PWEALL2-package: Design and Monitoring of Survival Trials Accounting for...

In marvels2031/PWEALL_1.4.0: Design and Monitoring of Survival Trials Accounting for Complex Situations

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. Additional functions are added to conduct NPH-MCPMod analysis. A vignette file is created to provide a step-by-step tutorial for this.

Details

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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 λ_2^{\bf x}(t\mid u). For Type (1), the Markov crossover,

λ_2^{\bf x}(t\mid u)=λ_2(t).

For Type (2), the Semi-Markov crossover,

λ_2^{\bf x}(t\mid u)=λ_2(t-u).

For Type (3), the hybrid crossover-1,

λ_2^{\bf x}(t\mid u)=π_2λ_2(t-u)+(1-π_2)λ_4(t).

For Type (4), the hazard after crossover is

λ_2^{\bf x}(t\mid u)=\frac{π_2λ_2(t-u)S_2(t-u)+(1-π_2)λ_4(t)S_4(t)/S_4(u)}{π_2 S_2(t-u)+(1-π_2)S_4(t)/S_4(u)}.

For Type (5), the hazard after crossover is

λ_2^{\bf x}(t\mid u)=\frac{π_2λ_2(t-u)S_2(t-u)+(1-π_2)λ_4(t-u)S_4(t-u)}{π_2 S_2(t-u)+(1-π_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 π_2 to have hazard λ_2 and probability 1-π_2 to have hazard λ_4. The types (4) and (5) differ in having λ_4 as Markov or Semi-markov.

Author(s)

NA

Maintainer: NA

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>.

marvels2031/PWEALL_1.4.0 documentation built on April 22, 2022, 12:52 a.m.