Practical use of multiple imputation

• Main Author: Morris, T. P.
• Published: University College London (University of London) 2014
• Subjects: 519.5
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.602896

Multiple imputation is a flexible technique for handling missing data that is widely used in medical research. Its properties are understood well for some simple settings but less so for the complex settings in which it is typically applied. The three research topics considered in thesis consider incomplete continuous covariates when the analysis model involves nonlinear functions of one or more of these. Chapters 2–4 evaluate two imputation techniques known as predictive mean matching and local residual draws, which may protect against bias when the imputation model is misspecified. Following a review of the literature, I focus on how to match, the appropriate size of donor pool, and whether transformation can improve imputation. Neither method performs as well as hoped when the imputation model is misspecified but both can offer some protection against imputation model misspecification. Chapter 5 investigates strategies for imputing the ratio of two variables. Various ‘active’ and ‘passive’ strategies are critiqued, applied to two datasets and compared in a simulation study. (‘Active’ indicates the ratio is imputed directly within a model; ‘passive’ means it is calculated externally to the imputation model.) Without prior transformation, passive imputation after imputing the numerator and denominator should be avoided, but other methods require less caution. Chapter 6 proposes techniques for combining multiple imputation with (multivariable) fractional polynomial methods. A new technique for imputing dimension-one fractional polynomials is developed and nested in a chained-equations procedure. Two candidate methods for estimating exponents in the fractional polynomial model, using Wald statistics and log-likelihoods, are assessed via simulation. Finally, the type I error and power are compared for model selection procedures based on Wald and likelihood-ratio type tests. Both methods can out-perform complete cases analysis, with the Wald method marginally better than likelihood-ratio tests.