TwoSampleBinary: Functions to Support the Two Sample Binary Scenario

In lylyf1987/GNGpkg:

Description

make.ts.bin.ppp: Make Two Sample Binary Prior/Posterior Plot. Returns a ggplot object.

make.ts.bin.spp: Make Two Sample Binary Shaded Posterior Plot. Returns a graphic built using grid.arrange.

get.ts.bin.trt.oc.df: Get Two Sample Binary Treatment Effect OC. Returns a data.frame.

make.ts.bin.trt.oc1: Make Two Sample Binary Treatment Effect. Returns a graphic built using grid.arrange.

make.ts.bin.trt.oc2: Make Two Sample Binary Treatment Effect. Returns a graphic built using grid.arrange.

get.ts.bin.ssize.oc.df: Get Two Sample Binary sample size OC data.frame. Returns a data.frame.

make.ts.bin.ssize.oc: Make Two Sample Binary Sample size OC plot

Usage

make.ts.bin.ppp(
  a.con = 1,
  b.con = 1,
  n.con = 40,
  x.con = 5,
  a.trt = 1,
  b.trt = 1,
  n.trt = 40,
  x.trt = 20
)

get.ts.bin.decision(
  a.con = 1,
  b.con = 1,
  n.con = 40,
  x.con = 5,
  a.trt = 1,
  b.trt = 1,
  n.trt = 40,
  x.trt = 20,
  Delta.tv = 0.25,
  Delta.lrv = 0.2,
  tau.tv = 0.1,
  tau.lrv = 0.8,
  tau.ng = 0.65
)

get.ts.bin.decision.df(
  a.con = 1,
  b.con = 1,
  n.con = 40,
  x.con = 0:40,
  a.trt = 1,
  b.trt = 1,
  n.trt = 40,
  x.trt = 0:40,
  Delta.tv = 0.25,
  Delta.lrv = 0.2,
  tau.tv = 0.1,
  tau.lrv = 0.8,
  tau.ng = 0.65
)

make.ts.bin.spp(
  a.con = 4,
  b.con = 36,
  n.con = 40,
  x.con = 4,
  a.trt = 1,
  b.trt = 1,
  n.trt = 40,
  x.trt = 14,
  Delta.lrv = 0.3,
  Delta.tv = 0.4,
  tau.tv = 0.1,
  tau.lrv = 0.8,
  tau.ng = 0.8,
  nlines.ria = 20,
  tsize = 4,
  nlines = 25
)

get.ts.bin.trt.oc.df(
  a.con = 1,
  b.con = 1,
  dcurve.con = 0.12,
  a.trt = 1,
  b.trt = 1,
  TE.OC.N = 80,
  Aratio = 1,
  TE.OC.Delta.LB = 0,
  TE.OC.Delta.UB = 1 - 0.12,
  Delta.tv = 0.35,
  Delta.lrv = 0.2,
  tau.tv = 0.01,
  tau.lrv = 0.8,
  tau.ng = 0.65
)

make.ts.bin.trt.oc1(my.df = get.ts.bin.trt.oc.df(), nlines = 25, tsize = 4)

make.ts.bin.trt.oc2(my.df = get.ts.bin.trt.oc.df(), tsize = 4, nlines = 25)

get.ts.bin.ssize.oc.df(
  a.con = 1,
  b.con = 1,
  a.trt = 1,
  b.trt = 1,
  dcurve.con = 0.12,
  TE.OC.N = 80,
  Aratio = 2,
  SS.OC.N.LB = 40,
  SS.OC.N.UB = 160,
  Delta.lrv = 0.2,
  Delta.tv = 0.25,
  SS.OC.Delta = 0.25,
  tau.tv = 0.1,
  tau.lrv = 0.8,
  tau.ng = 0.65,
  Delta.user = 0.3,
  nlines = 15,
  tsize = 4,
  npoints = 3
)

make.ts.bin.ssize.oc(
  for.plot = get.ts.bin.ssize.oc.df(),
  tsize = 4,
  nlines = 25,
  npoints = 5
)

Arguments

a.con	prior alpha parameter for control group
b.con	prior beta parameter for control group
n.con	sample size for control group
x.con	number of responders for control group
a.trt	prior alpha parameter for treatment group
b.trt	prior beta parameter for treatment group
n.trt	sample size for control treatment group
x.trt	number of responders for treatment group
Delta.tv	TPP Target Value aka Base TPP
Delta.lrv	TPP Lower Reference Value aka Min TPP
tau.tv	threshold associated with Base TPP
tau.lrv	threshold associated with Min TPP
tau.ng	threshold associated with No-Go
nlines.ria	Control for text spacing
tsize	Control for text size
nlines	Control for text spacing
dcurve.con	response rate assumed for control group
TE.OC.N	total sample size for treatment effect OC
Aratio	Allocation ratio
SS.OC.N.LB	sample size lower bound
SS.OC.N.UB	sample size upper bound
SS.OC.Delta	user's TPP
npoints	number of points for sample size OC curve
seed	random seed

Examples

## Not run: 
make.ts.bin.ppp()
make.ts.bin.spp()
get.ts.bin.trt.oc.df()
make.ts.bin.trt.oc1()
make.ts.bin.trt.oc2()
get.ts.bin.ssize.oc.df()
make.ts.bin.ssize.oc()

## End(Not run)

lylyf1987/GNGpkg documentation built on May 19, 2020, 12:07 a.m.