cprm.power: Chronic progressive repeated measures (CPRM) model sample...
In longpower: Sample Size Calculations for Longitudinal Data

cprm.power

R Documentation

Chronic progressive repeated measures (CPRM) model sample size calculations.

Description

This function performs sample size calculations for the chronic progressive repeated measures (CPRM) model when used to test for differences of change scores between groups at last visit. Input parameters are random effect variance and residual error variance as estimated by a REML fit to representative pilot data or data from a representative prior clinical trial or cohort study.

Usage

cprm.power(
  n = NULL,
  delta = NULL,
  power = NULL,
  t = NULL,
  lambda = 1,
  sig2.int = 0,
  sig2.s = NULL,
  sig.b0b1 = 0,
  sig2.e = NULL,
  sig2.int_2 = NULL,
  sig2.s_2 = NULL,
  sig.b0b1_2 = NULL,
  sig2.e_2 = NULL,
  sig.level = 0.05,
  p = NULL,
  p_2 = NULL,
  alternative = c("two.sided", "one.sided"),
  tol = NULL
)

Arguments

`n`	sample size, group 1
`delta`	group difference in fixed effect slopes
`power`	power
`t`	the observation times
`lambda`	allocation ratio (sample size group 1 divided by sample size group 2)
`sig2.int`	variance of random intercepts, group 1
`sig2.s`	variance of random slopes, group 1
`sig.b0b1`	covariance of random slopes and intercepts,group 1
`sig2.e`	residual variance, group 1
`sig2.int_2`	variance of random intercepts, group 2 (defaults to `sig2.int`)
`sig2.s_2`	variance of random slopes, group 2 (defaults to `sig2.s`)
`sig.b0b1_2`	covariance of random slopes and intercepts, group 2 (defaults to `sig.b0b1`)
`sig2.e_2`	residual variance, group 2 (defaults to `sig2.e`)
`sig.level`	type one error
`p`	proportion vector for group 1, if i indexes visits, `p[i]` = the proportion whose last visit was at visit `i` (`p` sums to `1`)
`p_2`	proportion vector for group 2 (defaults to `p`)
`alternative`	one- or two-sided test
`tol`	not used (no root finding used in this implementation).

Details

Default settings perform sample size / power / effect size calculations assuming equal covariance of repeated measures in the 2 groups, equal residual error variance across groups, equal allocation to groups, and assuming no study subject attrition. Specifically, variance parameters required for default settings are sig2.s, the variance of random slopes, and sig2.e, the residual error variance, both either known or estimated from a mixed model fit by REML to prior data.

This function accommodates different variance parameters across groups, unequal allocation across groups, and study subject attrition (loss to followup), which may also vary across groups. Details can be found in the description of edland.linear.power

Value

One of the number of subject required per arm, the power, or detectable effect size given sig.level and the other parameter estimates.

Author(s)

Steven D. Edland, Yu Zhao

References

Zhao Y, Edland SD. The chronic progressive repeated measures (CPRM) model for longitudinal data. In process.

Examples


## Not run: 
browseVignettes(package = "longpower")

## End(Not run)
# An Alzheimer's Disease example using ADAS-cog pilot estimates
t <- seq(0,1.5,0.25)
cprm.power(delta=1.5, t=t, sig2.s = 24, sig2.e = 10, sig.level=0.05, power = 0.80)

longpower documentation built on Sept. 11, 2024, 5:32 p.m.