Package 'gsMAMS'

Title: Group Sequential Designs of Multi-Arm Multi-Stage Trials
Description: It provides functions to generate operating characteristics and to calculate Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS) trials for different outcomes. The package is based on Jianrong Wu, Yimei Li, Liang Zhu (2023) <doi:10.1002/sim.9682>, Jianrong Wu, Yimei Li (2023) "Group Sequential Multi-Arm Multi-Stage Survival Trial Design with Treatment Selection"(Manuscript accepted for publication) and Jianrong Wu, Yimei Li, Shengping Yang (2023) "Group Sequential Multi-Arm Multi-Stage Trial Design with Ordinal Endpoints"(In preparation).
Authors: Tushar Patni [aut, cre], Yimei Li [aut], Jianrong Wu [aut]
Maintainer: Tushar Patni <[email protected]>
License: GPL-3
Version: 0.7.2
Built: 2025-02-22 05:03:06 UTC
Source: https://github.com/tpatni719/gsmams

Help Index

Design the clinical trial for continuous outcome

Description

This function generates the design parameters of a clinical trial for continuous outcome.

Usage

design_cont(delta0, delta1, alpha, beta, k, frac)

Arguments

delta0

numeric Standardized effect size in ineffective arm.
delta1

numeric Standardized effect size in effective arm.
alpha

numeric Type I error.
beta

numeric Type II error.
k

numeric Number of treatment arms.
frac

numeric Vector of fractions for information time at each look.

Value

List of cumulative sample size for each stage of treatment and control groups along with maximum total sample size of the trial. It also provides efficacy and futility boundaries of the trial.

Examples

design_cont(delta0 = 0.178, delta1 = 0.545, alpha = 0.05, beta = 0.1, k = 4, frac = c(1 / 2, 1))

Design the clinical trial for ordinal outcome

Description

This function generates the design parameters of a clinical trial for ordinal outcome.

Usage

design_ord(alpha, beta, k, prob, or0, or, frac)

Arguments

alpha

numeric Type I error.
beta

numeric Type II error.
k

numeric Number of treatment arms.
prob

numeric Probability of ordinal outcomes in control group.
or0

numeric Odds ratio of ineffective treatment group vs control.
or

numeric Odds ratio of effective treatment group vs control.
frac

numeric Vector of fractions for information time at each look.

Value

List of cumulative sample size for each stage of treatment and control groups along with maximum total sample size of the trial. It also provides efficacy and futility boundaries of the trial.

Examples

design_ord(alpha = 0.05,
           beta = 0.1,
           k = 4,
           prob = c(0.075, 0.182, 0.319, 0.243, 0.015, 0.166),
           or = 3.06,
           or0 = 1.32,
           frac = c(1 / 2, 1))

Design the clinical trial for survival outcome

Description

This function generates the design parameters of a clinical trial for survival outcome.

Usage

design_surv(m0, alpha, beta, k, hr0, hr1, ta, tf, kappa, eta, frac)

Arguments

m0

numeric Median survival time of control group.
alpha

numeric Type I error.
beta

numeric Type II error.
k

numeric Number of treatment arms.
hr0

numeric Hazard ratio of ineffective treatment group vs control.
hr1

numeric Hazard ratio of effective treatment group vs control.
ta

numeric Accrual time.
tf

numeric Follow-up time.
kappa

numeric Shape parameter (kappa=1 for exponential distribution).
eta

numeric Rate of loss to follow-up.
frac

numeric Vector of fractions for information time at each look.

Value

List of cumulative number of events for each stage of combined treatment and control groups along with total number of subjects and maximum total number of events for the trial. It also provides efficacy and futility boundaries of the trial.

Examples

design_surv(m0 = 20,
            hr0 = 1,
            hr1 = 0.65,
            ta = 20,
            tf = 40,
            alpha = 0.05,
            beta = 0.1,
            k = 3,
            kappa = 1,
            eta = 0,
            frac = c(1 / 2, 1))

Provides operating characteristics of group sequential MAMS trial for continuous outcome under null hypothesis

Description

Computes FWER and other characteristics for group-sequential MAMS trial for continuous outcome.

Usage

op_fwer_cont(alpha, beta, p, frac, delta0, delta1, nsim, seed)

Arguments

alpha

numeric Type I error.
beta

numeric Type II error.
p

numeric Number of treatment arms.
frac

numeric vector of fractions for information time at each look.
delta0

numeric Standardized effect size in ineffective arm.
delta1

numeric Standardized effect size in effective arm.
nsim

numeric Number of simulations.
seed

numeric Random seed number.

Value

A list of FWER, stage-wise type I error, average sample size used per arm, stopping probability, probability of futility.

Examples

op_fwer_cont(alpha=0.05, beta=0.1, p=2, frac=c(0.5, 1), delta0=0.178, delta1=0.545, nsim=15,seed=1)

Provides operating characteristics of group sequential MAMS trial for ordinal outcome under null hypothesis

Description

Computes FWER and other characteristics for group-sequential MAMS trial for ordinal outcome.

Usage

op_fwer_ord(alpha, beta, p, frac, or0, or, nsim, prob, seed)

Arguments

alpha

numeric Type I error.
beta

numeric Type II error.
p

numeric Number of treatment arms.
frac

numeric vector of fractions for information time at each look.
or0

numeric Odds ratio of ineffective treatment group vs control.
or

numeric Odds ratio of effective treatment group vs control.
nsim

numeric Number of simulations.
prob

numeric Probability of ordinal outcomes in control group.
seed

numeric Random seed number.

