Details

Autor(en) / Beteiligte

• Carpenter, James R.,

Titel

Multiple Imputation and Its Application

Auflage

Second edition

Ort / Verlag

Chichester, England : John Wiley & Sons Ltd,

Erscheinungsjahr

[2023]

Link zum Volltext

• Wiley Online Library - AutoHoldings Books

Beschreibungen/Notizen

• Includes bibliographical references and index.
• Cover -- Title Page -- Copyright -- Contents -- Part I FOUNDATIONS -- Chapter 1 Introduction -- 1.1 Reasons for missing data -- 1.2 Examples -- 1.3 Patterns of missing data -- 1.3.1 Consequences of missing data -- 1.4 Inferential framework and notation -- 1.4.1 Missing completely at random (MCAR) -- 1.4.2 Missing at random (MAR) -- 1.4.3 Missing not at random (MNAR) -- 1.4.4 Ignorability -- 1.5 Using observed data to inform assumptions about the missingness mechanism -- 1.6 Implications of missing data mechanisms for regression analyses -- 1.6.1 Partially observed response -- 1.6.2 Missing covariates -- 1.6.3 Missing covariates and response -- 1.6.4 Subtle issues I: the odds ratio -- 1.6.5 Implication for linear regression -- 1.6.6 Subtle issues II: sub‐sample ignorability -- 1.6.7 Summary: when restricting to complete records is valid -- Summary -- Exercises -- Chapter 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 -- 2.5.1 Large number of imputations -- 2.5.2 Small number of imputations -- 2.5.3 Inference for vector &amp -- bfitbeta -- -- 2.5.4 Combining likelihood ratio tests -- 2.6 Choosing the number of imputations -- 2.7 Some simple examples -- 2.7.1 Estimating the mean with σ2 known by the imputer and analyst -- 2.7.2 Estimating the mean with σ2 known only by the imputer -- 2.7.3 Estimating the mean with σ2 unknown -- 2.7.4 General linear regression with σ2 known -- 2.8 MI in more general settings -- 2.8.1 Proper imputation -- 2.8.2 Congenial imputation and substantive model -- 2.8.3 Uncongenial imputation and substantive models -- 2.8.4 Survey sample settings -- 2.9 Constructing congenial imputation models -- Discussion -- Exercises.
• Part II MULTIPLE IMPUTATION FOR SIMPLE DATA STRUCTURES -- Chapter 3 Multiple imputation of quantitative data -- 3.1 Regression imputation with a monotone missingness pattern -- 3.1.1 MAR mechanisms consistent with a monotone pattern -- 3.1.2 Justification -- 3.2 Joint modelling -- 3.2.1 Fitting the imputation model -- 3.2.2 Adding covariates -- 3.3 Full conditional specification -- 3.3.1 Justification -- 3.4 Full conditional specification versus joint modelling -- 3.5 Software for multivariate normal imputation -- 3.6 Discussion -- 3.6 Exercises -- Chapter 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 -- 4.3.1 Latent normal model for ordinal data -- 4.4 General location model -- 4.5 Full conditional specification -- 4.5.1 Justification -- 4.6 Issues with over‐fitting -- 4.7 Pros and cons of the various approaches -- 4.8 Software -- Discussion -- Exercises -- Chapter 5 Imputation of unordered categorical data -- 5.1 Monotone missing data -- 5.2 Multivariate normal imputation for categorical data -- 5.3 Maximum indicant model -- 5.3.1 Continuous and categorical variable -- 5.3.2 Imputing missing data -- 5.4 General location model -- 5.5 FCS with categorical data -- 5.6 Perfect prediction issues with categorical data -- 5.7 Software -- Discussion -- Exercises -- Part III Multiple imputation in practice -- Chapter 6 Non‐linear relationships, interactions, and other derived variables -- 6.1 Introduction -- 6.1.1 Interactions -- 6.1.2 Squares -- 6.1.3 Ratios -- 6.1.4 Sum scores -- 6.1.5 Composite endpoints -- 6.2 No missing data in derived variables -- 6.3 Simple methods -- 6.3.1 Impute then transform -- 6.3.2 Transform then impute/just another variable.
• 6.3.3 Adapting standard imputation models and passive imputation -- 6.3.4 Predictive mean matching -- 6.3.5 Imputation separately by groups for interactions -- 6.4 Substantive‐model‐compatible imputation -- 6.4.1 The basic idea -- 6.4.2 Latent‐normal joint model SMC imputation -- 6.4.3 Factorised conditional model SMC imputation -- 6.4.4 Substantive model compatible fully conditional specification -- 6.4.5 Auxiliary variables -- 6.4.6 Missing outcome values -- 6.4.7 Congeniality versus compatibility -- 6.4.8 Discussion of SMC imputation -- 6.5 Returning to the problems -- 6.5.1 Ratios -- 6.5.2 Splines -- 6.5.3 Fractional polynomials -- 6.5.4 Multiple imputation with conditional questions or 'skips' -- Exercises -- Chapter 7 Survival data -- 7.1 Missing covariates in time‐to‐event data -- 7.1.1 Approximately compatible approaches -- 7.1.2 Substantive model compatible approaches -- 7.2 Imputing censored event times -- 7.3 Non‐parametric, or 'hot deck' imputation -- 7.3.1 Non‐parametric imputation for time‐to‐event data -- 7.4 Case-cohort designs -- 7.4.1 Standard analysis of case-cohort studies -- 7.4.2 Multiple imputation for case-cohort studies -- 7.4.3 Full cohort -- 7.4.4 Intermediate approaches -- 7.4.5 Sub‐study approach -- Discussion -- Exercises -- Chapter 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 -- 8.5.1 Model building with missing data -- 8.5.2 Imputing predictors when model building is to be performed -- 8.6 Model performance -- 8.6.1 How should we pool MI results for estimation of performance? -- 8.6.2 Calibration -- 8.6.3 Discrimination -- 8.6.4 Model performance measures with clinical interpretability -- 8.7 Model validation -- 8.7.1 Internal model validation.
