survm_effectsize {survmixer}

Effect size calculation for mixture survival distributions

Description

The function 'survm_effectsize' calculates the effect size in terms of the difference of restricted mean survival times (RMST) according to the information on responders and non-responders.

Usage

survm_effectsize(
  ascale0_r,
  ascale0_nr,
  delta_p,
  p0,
  bshape0 = 1,
  bshape1 = 1,
  ascale1_r,
  ascale1_nr,
  tau,
  Delta_r = NULL,
  Delta_0 = NULL,
  Delta_nr = NULL,
  anticipated_effects = FALSE
)

Arguments

ascale0_r

scale parameter for the Weibull distribution in the control group for responders

ascale0_nr

scale parameter for the Weibull distribution in the control group for non-responders

delta_p

effect size for the response rate

p0

event rate for the response

bshape0

shape parameter for the Weibull distribution in the control group

bshape1

shape parameter for the Weibull distribution in the intervention group

ascale1_r

scale parameter for the Weibull distribution in the intervention group for responders

ascale1_nr

scale parameter for the Weibull distribution in the intervention group for non-responders

tau

follow-up

Delta_r

RMST difference between intervention and control groups for responders

Delta_0

RMST difference between responders and non-responders in the control group

Delta_nr

RMST difference between intervention and control groups for non-responders

anticipated_effects

Logical parameter. If it is TRUE then the effect size is computed based on previous information on the effect sizes on response rate and survival-by-responses (that is, based on Delta_r, Delta_0, Delta_nr); otherwise is based on the distributional parameters (ascale0_r, ascale0_nr, ascale1_r, ascale1_nr, bshape0, bshape1).

Value

This function returns the overall mean survival improvement (RMST difference between groups) and it also includes the mean survival improvement that would be assumed for each responders and non-responders.

Author(s)

Marta Bofill Roig.

References

Design of phase III trials with long-term survival outcomes based on short-term binary results. Marta Bofill Roig, Yu Shen, Guadalupe Gomez Melis. arXiv:2008.12887

Examples

survm_effectsize(ascale0_r=8,ascale0_nr=5.6,ascale1_r=36,ascale1_nr=5.6,delta_p=0.2,p0=0.2,tau=5)