Value

A list of FWER, stage-wise type I error, average sample size used per arm, stopping probability, probability of futility.

Examples

op_fwer_ord(alpha = 0.05,
            beta = 0.1,
            p = 4,
            frac = c(0.5, 1),
            or0 = 1.32,
            or = 3.06,
            nsim = 15,
            prob = c(0.075, 0.182, 0.319, 0.243, 0.015, 0.166),
            seed = 13)

Provides operating characteristics of group sequential MAMS trial for survival outcome under null hypothesis

Description

Computes FWER and other characteristics for group-sequential MAMS trial for survival outcome.

Usage

op_fwer_surv(
  m0,
  alpha,
  beta,
  p,
  frac,
  hr0,
  hr1,
  nsim,
  ta,
  tf,
  kappa,
  eta,
  seed
)

Arguments

m0

numeric Median survival time in control group.
alpha

numeric Type I error.
beta

numeric Type II error.
p

numeric Number of treatment arms.
frac

numeric Vector of fractions for information time at each look.
hr0

numeric Hazard ratio of ineffective treatment group vs control.
hr1

numeric Hazard ratio of effective treatment group vs control.
nsim

numeric Number of simulations.
ta

numeric Accrual time.
tf

numeric Follow-up time.
kappa

numeric Shape parameter (Kappa=1 for exponential distribution).
eta

numeric Rate of loss to follow-up.
seed

numeric Random seed number.

Value

A list of FWER, stage-wise type I error, stopping probability, probability of futility, average number of events happened per arm, average duration of trial.

Examples

op_fwer_surv(m0 = 20,
             alpha = 0.05,
             beta = 0.1,
             p = 4,
             frac = c(1 / 2, 1),
             hr0 = 1,
             hr1 = 0.75,
             nsim = 12,
             ta = 40,
             tf = 20,
             kappa = 1,
             eta = 0,
             seed = 12)

Provides operating characteristics of group sequential MAMS trial for continuous outcome

Description

Computes power and other characteristics for group-sequential MAMS trial for continuous outcome.

Usage

op_power_cont(alpha, beta, p, frac, delta0, delta1, nsim, seed)

Arguments

alpha

numeric Type I error.
beta

numeric Type II error.
p

numeric Number of treatment arms.
frac

numeric Vector of fractions for information time at each look.
delta0

numeric Standardized effect size in ineffective arm.
delta1

numeric Standardized effect size in effective arm.
nsim

numeric Number of simulations.
seed

numeric Random seed number.

Value

A list of power, stage-wise probability of success, average sample size used per arm, stopping probability, probability of futility.

Examples

op_power_cont(alpha = 0.05,
              beta = 0.1,
              p = 4,
              frac = c(1 / 5, 2 / 5, 3 / 5, 4 / 5, 1),
              delta0 = 0.178,
              delta1 = 0.545,
              nsim = 12,
              seed = 12)

Provides operating characteristics of group sequential MAMS trial for ordinal outcome

Description

Computes power and other characteristics for group-sequential MAMS trial for ordinal outcome.

Usage

op_power_ord(alpha, beta, p, frac, or0, or, nsim, prob, seed)

Arguments

alpha

numeric Type I error.
beta

numeric Type II error.
p

numeric Number of treatment arms.
frac

numeric Vector of fractions for information time at each look.
or0

numeric Odds ratio of ineffective treatment group vs control.
or

numeric Odds ratio of effective treatment group vs control.
nsim

numeric Number of simulations.
prob

numeric Probability of ordinal outcomes in control group.
seed

numeric Random seed number.

Value

A list of power, stage-wise probability of success, average sample size used per arm, stopping probability, probability of futility.

Examples

op_power_ord(alpha = 0.05,
             beta = 0.1,
             p = 4,
             frac = c(0.5, 1),
             or0 = 1.32,
             or = 3.06,
             nsim = 12,
             prob = c(0.075, 0.182, 0.319, 0.243, 0.015, 0.166),
             seed = 13)

Provides operating characteristics of group sequential MAMS trial for survival outcome

Description

Computes power and other characteristics for group-sequential MAMS trial for survival outcome.

Usage

op_power_surv(
  m0,
  alpha,
  beta,
  p,
  frac,
  hr0,
  hr1,
  nsim,
  ta,
  tf,
  kappa,
  eta,
  seed
)

Arguments

m0

numeric Median survival time of control group.
alpha

numeric Type I error.
beta

numeric Type II error.
p

numeric Number of treatment arms.
frac

numeric Vector of fractions for information time at each look.
hr0

numeric Hazard ratio of ineffective treatment group vs control.
hr1

numeric Hazard ratio of effective treatment group vs control.
nsim

numeric Number of simulations.
ta

numeric Accrual time.
tf

numeric Follow-up time.
kappa

numeric Shape parameter (kappa=1 for exponential distribution).
eta

numeric Rate of loss to follow-up.
seed

numeric Random seed number.

Value

A list of power, stage-wise probability of success, stopping probability, probability of futility, average number of events happened per arm, average duration of trial.

Examples

op_power_surv(m0 = 20,
              alpha = 0.05,
              beta = 0.1,
              p = 4,
              frac = c(1 / 2, 1),
              hr0 = 1,
              hr1 = 0.74,
              ta = 12,
              tf = 40,
              nsim = 20,
              kappa = 1,
              eta = 0,
              seed = 12)