Multiple Imputation and its Application 2nd Edition by James Carpenter, Jonathan Bartlett, Tim Morris, Angela Wood, Matteo Quartagno, Michael Kenward 1119756081 9781119756088

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ISBN 10: 1119756081

ISBN 13: 9781119756088

Author: James R Carpenter, Jonathan W Bartlett, Tim P Morris, Angela M Wood, Matteo Quartagno, Michael G Kenward

In this comprehensively revised Second Edition of Multiple Imputation and its Application, a team of distinguished statisticians delivers an overview of the issues raised by missing data, the rationale for multiple imputation as a solution, and the practicalities of applying it in a multitude of settings.

Multiple Imputation and its Application 2nd Table of contents:

Part I: Foundations

1 Introduction

1.1 Reasons for missing data

1.2 Examples

1.3 Patterns of missing data

1.4 Inferential framework and notation

1.5 Using observed data to inform assumptions about the missingness mechanism

1.6 Implications of missing data mechanisms for regression analyses

1.7 Summary

Exercises

2 The multiple imputation procedure and its justification

2.1 Introduction

2.2 Intuitive outline of the MI procedure

2.3 The generic MI procedure

2.4 Bayesian justification of MI

2.5 Frequentist inference

2.6 Choosing the number of imputations

2.7 Some simple examples

2.8 MI in more general settings

2.9 Constructing congenial imputation models

2.10 Discussion

Exercises

Part II: Multiple Imputation for Simple Data Structures

3 Multiple imputation of quantitative data

3.1 Regression imputation with a monotone missingness pattern

3.2 Joint modelling

3.3 Full conditional specification

3.4 Full conditional specification versus joint modelling

3.5 Software for multivariate normal imputation

3.6 Discussion

Exercises

4 Multiple imputation of binary and ordinal data

4.1 Sequential imputation with monotone missingness pattern

4.2 Joint modelling with the multivariate normal distribution

4.3 Modelling binary data using latent normal variables

4.4 General location model

4.5 Full conditional specification

4.6 Issues with over‐fitting

4.7 Pros and cons of the various approaches

4.8 Software

4.9 Discussion

Exercises

Note

5 Imputation of unordered categorical data

5.1 Monotone missing data

5.2 Multivariate normal imputation for categorical data

5.3 Maximum indicant model

5.4 General location model

5.5 FCS with categorical data

5.6 Perfect prediction issues with categorical data

5.7 Software

5.8 Discussion

Exercises

Part III: Multiple Imputation in Practice

6 Non‐linear relationships, interactions, and other derived variables

6.1 Introduction

6.2 No missing data in derived variables

6.3 Simple methods

6.4 Substantive‐model‐compatible imputation

6.5 Returning to the problems

Exercises

7 Survival data

7.1 Missing covariates in time‐to‐event data

7.2 Imputing censored event times

7.3 Non‐parametric, or ‘hot deck’ imputation

7.4 Case–cohort designs

7.5 Discussion

Exercises

8 Prognostic models, missing data, and multiple imputation

8.1 Introduction

8.2 Motivating example

8.3 Missing data at model implementation

8.4 Multiple imputation for prognostic modelling

8.5 Model building

8.6 Model performance

8.7 Model validation

8.8 Incomplete data at implementation

Exercises

9 Multi‐level multiple imputation

9.1 Multi‐level imputation model

9.2 MCMC algorithm for imputation model

9.3 Extensions

9.4 Other imputation methods

9.5 Individual participant data meta‐analysis

9.6 Software

9.7 Discussion

Exercises

10 Sensitivity analysis: MI unleashed

10.1 Review of MNAR modelling

10.2 Framing sensitivity analysis: estimands

10.3 Pattern mixture modelling with MI

10.4 Pattern mixture approach with longitudinal data via MI

10.5 Reference based imputation

10.6 Approximating a selection model by importance weighting

10.7 Discussion

Exercises

11 Multiple imputation for measurement error and misclassification

11.1 Introduction

11.2 Multiple imputation with validation data

11.3 Multiple imputation with replication data

11.4 External information on the measurement process

11.5 Discussion

Exercises

12 Multiple imputation with weights

12.1 Using model‐based predictions in strata

12.2 Bias in the MI variance estimator

12.3 MI with weights

12.4 A multi‐level approach

12.5 Further topics

12.6 Discussion

Exercises

Note

13 Multiple imputation for causal inference

13.1 Multiple imputation for causal inference in point exposure studies

13.2 Multiple imputation and propensity scores

13.3 Principal stratification via multiple imputation

13.4 Multiple imputation for IV analysis

13.5 Discussion

Exercises

14 Using multiple imputation in practice

14.1 A general approach

14.2 Objections to multiple imputation

14.3 Reporting of analyses with incomplete data

14.4 Presenting incomplete baseline data

14.5 Model diagnostics

14.6 How many imputations?

14.7 Multiple imputation for each substantive model, project, or dataset?

14.8 Large datasets

14.9 Multiple imputation and record linkage

14.10 Setting random number seeds for multiple imputation analyses

14.11 Simulation studies including multiple imputation

14.12 Discussion

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