TippingPoint

In TippingPoint: Enhanced Tipping Point Displays the Results of Sensitivity Analysis for Missing Data

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A manual to show the R package TippingPoint.

Introduction

The TippingPoint package aims to handle missing outcome data by listing out all the possible combinations of missing values in two treatment arms, calculating the corresponding estimated treatment effects and p-values, finding the margin (the so-called Tipping Point) that would change the result of the study, and drawing a colored heat-map to visualize them. In addition, the package provides a visualized method to compare various imputation methods by adding the rectangles or convex hulls on the basic plot. Some examples are displayed below to explain the idea.

# Change the width of html file
options(width = 150)

# the package can be downloaded from cran and github:

# install.packages("TipingPoint")

# devtools::install_github("XikunHan/TippingPoint")


library(TippingPoint)

# Load the dataset

data(tippingdata)

# Show the first 6 rows of the data

head(tippingdata)

Basic plot

## for binary outcome


# Using `estimate`
TippingPoint(outcome=tippingdata$binary,
             treat= tippingdata$treat,group.infor=TRUE,
             plot.type = "estimate",ind.values = TRUE,
             impValuesT  = NA,  impValuesC = NA,
             summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(0.38,0.4), HistMeanC =  c(0.2,0.55))

# Using `p.value` with formula class
TippingPoint(binary~treat, data=tippingdata,
             plot.type = "p.value",ind.values = TRUE,
             impValuesT  = NA,  impValuesC = NA,
             summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(0.38,0.4), HistMeanC =  c(0.2,0.55))

# Using `both` 
TippingPoint(outcome=tippingdata$binary,treat= tippingdata$treat,
             plot.type = "both",ind.values = TRUE,
             impValuesT  = NA,  impValuesC = NA,
             summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(0.38,0.4), HistMeanC =  c(0.2,0.55))



# for continuous outcome
TippingPoint(continuous~treat, data=tippingdata,
             group.infor=TRUE, plot.type = "estimate",ind.values = TRUE,
             impValuesT  = NA,  impValuesC = NA,
             summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(120), HistMeanC =  c(131,137))

TippingPoint(outcome=tippingdata$continuous,treat= tippingdata$treat,
             plot.type = "p.value",ind.values = TRUE,
             impValuesT  = NA,  impValuesC = NA,
             summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(120), HistMeanC =  c(131,137))

TippingPoint(outcome=tippingdata$continuous,treat= tippingdata$treat,
             plot.type = "both",ind.values = TRUE,
             impValuesT  = NA,  impValuesC = NA,
             summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(120), HistMeanC =  c(131,137))

Using imputed data

# Load the imputed dataset

data(imputedata)

# Show the first 6 rows of the data

head(imputedata)


## for binary outcome

TippingPoint(outcome=tippingdata$binary,
             treat= tippingdata$treat, group.infor=TRUE,
             plot.type = "estimate",ind.values = TRUE,
             impValuesT  = imputedata[,c("MAR_T2","MCAR_T2")],  
             impValuesC = imputedata[,c("MAR_C2","MCAR_C2")],
             summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(0.38,0.4), HistMeanC =  c(0.2,0.55))


# User-defined colors
TippingPoint(outcome=tippingdata$binary,treat= tippingdata$treat,
             plot.type = "p.value",ind.values = TRUE,
             impValuesT  = imputedata[,c("MAR_T2","MCAR_T2")],  
             impValuesC = imputedata[,c("MAR_C2","MCAR_C2")],
             impValuesColor = RColorBrewer::brewer.pal(8,"Accent")[5:6],
             summary.type = "credible.region", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(0.38,0.4), HistMeanC =  c(0.2,0.55))

# Using `point.size` and `point.shape` to control the points.
TippingPoint(outcome=tippingdata$binary,treat= tippingdata$treat,
             plot.type = "both",ind.values = TRUE,
             impValuesT  = imputedata[,c("MAR_T2","MCAR_T2")],  
             impValuesC = imputedata[,c("MAR_C2","MCAR_C2")],
             impValuesColor =c("red","blue"),
             point.size=0.8,point.shape = 15,
             summary.type = "convex.hull", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(0.38,0.4), HistMeanC =  c(0.2,0.55))



## for continuous outcome
TippingPoint(outcome=tippingdata$continuous,
             treat= tippingdata$treat, group.infor=TRUE,
             plot.type = "p.value",ind.values = TRUE,
             impValuesT  = imputedata[,c("MAR_T1","MCAR_T1")],  
             impValuesC = imputedata[,c("MAR_C1","MCAR_C1")],
             summary.type = "density", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(120), HistMeanC =  c(131,137))


# Using `point.size` and `point.shape` to control the points.
TippingPoint(outcome=tippingdata$continuous,treat= tippingdata$treat,
             plot.type = "p.value",ind.values = TRUE,
             impValuesT  = imputedata[,c("MAR_T1","MCAR_T1")],  
             impValuesC = imputedata[,c("MAR_C1","MCAR_C1")],
             point.size = 0.8, point.shape = 15,
             summary.type = "credible.region", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(120), HistMeanC =  c(131,137))

TippingPoint(outcome=tippingdata$continuous,treat= tippingdata$treat,
             plot.type = "both",ind.values = TRUE,
             impValuesT  = imputedata[,c("MAR_T1","MCAR_T1")],  
             impValuesC = imputedata[,c("MAR_C1","MCAR_C1")],
             summary.type = "convex.hull", alpha = 0.95, S=1.5, n.grid = 100,
             HistMeanT = c(120), HistMeanC =  c(131,137))

Reference

The TippingPoint package is created based on Liublinska and Rubin's work. For more details:

• Liublinska, V. and Rubin, D.B. Enhanced Tipping-Point Displays. In JSM Proceedings, Section on Survey Research Methods, San Diego, CA: American Statistical Association. 3861-3686 (2012).
• Liublinska, V. & Rubin, D.B. Sensitivity analysis for a partially missing binary outcome in a two-arm randomized clinical trial. Stat Med 33, 4170-85 (2014).
• Liublinska, V. (May, 2013) Sensitivity Analyses in Empirical Studies Plagued with Missing Data. PhD Dissertation, Harvard University, https://dash.harvard.edu/handle/1/11124841
• https://sites.google.com/site/vliublinska/research

TippingPoint documentation built on April 8, 2022, 5:08 p.m.