bootPRO: Effectiveness Evaluation based on PROs with bootstrapping...

In bda: Binned Data Analysis

Effectiveness Evaluation based on PROs with bootstrapping method

Description

Effectiveness Evaluation based on PROs with bootstrapping method.

Usage

 bootPRO(x,type="relative",MCID,iter=999,conf.level=0.95)

Arguments

x	Data frame of repeated PRO measures. First column gives the treatment groups: 'group' or 'treat'; Repeated measures listed after column 1.
type	use absolute changes ("absolute") or relative (percent) changes ("relative").
MCID	A positive value to define responders.
iter	number of iterations used by the bootstrap algorithm.
conf.level	Confidence level of the bootstrapping confidence interval.

Value

NONE.

References

To be updated.

Examples

  data(Pain)
  x <- pain[,c(2,3:9,11:17)]
  grp <- rep("treat",nrow(x))
  grp[x[,1]==0] <- "control"
  x[,1] <- grp
  #bootPRO(x,type='mean')
  #bootPRO(x,type='mean',MCID=1)
  #bootPRO(x,type='mean',MCID=1.5)
  #bootPRO(x,type='mean',MCID=2)

  #bootPRO(x,type='rel')
  #bootPRO(x,type='relative',MCID=.2)
  #bootPRO(x,type='relative',MCID=.3)
  ##bootPRO(x,type='relative',MCID=.5)
 

bda documentation built on Sept. 11, 2024, 9:01 p.m.