Kalaycioglu, O; (2015) Methods for Handling Missing Data for Observational Studies with Repeated Measurements. Doctoral thesis , UCL (University College London).

Abstract

Missing data is common in longitudinal observational studies where, data on both outcome and explanatory variables are collected repeatedly at several time points. The research in this thesis is motivated by the repeated measurements observational study with incomplete outcome and explanatory variables. When the missing values on the explanatory variables are related to the observed values of the outcome, it has been recommended to use multiple imputation (MI) techniques to alleviate the problems of both bias and the efficiency of the parameter estimates. In this thesis MI techniques were reviewed, extended where necessary and compared regarding the bias and efficiency of the regression coefficient estimates using simulation studies in order to suggest the choice of the most optimal MI method when MAR explanatory variables occur in repeated measurements studies. Multivariate normal imputation (MVNI) produced the least bias in most situations, is theoretically well justified and allows flexible correlation for the repeated measurements in the imputation model. Bayesian MI is efficient and maybe preferable for imputing categorical variables with extreme prevalences. Imputation by chained equations (ICE) approaches were sensitive to the correlation between the repeated measurements of the incomplete variables. A complete missing data analysis requires sensitivity analysis which investigates the departures from MAR mechanism. Models for handling MNAR in both outcome and explanatory variables are not well developed and can potentially be complicated, especially when there are several missingness patterns. In this thesis selection modelling and pattern mixture modelling frameworks are extended to accommodate MNAR mechanism on time-varying outcome and explanatory variables, with mixed type of missingness patterns using fully Bayesian estimation technique. The investigations suggested that, when the true form of missingness mechanism is specified and the variables that cause missingness are used in the missingness models, the parameter estimates will be less biased than using standard MAR methods. The bias can be reduced, if the true values of missingness parameters are incorporated into the missingness models using informative priors.

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