MISSUITE: A Shiny Application for Clinical Trial

Missing Data Analysis

GASP 2018

Chenguang Wang

Johns Hopkins University
Motivation
Missing data

• Missing data is ubiquitous in clinical trials

• Validity of statistical analysis results are threatened by

missing data

• Inference requires untestable assumptions about missing

data mechanism

• Rigorous sensitivity analyses examining sensitivity to

missing data mechanism assumptions are crucial and

should even be mandatory

1
Global sensitivity analysis

• Apply benchmark assumptions to identify the full data

model

• Consider deviations from the benchmark assumptions and

examine the robustness

• Exploring the basics of the missing data helps to design

the sensitivity analysis

2
Goal

• To develop a statistical software that is user-friendly with

interactive features

• To aid users to efficiently apply missing data imputation

methods in existing software packages

• To explore the nature of the missing data

• To serve as the first step of rigorous missing data

sensitivity analysis

3
Imputation Algorithms
General setting

• Z: treatment assignment

• X1, . . . XP: baseline covariates

• Y1, . . . , YK: post-randomization outcomes

• D = {D1, . . . , DJ} = {X1, . . . , Xp, Y1, . . . , YK}: all data

• M = {M1, . . . , MJ}: missing data indicator

• Dobs: observed data

• Dmis: missing data

• D−j = {D1, . . . , Dj−1, Dj+1, . . . DJ}

4
Missing at random

• M|D = M|Dobs

• Dmis|M, Dobs = Ymis|Dobs

5
Data type

• Binary

• Unordered−Categorical

• Ordered−Categorical

• Continuous

• Proportion

• Non-Negative

6
Multiple imputation software packages

• MICE: Multivariate Imputation by Chained Equations

• Amelia: A Program for Missing Data

• missForest: Nonparametric Missing Value Imputation using

Random Forest

• Hmisc: Harrell Miscellaneous

• mi: Missing Data Imputation and Model Checking

7
MICE

• Multiple imputation using Fully Conditional Specification

(FCS), also known as multiple imputation using chained

equations (MICE)

• Imputation models specified conditionally for each

variable

f(D1|D−1, θ1)

f(D2|D−2, θ2)

.

.

.

• At tth iteration

θ

(t)

j ∼ π(θj

|Dj,obs, D

(t−1)

−j

)

D

(t)

j,mis ∼ f(Dj

|D

(t−1)

−j

, θ(t)

j

)

8
Amelia

• Assume D ∼ N(μ, Σ)

• Imputation by EM with bootstrapping (EMB) algorithm

• Apply EM to find the mode of the posterior given the

bootstrapped sample

• Draw Dmis from f(Dmis|Dobs, μ, Σ)

• Ordinal data are considered continuous

• Nominal data are re-coded using dummy variables that

are further considered continuous

9
missForest

• An implementation of non-parametric random forest (RF)

algorithm

• For j, train an RF on the observed data Dobs,j|Dobs,−j, then

predict the missing values Dmis,j|Dmis,−j

• Proceed iteratively until convergence

• By averaging over trees, random forest intrinsically

constitutes a multiple imputation scheme

10
Hmisc

• A multiple purpose package for data analysis, graphics,

model fitting, etc.

• Provides function aregImpute for multiple imputation

using additive regression, bootstrapping, and predictive

mean matching

• continuous variables: restricted cubic splines

• categorical variables: Fisher’s optimum scoring method

• each imputation uses a different bootstrap sample

11
mi

• Also implements the chained equation approach

• Implements Bayesian imputation models such as

Bayesian generalized linear models

• Provide diagnostic tools for checking the fit of the

imputation models

12
Visualization
Software package

• VIM: Visualization and Imputation of Missing Values

• Different type of plots

• Aggregation plot

• Histogram

• Spinogram

• Marginal plot

• Scatter plot

• Jitter plot

• Matrix plot

• Spaghetti plot

13
Missuite
Shiny

• RStudio product

• A web application framework for R

• Turn R code into interactive web applications

• No HTML, CSS, or JavaScript knowledge required

14
Architecture

15
Access Missuite

• Demo on

https://olssol.shinyapps.io/missuite/

16
Discussion
Statistical software for regulatory applications

• Communication

• Efficiency

• Reproducible research

• Education

17
Additional example

• IDEM

• Composite Endpoint Death Truncated Data Analysis

• Available on CRAN

• Demo on https://olssol.shinyapps.io/idem/

18
The End.

18