| PWEALL-package {PWEALL} | R Documentation |
Design and Monitoring of Survival Trials Accounting for Complex Situations
Description
Calculates various functions needed for design and monitoring survival trials accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual, and different censoring distributions between groups. The event time distribution is assumed to be piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution. As compared with Version 1.2.1, two more types of hybrid crossover are added. A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, density, hazard and cumulative hazard functions. Also, to generate the crossover-adjusted event time random variable, a more efficient algorithm is used and the output includes crossover indicators.
Details
The DESCRIPTION file:
| Package: | PWEALL |
| Type: | Package |
| Version: | 1.3.0.1 |
| Date: | 2018-10-18 |
| Title: | Design and Monitoring of Survival Trials Accounting for Complex Situations |
| Description: | Calculates various functions needed for design and monitoring survival trials accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual, and different censoring distributions between groups. The event time distribution is assumed to be piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution. As compared with Version 1.2.1, two more types of hybrid crossover are added. A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, density, hazard and cumulative hazard functions. Also, to generate the crossover-adjusted event time random variable, a more efficient algorithm is used and the output includes crossover indicators. |
| Authors@R: | c( person(given="Xiaodong", family="Luo", email = "Xiaodong.Luo@sanofi.com", role =c("aut", "cre")), person(given="Xuezhou", family="Mao", role = "ctb"), person(given="Xun", family="Chen", role = "ctb"), person(given="Hui", family="Quan", role = "ctb"), person("Sanofi", role = "cph")) |
| Depends: | R (>= 3.1.2) |
| Imports: | survival, stats |
| License: | GPL (>= 2) |
| RoxygenNote: | 5.0.1 |
| LazyData: | true |
| NeedsCompilation: | yes |
| Packaged: | 2018-10-18 03:31:00 UTC; Administrator |
| Author: | Xiaodong Luo [aut, cre], Xuezhou Mao [ctb], Xun Chen [ctb], Hui Quan [ctb], Sanofi [cph] |
| Maintainer: | Xiaodong Luo <Xiaodong.Luo@sanofi.com> |
| Repository: | CRAN |
| Date/Publication: | 2018-10-18 11:30:13 UTC |
Index of help topics:
PWEALL-package Design and Monitoring of Survival Trials
Accounting for Complex Situations
cp Conditional power given observed log hazard
ratio
cpboundary The stopping boundary based on the conditional
power criteria
cpstop The stopping probability based on the stopping
boundary
fourhr A utility functon
hxbeta A function to calculate the beta-smoothed
hazard rate
innercov A utility function to calculate the inner
integration of the overall covariance
innervar A utility function to calculate the inner
integration of the overall variance
instudyfindt calculate the timeline in study when some or
all subjects have entered
ovbeta calculate the overall log hazard ratio
overallcov calculate the overall covariance
overallcovp1 calculate the first part of the overall
covariance
overallcovp2 calculate the other parts of the overall
covariance
overallvar calculate the overall variance
pwe Piecewise exponential distribution: hazard,
cumulative hazard, density, distribution,
survival
pwecx Various function for piecewise exponential
distribution with crossover effect
pwecxcens Integration of the density of piecewise
exponential distribution with crossover effect
and the censoring function
pwecxpwu Integration of the density of piecewise
exponential distribution with crossover effect,
censoring and recruitment function
pwecxpwufindt calculate the timeline when certain number of
events accumulates
pwecxpwuforvar calculate the utility function used for
varaince calculation
pwefv2 A utility function
pwefvplus A utility functon
pwepower Calculating the powers of various the test
statistics for superiority trials
pwepowereq Calculating the powers of various the test
statistics for equivalence trials
pwepowerfindt Calculating the timepoint where a certain power
of the specified test statistics is obtained
pwepowerni Calculating the powers of various the test
statistics for non-inferiority trials
pwesim simulating the test statistics
pwu Piecewise uniform distribution: distribution
qpwe Piecewise exponential distribution: quantile
function
qpwu Piecewise uniform distribution: quantile
function
rmstcov Calculation of the variance and covariance of
estimated restricted mean survival time
rmsth Estimate the restricted mean survival time
(RMST) and its variance from data
rmstpower Calculate powers at different cut-points based
on difference of restricted mean survival times
(RMST)
rmstpowerfindt Calculating the timepoint where a certain power
of mean difference of RMSTs is obtained
rmstsim simulating the restricted mean survival times
rmstutil A utility function to calculate the true
restricted mean survival time (RMST) and its
variance account for delayed treatment,
discontinued treatment and non-uniform entry
rpwe Piecewise exponential distribution: random
number generation
rpwecx Piecewise exponential distribution with
crossover effect: random number generation
rpwu Piecewise uniform distribution: random number
generation
spf A utility function
wlrcal A utility function to calculate the weighted
log-rank statistics and their varainces given
the weights
wlrcom A function to calculate the various weighted
log-rank statistics and their varainces
wlrutil A utility function to calculate some common
functions in contructing weights
There are 5 types of crossover considered in the package: (1) Markov crossover, (2) Semi-Markov crosover, (3) Hybrid crossover-1, (4) Hybrid crossover-2 and (5) Hybrid crossover-3. The first 3 types are described in Luo et al. (2018). The fourth and fifth types are added for Version 1.3.0. The crossover type is determined by the hazard function after crossover \lambda_2^{\bf x}(t\mid u). For Type (1), the Markov crossover,
\lambda_2^{\bf x}(t\mid u)=\lambda_2(t).
For Type (2), the Semi-Markov crossover,
\lambda_2^{\bf x}(t\mid u)=\lambda_2(t-u).
For Type (3), the hybrid crossover-1,
\lambda_2^{\bf x}(t\mid u)=\pi_2\lambda_2(t-u)+(1-\pi_2)\lambda_4(t).
For Type (4), the hazard after crossover is
\lambda_2^{\bf x}(t\mid u)=\frac{\pi_2\lambda_2(t-u)S_2(t-u)+(1-\pi_2)\lambda_4(t)S_4(t)/S_4(u)}{\pi_2 S_2(t-u)+(1-\pi_2)S_4(t)/S_4(u)}.
For Type (5), the hazard after crossover is
\lambda_2^{\bf x}(t\mid u)=\frac{\pi_2\lambda_2(t-u)S_2(t-u)+(1-\pi_2)\lambda_4(t-u)S_4(t-u)}{\pi_2 S_2(t-u)+(1-\pi_2)S_4(t-u)}.
The types (4) and (5) are more closely related to "re-randomization", i.e. when a patient crosses, (s)he will have probability \pi_2 to have hazard \lambda_2 and probability 1-\pi_2 to have hazard \lambda_4. The types (4) and (5) differ in having \lambda_4 as Markov or Semi-markov.
Author(s)
Xiaodong Luo [aut, cre], Xuezhou Mao [ctb], Xun Chen [ctb], Hui Quan [ctb], Sanofi [cph]
Maintainer: Xiaodong Luo <Xiaodong.Luo@sanofi.com>
References
Luo et al. (2018) Design and monitoring of survival trials in complex scenarios, Statistics in Medicine <doi: https://doi.org/10.1002/sim.7975>.