bate {prome}

Bayesian Hierarchical Model for RPO data with repeated measures

Description

A Bayesian hierachical model to denoise PRO data using repeated measures.

Usage

bate(x0,x1,group,z,x.range,...)
ResponderAnalysis(x,mcid,type="absolute",conf.level=0.95,show=TRUE)

Arguments

x0, x1

Numeric vector/matrix of observations at T0 (baseline) and T1 (end point) of a study.

z

covariates

group

group assignments. Current version support one or two groups only

x.range

range of data 'x0' and 'x1'

x

An R object generated by memixed

mcid

A threshold to define 'responder'

type

The type of responder analysis: absolute or relative changes

conf.level

Confidence level of the credible interval

show

control whether results should be displayed

...

Parameters ("adapt_delta","stepsize","max_treedepth") to improve model fitting/convergence.

Value

• 'xfit': fitted results using stan.

• 'mu.t0': baseline mean.

• 'sig.t0': baseline SD.

• 'sig.me': SD of measurement errors.

• 'mu.active': mean effect size of active treatment.

• 'sig.active': sd of effect size of active treatment.

• 'mu.sham': mean effect size of sham treatment.

• 'sig.sham': sd of effect size of sham treatment.

Examples

data(n100x3)
out1  <-  bate(x0=ex100x3$w0,x1=ex100x3$w1,group=ex100x3$group)
out1
ResponderAnalysis(out1,mcid=1,type="abs")
out2  <-  bate(x0=ex100x3$w0,x1=ex100x3$w1,group=ex100x3$group,
    control = list(adapt_delta = 0.8,
               stepsize = 5,
               max_treedepth = 10)
)
out2
ResponderAnalysis(out2,mcid=1,type="abs")
out <- out2
ResponderAnalysis(out,mcid=0.5,type="abs")
ResponderAnalysis(out,mcid=1,type="abs")
ResponderAnalysis(out,mcid=1.5,type="abs")
ResponderAnalysis(out,mcid=0.3,type="relative")
ResponderAnalysis(out,mcid=0.2,type="relative")
ResponderAnalysis(out,mcid=0.1,type="relative")