ssanv.package: Sample Size Adjusted for Nonadherence or Variability of Input...
In ssanv: Sample Size Adjusted for Nonadherence or Variability of Input Parameters

Description Details Author(s) References Examples

Calculate sample size for two sample difference in means tests. The function, ss.nonadh accounts for known proportions of nonadherence. There are three functions that calculate sample size accounting for the variability that comes from estimating parameters from data.

Package:	ssanv
Type:	Package
Version:	1.1
Date:	2015-06-22
License:	GPL3

This package does adjustments for sample size calculations for two-sample difference in means tests. The function ss.nonadh adjusts for nonadherence, where the responses may be continuous, discrete, or binary. There are 3 functions which account for variability of estimating parameters from data: ss.fromdata.nvar– accouts for variability of standard deviation estimate when data are normal and the clincially significant difference is known, ss.fromdata.neff– accounts for variability of estimated standardized difference in means when both groups come from normal distributions with the same variance, ss.fromdata.pois– accounts for variability of means when both means are estimated from data assumed Poisson.

Michael Fay <mfay@niaid.nih.gov>

Maintainer: Michael Fay <mfay@niaid.nih.gov>

Fay, M.P., Halloran, M.E., and Follmann, D.A. (2007). 'Accounting for Variability in Sample Size Estimation with Applications to Nonadherence and Estimation of Variance and Effect Size' Biometrics 63: 465-474.

### Suppose treatment will not work on about 30 percent of subjects 
### then rho0=.3 and clinically significant difference in means is .5 
ss.nonadh(delta=.5,rho0=.3)

### Suppose you estimate standard deviation of .32 from a sample of size 12
### and the clinically significant difference is .2
ss.fromdata.nvar(delta=.2,sdhat=.32,df=11)
### The usual method is to assume that the standard deviation is known 
### this result is the same as df=Inf
ss.fromdata.nvar(delta=.2,sdhat=.32,df=Inf)