• 8.7.2 External model validation -- 8.8 Incomplete data at implementation -- 8.8.1 MI for incomplete data at implementation -- 8.8.2 Alternatives to multiple imputation -- Exercises -- Chapter 9 Multi‐level multiple imputation -- 9.1 Multi‐level imputation model -- 9.1.1 Imputation of level‐1 variables -- 9.1.2 Imputation of level 2 variables -- 9.1.3 Accommodating the substantive model -- 9.2 MCMC algorithm for imputation model -- 9.2.1 Ordered and unordered categorical data -- 9.2.2 Imputing missing values -- 9.2.3 Substantive model compatible imputation -- 9.2.4 Checking model convergence -- 9.3 Extensions -- 9.3.1 Cross‐classification and three‐level data -- 9.3.2 Random level 1 covariance matrices -- 9.3.3 Model fit -- 9.4 Other imputation methods -- 9.4.1 One‐step and two‐step FCS -- 9.4.2 Substantive model compatible imputation -- 9.4.3 Non‐parametric methods -- 9.4.4 Comparisons of different methods -- 9.5 Individual participant data meta‐analysis -- 9.5.1 Different measurement scales -- 9.5.2 When to apply Rubin's rules -- 9.5.3 Homoscedastic versus heteroscedastic imputation model -- 9.6 Software -- Discussion -- Exercises -- Chapter 10 Sensitivity analysis: MI unleashed -- 10.1 Review of MNAR modelling -- 10.2 Framing sensitivity analysis: estimands -- 10.2.1 Definition of the estimand -- 10.2.2 Two common estimands -- 10.3 Pattern mixture modelling with MI -- 10.3.1 Missing covariates -- 10.3.2 Sensitivity with multiple variables: the NAR FCS procedure -- 10.3.3 Application to survival analysis -- 10.4 Pattern mixture approach with longitudinal data via MI -- 10.4.1 Change in slope post‐deviation -- 10.5 Reference based imputation -- 10.5.1 Constructing joint distributions of pre‐ and post‐intercurrent event data -- 10.5.2 Technical details -- 10.5.3 Software -- 10.5.4 Information anchoring.
• 10.6 Approximating a selection model by importance weighting -- 10.6.1 Weighting the imputations -- 10.6.2 Stacking the imputations and applying the weights -- Discussion -- Exercises -- Chapter 11 Multiple imputation for measurement error and misclassification -- 11.1 Introduction -- 11.2 Multiple imputation with validation data -- 11.2.1 Measurement error -- 11.2.2 Misclassification -- 11.2.3 Imputing assuming error is non‐differential -- 11.2.4 Non‐linear outcome models -- 11.3 Multiple imputation with replication data -- 11.3.1 Measurement error -- 11.3.2 Misclassification -- 11.4 External information on the measurement process -- Discussion -- Exercises -- Chapter 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 -- 12.3.1 Conditions for the consistency of &amp -- bfittheta -- &amp -- wHat -- MI -- 12.3.2 Conditions for the consistency of V&amp -- wHat -- MI -- 12.4 A multi‐level approach -- 12.4.1 Evaluation of the multi‐level multiple imputation approach for handling survey weights -- 12.4.2 Results -- 12.5 Further topics -- 12.5.1 Estimation in domains -- 12.5.2 Two‐stage analysis -- 12.5.3 Missing values in the weight model -- Discussion -- Exercises -- Chapter 13 Multiple imputation for causal inference -- 13.1 Multiple imputation for causal inference in point exposure studies -- 13.1.1 Randomised trials -- 13.1.2 Observational studies -- 13.2 Multiple imputation and propensity scores -- 13.2.1 Propensity scores for confounder adjustment -- 13.2.2 Multiple imputation of confounders -- 13.2.3 Imputation model specification -- 13.3 Principal stratification via multiple imputation -- 13.3.1 Principal strata effects -- 13.3.2 Estimation -- 13.4 Multiple imputation for IV analysis -- 13.4.1 Instrumental variable analysis for non‐adherence.
• 13.4.2 Instrumental variable analysis via multiple imputation.
• "Multiple imputation remains the most widely used methodology for missing data. Since the publication of the first edition, both MI methodology and the range of applications has continued to expand and develop. Methodological advances include extended MI methodologies for multilevel data and causal models, alongside important practical developments in sensitivity analysis. Key practical applications are clinical trials, prognostic modelling and causal modelling. Following on from the first edition, the authors here present the concepts in an intuitive way, setting out the issues raised by missing data, describing the rationale for MI, and show how it can be applied in increasingly complex settings with a range of examples. Also available for the first time are theoretical and computer-based exercises using Stata and R to help the instructor. Multiple Imputation and its Application, Second Edition is aimed at quantitative medical and social researchers by presenting the concepts in an intuitive way, illustrating with a range of examples. Alongside this, inclusion of key mathematical details, and theoretical and computer-based exercises will make the text suitable for graduate teaching and short courses"--
• Description based on print version record.