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| Preface | |
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| Data acknowledgements | |
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| Acknowledgements | |
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| Glossary | |
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| Foundations | |
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| Introduction | |
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| Reasons for missing data | |
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| Examples | |
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| Patterns of missing data | |
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| Consequences of missing data | |
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| Inferential framework and notation | |
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| Missing Completely At Random (MCAR) | |
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| Missing At Random (MAR) | |
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| Missing Not At Random (MNAR) | |
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| Ignorability | |
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| Using observed data to inform assumptions about the missingness mechanism | |
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| Implications of missing data mechanisms for regression analyses | |
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| Partially observed response | |
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| Missing covariates | |
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| Missing covariates and response | |
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| Subtle issues I: The odds ratio | |
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| Implication for linear regression | |
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| Subtle issues II: Subsample ignorability | |
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| Summary: When restricting to complete records is valid | |
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| Summary | |
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| The multiple imputation procedure and its justification | |
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| Introduction | |
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| Intuitive outline of the MI procedure | |
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| The generic MI procedure | |
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| Bayesian justification of MI | |
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| Frequentist inference | |
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| Large number of imputations | |
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| Small number of imputations | |
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| Choosing the number of imputations | |
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| Some simple examples | |
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| MI in more general settings | |
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| Survey sample settings | |
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| Constructing congenial imputation models | |
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| Practical considerations for choosing imputation models | |
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| Discussion | |
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| Multiple Imputation for Cross Sectional Data | |
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| Multiple imputation of quantitative data | |
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| Regression imputation with a monotone missingness pattern | |
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| MAR mechanisms consistent with a monotone pattern | |
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| Justification | |
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| Joint modelling | |
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| Fitting the imputation model | |
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| Full conditional specification | |
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| Justification | |
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| Full conditional specification versus joint modelling | |
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| Software for multivariate normal imputation | |
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| Discussion | |
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| Multiple imputation of binary and ordinal data | |
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| Sequential imputation with monotone missingness pattern | |
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| Joint modelling with the multivariate normal distribution | |
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| Modelling binary data using latent normal variables | |
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| Latent normal model for ordinal data | |
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| General location model | |
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| Full conditional specification | |
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| Justification | |
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| Issues with over-fitting | |
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| Pros and cons of the various approaches | |
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| Software | |
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| Discussion | |
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| Multiple imputation of unordered categorical data | |
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| Monotone missing data | |
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| Multivariate normal imputation for categorical data | |
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| Maximum indicant model | |
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| Continuous and categorical variable | |
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| Imputing missing data | |
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| More than one categorical variable | |
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| General location model | |
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| FCS with categorical data | |
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| Perfect prediction issues with categorical data | |
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| Software | |
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| Discussion | |
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| Nonlinear relationships | |
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| Passive imputation | |
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| No missing data in nonlinear relationships | |
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| Missing data in nonlinear relationships | |
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| Predictive Mean Matching (PMM) | |
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| Just Another Variable (JAV) | |
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| Joint modelling approach | |
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| Extension to more general models and missing data patterns | |
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| Metropolis-Hastings sampling | |
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| Rejection sampling | |
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| FCS approach | |
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| Discussion | |
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| Interactions | |
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| Interaction variables rally observed | |
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| Interactions of categorical variables | |
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| General nonlinear relationships | |
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| Software | |
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| Discussion | |
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| Advanced Topics | |
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| Survival data, skips and large datasets | |
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| Time-to-event data | |
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| Imputing missing covariate values | |
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| Survival data as categorical | |
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| Imputing censored survival times | |
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| Nonparametric, or 'hot deck' imputation | |
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| Nonparametric imputation for survival data | |
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| Multiple imputation for skips | |
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| Two-stage MI | |
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| Large datasets | |
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| Large datasets and joint modelling | |
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| Shrinkage by constraining parameters | |
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| Comparison of the two approaches | |
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| Multiple imputation and record linkage | |
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| Measurement error | |
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| Multiple imputation for aggregated scores | |
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| Discussion | |
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| Multilevel multiple imputation | |
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| Multilevel imputation model | |
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| MCMC algorithm for imputation model | |
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| Imputing level-2 covariates using FCS | |
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| Individual patient meta-analysis | |
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| When to apply Rubin's rules | |
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| Extensions | |
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| Random level-1 covariance matrices | |
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| Model fit | |
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| Discussion | |
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| Sensitivity analysis: MI unleashed | |
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| Review of MNAR modelling | |
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| Framing sensitivity analysis | |
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| Pattern mixture modelling with MI | |
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| Missing covariates | |
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| Application to survival analysis | |
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| Pattern mixture approach with longitudinal data via MI | |
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| Change in slope post-deviation | |
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| Piecing together post-deviation distributions from other trial arms | |
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| Approximating a selection model by importance weighting | |
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| Algorithm for approximate sensitivity analysis by re-weighting | |
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| Discussion | |
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| Including survey weights | |
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| Using model based predictions | |
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| Bias in the MI variance estimator | |
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| MI with weights | |
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| Estimation in domains | |
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| A multilevel approach | |
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| Further developments | |
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| Discussion | |
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| Robust multiple imputation | |
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| Introduction | |
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| Theoretical background | |
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| Simple estimating equations | |
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| The Probability Of Missingness (POM) model | |
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| Augmented inverse probability weighted estimating equation | |
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| Robust multiple imputation | |
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| Univariate MAR missing data | |
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| Longitudinal MAR missing data | |
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| Simulation studies | |
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| Univariate MAR missing data | |
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| Longitudinal monotone MAR missing data | |
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| Longitudinal nonmonotone MAR missing data | |
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| Nonlongitudinal nonmonotone MAR missing data | |
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| Results and discussion | |
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| The RECORD study | |
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| Discussion | |
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| Markov Chain Monte Carlo | |
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| Probability distributions | |
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| Posterior for the multivariate normal distribution | |
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| Bibliography | |
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| Index of Authors | |
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| Index of Examples | |
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| Index | |