung cancer (NSCLC) patients.
Format
A data frame with 1217 rows and 3 variables:
time: progression free survival (in months)
status: failure indicator (1 - failure; 0 - otherwise)
arm: (1 - gefitinib; 0 - carboplatin/paclitaxel doublet chemotherapy)
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
References
Argyropoulos, C. and Unruh, M. L. (2015). Analysis of time to event outcomes in randomized controlled trials by generalized additive models. PLOS One 10, 1-33.
Exponentiated Weibull (EW) Cumulative Distribution Function.
Description
Exponentiated Weibull (EW) Cumulative Distribution Function.
Usage
pexpweibull(t, lambda, kappa, alpha, log.p = FALSE)
Arguments
t |
: positive argument |
lambda |
: scale parameter |
kappa |
: shape parameter |
alpha |
: shape parameter |
log.p |
:log scale (TRUE or FALSE) |
Value
the value of the EW cumulative distribution function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
pexpweibull(t=t, lambda=0.65,kappa=0.45, alpha=0.25, log.p=FALSE)
Generalised Gamma (GG) Cumulative Distribution Function.
Description
Generalised Gamma (GG) Cumulative Distribution Function.
Usage
pggamma(t, kappa, alpha, eta, log.p = FALSE)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
log.p |
:log scale (TRUE or FALSE) |
Value
the value of the GG cumulative distribution function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
pggamma(t=t, kappa=0.5, alpha=0.35, eta=0.9,log.p=FALSE)
Generalised Gamma (GG) Survival Function.
Description
Generalised Gamma (GG) Survival Function.
Usage
sggamma(t, kappa, alpha, eta, log.p = FALSE)
Arguments
t |
: positive argument |
kappa |
: scale parameter |
alpha |
: shape parameter |
eta |
: shape parameter |
log.p |
:log scale (TRUE or FALSE) |
Value
the value of the GG survival function
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
t=runif(10,min=0,max=1)
sggamma(t=t, kappa=0.5, alpha=0.35, eta=0.9,log.p=FALSE)